Workshop 6: Targeting Cancer Cell Proliferation and Metabolism Networks

(March 23,2015 - March 27,2015 )

Organizers


Baltazar Aguda
Founder & CEO, Disease Pathways, LLC
Robert Gatenby
H. Lee Moffitt Cancer Center & Research Institute
Vito Quaranta
Department of Cancer Biology, Vanderbilt University
Santiago Schnell
Department of Molecular & Integrative Biology, University of Michigan Medical School

A fundamental property of cancer is uncontrolled cell proliferation. Much knowledge has accumulated on altered genetic and signaling networks that drive uncontrolled proliferation. Recently, there has been a resurgence of interest in the intimate link between proliferation and metabolism, absolutely required to fulfill energy and biomass demands for cell division. In cancer, metabolic networks are highly adaptable, and often metabolism of cancer cells relies largely on aerobic glycolysis, a property referred to as the Warburg effect and akin to fermentation: even in the presence of oxygen, energy metabolism bypasses mitochondrial respiration. The dysregulated interface between metabolic networks and oncogene-modified proliferation networks is emerging as a fertile area to identify critical target nodes, or strategies to defy the drive to ever-adaptable uncontrolled proliferation. This workshop will encompass a mix of experimentalists and mathematicians. Ideally, the former will be engaged on the production of large datasets on cancer cell proliferation, both at the cell population and single-cell level, and in response to microenvironment perturbations including anti-proliferative drugs. The latter will focus on mathematical models of proliferation and metabolism at several scales, including genetic, signaling and cellular, including a focus on the ability of cancer cell populations to regenerate and reprogram in response to hostile microenvironment and to targeted treatment, ultimately persisting in their proliferative state. Multi-scale models connecting the growth of cultured cancer cells and/or individual tumors to epidemiological data will also be considered. Although tumor growth and cancer cell proliferation have been modeled mathematically for decades, adequate datasets have been scarce and fragmentary due to experimental limitations. Recently, several game-changing high-throughput technologies, including genomics, proteomics, and automated microscopy, have created remarkable opportunities for renewed modeling efforts. Furthermore, small-molecule drugs with exquisite specificity for signaling network nodes are in an intensive phase of development and deployment into clinical trials. As these targeted agents increasingly enter standard clinical practice, a major challenge is to improve outcomes by rational drugging strategies. Sheer combinatorics makes drug strategy testing in the field prohibitively expensive, both financially and temporally, opening avenues for mathematical and statistical approaches that, combined with experimentation, have the power to streamline testing.

Accepted Speakers

Brian Altman
Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine
Steven Altschuler
Pharmaceutical Chemistry,
Riccardo Colombo
Department of Informatics, Systems and Communication, University of Milan - Bicocca
Avner Friedman
Department of Mathematics, The Ohio State University
Avner Friedman
Department of Mathematics, The Ohio State University
Avner Friedman
Department of Mathematics, The Ohio State University
Avner Friedman
Department of Mathematics, The Ohio State University
Daniela Gaglio
Institute of Bioimaging and Molecular Physiology,
Ido Goldstein
Lab of Receptor Biology and Gene Expression, National Cancer Institute
Peng Huang
Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center
Sui Huang
., Institute for Systems Biology
Sui Huang
., Institute for Systems Biology
Paul Hwang
Center for Molecular Medicine, NHLBI-NIH
Radhakrishnan Mahadevan
Chemical Engineering & Applied Chemistry, University of Toronto
Christian Mazza
Department of mathematics 23 chemin du Musée CH-1700 Fribourg, Department of Mathematics University of Fribourg
Rafael Moreno-Sanchez
Biochemistry, Instituto Nacional de Cardiologia and Universidad Nacional Autonoma de Mexico
Jacques Pouysségur
Institute for Research on Cancer & Aging , nice (IRCAN),
Jacques Pouysségur
Institute for Research on Cancer & Aging , nice (IRCAN),
Jeffrey Rathmell
Pharmacology and Cancer Biology, Duke University
Osbaldo Resendis-Antonio
Computational Genomics Consortium, Instituto Nacional de Medicina Genomica
Osbaldo Resendis-Antonio
Computational Genomics Consortium, Instituto Nacional de Medicina Genomica
Osbaldo Resendis-Antonio
Computational Genomics Consortium, Instituto Nacional de Medicina Genomica
R. Brooks Robey
Research & Development Service, Veterans Afaairs Medical Center
R. Brooks Robey
Research & Development Service, Veterans Afaairs Medical Center
Ashwini Sharma
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ)
Siv Sivaloganathan
Applied Mathematics, University of Waterloo
Siv Sivaloganathan
Applied Mathematics, University of Waterloo
Sriram Venneti
Pathology, University of Michigan
Jin Wang
Chemistry and Physics, Stony Brook University
Lani Wu
Pharmaceutical Chemistry, University of California San Francisco
Michelle Wynn
Molecular & Integrative Physiology, University of Michigan
Michelle Wynn
Molecular & Integrative Physiology, University of Michigan
Michelle Wynn
Molecular & Integrative Physiology, University of Michigan
Thomas Yankeelov
Radiology, Vanderbilt University
Monday, March 23, 2015
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
08:45 AM

Breakfast

08:45 AM
09:00 AM

Introductions and Information - Marty Golubitsky

09:00 AM
09:45 AM
Jeffrey Rathmell - The Metabolism of Proliferating and Leukemic Lymphocytes

Abstract not submitted.

09:45 AM
10:30 AM
Paul Hwang - p53 Regulation of Mitochondria

p53, one of the most commonly mutated tumor suppressor genes in human cancers, promotes oxidative metabolism by regulating mitochondrial biogenesis through various mechanisms. These include the transactivation of mitochondrial biogenesis genes in the nucleus and the maintenance of mitochondrial genomic DNA by the translocation of p53 protein into the mitochondria. While oxygen is essential for oxidative metabolism, it also serves as the essential substrate for reactive oxygen species (ROS) that can damage the genome. Given the well-established role of p53 in maintaining genomic stability, its promotion of respiration that efficiently converts reactive oxygen to water may serve to contribute to the antioxidant activities of p53. Interestingly, one physiological impact of p53 promoting mitochondrial respiration is increased aerobic exercise capacity that parallels the strong inverse relationship between cardio-respiratory fitness and cancer-free survival observed in large epidemiologic studies. On the other hand, our recent observations from mouse and human studies of Li-Fraumeni syndrome, a premature cancer condition caused by germline mutations of p53, indicate that mutant p53 can also increase oxidative metabolism. We suggest that these disparate findings can be reconciled by the dissociation of the cell cycle and mitochondrial activities of p53. Our observations are also consistent with the growing evidence that cancer cells depend not only on aerobic glycolysis (Warburg effect) but also on mitochondrial metabolism which may contribute to tumorigenesis in the setting of defective cell cycle regulation by mutated p53.

10:30 AM
11:00 AM

Break

11:00 AM
11:45 AM
Riccardo Colombo - Modeling metabolic networks with an ensemble evolutionary flux balance analysis approach

Metabolism can be seen as the biochemical factory producing building blocks and energy for cellular functioning. The study of its control mechanisms and dysregulation is today an area of intense application of modeling efforts. Due to the complexity and dimension of metabolic networks, mechanistic modeling of their dynamics becomes unfeasible. For this reason, the computational investigation of metabolic models makes typically use of constraint-based approaches, which exploits the knowledge about the structure of cell metabolism, while disregarding dynamic intracellular behavior, on the basis of a pseudo-steady state assumption.

Assuming also that cell behavior is optimal with respect to a “metabolic objective”, flux balance analysis (FBA) is widely used to calculate a single optimal flux distribution. This approach has proven to be effective in implementing metabolic engineering design goals, such as the maximization of the cell production of metabolites of industrial interest. However, FBA has recently received increasing attention in Systems Biology, to gain novel knowledge about the physiological state of a cell. In this regard, the assumption of maximization of biomass yield as objective function has revealed successful in predicting some phenotypical characteristics of microorganisms. Nevertheless, when dealing with multicellular organisms, the definition of a plausible objective function is not straightforward and, besides, even if we knew the true objective function, we could not still exclude that the systems is in a sub-optimal space. For this reason, new approaches aimed at describing global network properties are emerging for an unbiased analysis of the solutions space. In this context, we proposed an extension of the classic constraint-based modeling approach in order to overcome the problem of defining an objective function and to analyze the space of fluxes distribution using clustering techniques.

In particular, the developed method does not focus on a given flux distribution assuming that it corresponds to the real one, but is rather designed to explore the solution space sampling it by means of several distinct random objective functions and looking for an ensemble solutions that match a given metabolic phenotype (the system expected behavior). The identified ensemble may indeed be capable of greater prediction accuracy than any of their individual members. By analyzing the generic properties of the ensemble of matching solutions we may identify some patterns responsible for the emergent phenotype. If it is the case that a metabolic phenotype may result form alternative sub-phenotypes. By finding clusters of similar solutions and analyzing their common/distinguishing properties we may therefore gain information about such possible sub-phenotypes. The approach reveals even more effective when two metabolic responses, let say for example a physiological against a pathological one, are compared. In this case, two different ensembles will therefore be obtained: one that matches the former conditions and the other that matches the latter. By comparing the generic properties of the two ensembles the pathways mainly involved in the differential response will be disentangled. It goes without saying that the expected behavior must be abstracted and formalized, and we propose to express the definition in terms of a metabolic response to a condition variation (e.g. redistribution of fluxes as a consequence of a variation in nutrient availability). Depending on the specific problem, the match between a solution and the metabolic response definition can be Boolean (the condition is either met or not) or expressed in term of a fitness (how close the condition is) exploiting an evolutionary algorithm.

We tested our method on a yeast core metabolic model, from which we identified two ensembles of solutions, the first in agreement with a definition for “Crabtree-positive yeasts” and the second in agreement with a definition for “Crabtree-negative yeasts”. In a further step we showed how the ensembles can be further characterized and refined by means of a cluster analysis in order to identify sub-phenotypes. Finally the fluxes that significantly differ between the two ensembles have been identified according to a Kolmogorov-Smirnov test. The aim now, in SysBio – Italy, is to apply this method to the experimentally validated network structures of cancer metabolic rewiring.

11:45 AM
12:30 PM
Mohammad Fallahi-Sichani - Systematic analysis of adaptive resistance and fractional responses of melanoma cells to RAF/MEK inhibition

Treatment of BRAFV600E melanomas with drugs, such as vemurafenib, that inhibit RAF/MEK signaling is effective in the short term, but remission is not durable. Drug resistance appears to involve short-term adaptive responses that compensate for RAF/MEK inhibition via up-regulation of other pro-growth mechanisms. Understanding and ultimately preventing adaptive responses is a key to durable therapy. Systematic data comparing BRAFV600E tumor cells is generally lacking and it is not known whether adaptation is fundamentally similar across cell types or among individual cells within a cell population.
We apply a systematic approach to studying the responses of human melanoma cell lines to five RAF and MEK inhibitors, with the overall goal of (i) characterizing variability in adaptation with time, dose, cell type and across individual cells, (ii) discovering new or poorly characterized adaptive mechanisms, and (iii) demonstrating the effectiveness of a high-throughput approach involving multiplex measurement, single-cell analysis and computational modeling. The data involves time-course measurement of total level and activity of signaling proteins and cell state markers using array-based methods and single-cell immunofluorescence assays as well as measurement of apoptosis and cell viability under the same conditions. Statistical modeling using partial least squares regression (PLSR) revealed which of the changes in the ~200,000 point dataset were phenotypically consequential.
We found that responses to RAF inhibitors are remarkably diverse and involve multiple pathways that can be up or down-regulated over time, with significant variability across cell types and individual cells. We identified a role for JNK/c-Jun signaling in altering the cell-cycle distribution of melanoma cells, causing apoptosis-resistant cells to accumulate and drug maximal effect (Emax) to fall; co-drugging with RAF and JNK inhibitors or JUN knockdown reverse this effect. The primary effect of JNK inhibitors is to minimize the cell-to-cell variability in pS6 suppression, promoting the induction of apoptosis.
Our study shows that a systems-level approach (combining high density time-dependent measurements, quantitative modeling and single-cell analysis) may provide a general framework for evaluating new drugs with adaptive and paradoxical response, and identifying potentially useful combination therapies.

12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:45 PM
Ido Goldstein - Massive re-organization of the liver chromatin landscape following metabolic and inflammatory signals

Diabetes and chronic inflammation are major risk factors for developing various cancer types, including liver cancer. We set out to study the chromatin and transcriptional changes in liver following metabolic and inflammatory signals that are associated with a cancer-promoting phenotype.

One of the hallmarks of diabetes is a hyper-activated, de-regulated response to fasting orchestrated mainly by the liver. In non-pathological states, this response is achieved by eliciting a comprehensive and elaborate transcriptional program leading to fuel production in the form of glucose and ketone bodies. These changes in transcription are mediated by alterations in chromatin structure and transcription factor occupancy. To evaluate the alterations in chromatin landscape and gene expression following fasting, we analyzed livers from fasted mice in three high-throughput experiments. We employed the DNase I Hyper-Sensitivity assay followed by sequencing (DHS-seq) to globally map the accessible regions of chromatin; thus detecting transcriptional regulatory nodes in chromatin (mainly promoters and enhancers). We have found ~4,000 sites in liver chromatin in which accessibility was altered following fasting. These altered regulatory regions were enriched in binding sites for transcription factors known to regulate the fasting response. We obtained similar results by globally mapping active enhancers (by chromatin immunoprecipitation of the active enhancer mark H3K27Ac followed by sequencing – ChIP-seq). Moreover, we determined the alterations in gene expression by profiling the transcriptome of those livers using RNA-seq, with the aim of linking between alterations in chromatin landscape and gene expression. Regions with increased accessibility and increased H3K27 acetylation following fasting (suggesting active transcriptional regulation) were evidenced proximally to fasting induced genes. We present surprising findings showing that a very common metabolic perturbation (i.e. fasting) leads to a massive re-organization of liver chromatin which supports the onset of a complex, temporally organized, mutli-stage transcriptional response.

In a parallel effort, we characterized the chromatin and gene expression patterns of primary hepatocytes in response to three major pro-inflammatory cytokines associated with hepato-carcinogenesis (IL-6, IL-1 beta and TNF alpha). This analysis revealed a complex and diverse pattern of chromatin and gene regulation in which inflammatory cytokines play complementing roles whereby a certain cytokine ‘primes’ an enhancer while the second cytokine induces gene expression.

We believe that understanding the transcriptional response and its underlining chromatin regulation following cancer-promoting stimuli is critical to elucidating the mechanisms initiating carcinogenesis and is expected to aid in combating cancer as well as interconnected metabolic disorders

02:45 PM
03:00 PM

Break

03:00 PM
03:45 PM

To Be Announced

03:45 PM
06:00 PM

Poster Session and Reception

06:00 PM

Shuttle pick-up from MBI

Tuesday, March 24, 2015
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Vito Quaranta - Modeling targeted therapy response in oncogene addiction

With a high-throughput colony Fractional Proliferation (cFP) assay, we simultaneously track in real-time the proliferation dynamics of hundreds to thousands of single-cell derived clones in a cell population exposed to perturbations (Frick et al, 2015, DOI: 10.1002/jcp.24888). In the mutant EGFR-addicted PC9 lung cancer cell line treated with erlotinib, cell fates (death, quiescence, continued proliferation) within each clone vary from cell-to-cell, even between siblings. This widespread heterogeneity of drug response is captured by a new metric, the drug-induced proliferation (DIP) rate, which encapsulates single-cell variation into a dynamic measure of drug response outcomes.

DIP rates variation from colony to colony in PC9 is approximately normally distributed, a strong indication it arises from stochastic sources. Measurement error or mixed colony ancestry could not account for this variation, since DIP rates of PC9 sublines isolated from single cells and propagated in long-term culture (PC9-DS1/95) exhibited the same normal distribution and maintained it for over 25 generations. Similar distributions were obtained from many additional oncogene-addicted cell lines, rigorously re-derived from single cells. Thus, a mutated driver oncogene does not ensure cell-to-cell homogeneity of response, even when genetic background diversity is minimized.

To explore whether these distributions are of consequence to treatment, we constructed a Polyclonal Growth (PG) mathematical model able to incorporate theoretical or experimental DIP rates as parameters. Since DIP rate distributions are normal, they are entirely defined by two parameters, mean and variance. Inputting the average DIP rate of parental PC9 predicts that the cell line as a whole will completely succumb to treatment. In contrast, with the DIP rate distribution parameters as input, a completely different result was obtained: the size of the erlotinib treated population rebounded to initial values after ~11 days, after an initial drop to half the value at 5 days. The PG model predicted similar dynamics of erlotinib response for several mutant EGFR-addicted cell lines: in every cell line tested, rebound occurred within days to weeks, after initial drops to varying depths. Time to rebound is affected primarily by the extent to which the right tail of the DIP rate distribution extends into positive territory. Using stochastic simulations of the PG model, we are able to differentiate the effects of clonal heterogeneity from those of stochastic cell fate decisions (intrinsic noise) that cause significant variability in the response trajectories, including response depth and duration. Predictions were validated experimentally in PC9. It is unlikely that conventional acquired resistance was responsible for the rebound, since SNaPshot multigene assays were negative and response dynamics were inconsistent with a model of rare drug resistant clones. These findings suggest that, even in the absence of acquired genetic resistance, heterogeneity of drug response promotes rebound of the treated population. We propose that these experimental and modeling tools (cFP assay and PG model) enable realistic evaluation of depth and duration of response to targeted drug treatment. Expected and unexpected PG model predictions and suggested avenues for treatment, especially drug combinations, will be discussed.

09:45 AM
10:30 AM
Ed Reznik - How Many MTDNA's Does It Take to Make a Tumor?

In cancer, mitochondrial dysfunction, through mutations, deletions, and changes in copy number of mitochondrial DNA (MTDNA), contributes to the malignant transformation and progression of tumors. Here, we report the first large-scale survey of MTDNA copy number variation across 21 distinct solid tumor types, examining over 8,000 samples of tumor and adjacent normal tissue profiled with next-generation sequencing methods. Our findings uncover a tendency for cancers, especially bladder, breast, and kidney tumors, to be significantly depleted of MTDNA, relative to matched normal tissue. In a subset of tumor types, including kidney chromophobe and adrenocortical carcinomas, MTDNA copy number is significantly associated to patient survival. We show that MTDNA copy number is correlated to the expression of mitochondrially-localized metabolic pathways, suggesting that MTDNA accumlation and depletion reflect gross changes in mitochondrial metabolic activity. Finally, we identify a subset of tumor-type-specific somatic alterations, including IDH1 and NF1 mutations in gliomas, whose incidence is strongly correlated to MTDNA copy number. Our findings point to an intimate connection between MTDNA content and the molecular events underlying the initiation and progression of tumors.

10:30 AM
11:00 AM

Break

11:00 AM
11:45 AM
Ashwini Sharma - Linear proximity of cancer causing and metabolic genes in the genome -does it drive metabolic reprogramming via somatic copy number changes?

Reprogramming of metabolism is an emerging hallmark of cancer. Metabolic genes (MG) have been identified as oncogenes (OG) and tumor suppressor genes (TSG) or targets of oncogenic signaling. Cancer is a direct consequence of genomic aberrations, such as somatic copy number alterations (SCNA) that frequently occur across many cancer types affecting not only OG and TSG, bur also multiple passenger and "potential" co-driver genes at the perturbed loci. l will present our recent work on elucidating how linear proximity of MG and cancer causing genes (CG) in the chromosomes can lead to metabolic remodeling. We have developed the analysis pipeline Identification of Metabolic Cancer Genes (iMetCG) to interrogate such events by integrating data for 19 different cancer types from TCGA that led to the identification of novel metabolic cancer genes.

11:45 AM
12:30 PM
Daniela Gaglio - Institute of Bioimmaging and Molecular Physiology-CNR, Segrate (Milan)

The investigation on metabolic profiling of normal and cancer cells is recently gaining more interest in molecular oncology due to the understanding that a metabolic rewiring underlies the ability of uncontrolled proliferation of cancer cells. It is not only the well known Warburg effect, but also an increased utilization of glutamine by reductive carboxylation that takes place in cancer cells.

We have developed transcriptional and metabolic analysis in various cancer cell lines, to reach a more detailed definition of the metabolic steps involved in cancer metabolic rewiring and on its redox regulation. These data will be discussed in the logic of a drug discovery attempt. The construction of mathematical models of the indicated cancer metabolic rewiring is in progress.

12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:45 PM
Jin Wang - Landscape and Flux of Cell Cycle and Cancer

Understanding the mechanisms of the cell cycle remains challenging. The cell cycle is regulated by the underlying generegulatory networks. We uncovered the underlying Mexican hat landscape of a mammalian cell cycle network. Three local basins of attraction along the cell cycle loop emerge, corresponding to three distinct cell cycle states: the G1, S/G2, and M phases. Two barriers along the loop characterize G1 and S/G2 checkpoints, respectively, of the cell cycle, which provide a physical explanation for cell cycle checkpoint mechanisms. The cell cycle is determined by two driving forces: curl flux and potential barriers. We uncovered the key gene regulations determining the progression of cell cycle, which can be used to guide the design of new anticancer tactics.

Cancer is a disease regulated by the underlying gene networks. The emergence of normal and cancer states as well as the transformation between them can be thought of as a result of the gene network interactions and associated changes. We developed a global potential landscape and path framework to quantify cancer and associated processes. We constructed a cancer gene regulatory network based on the experimental evidences and uncovered the underlying landscape. The resulting tristable landscape characterizes important biological states: normal, cancer and apoptosis. The landscape topography in terms of barrier heights between stable state attractors quantifies the global stability of the cancer network system. We propose two mechanisms of cancerization: one is by the changes of landscape topography through the changes in regulation strengths of the gene networks. The other is by the fluctuations that help the system to go over the critical barrier at fixed landscape topography. The kinetic paths from least action principle quantify the transition processes among normal state, cancer state and apoptosis state. The kinetic rates provide the quantification of transition speeds among normal, cancer and apoptosis attractors. By the global sensitivity analysis of the gene network parameters on the landscape topography, we uncovered some key gene regulations determining the transitions between cancer and normal states. This can be used to guide the design of new anti-cancer tactics, through cocktail strategy of targeting multiple key regulation links simultaneously, for preventing cancer occurrence or transforming the early cancer state back to normal state.

02:45 PM
03:30 PM
Lani Wu - Inferring signaling crosstalk from cellular heterogeneity

Cancer cell populations can be highly heterogeneous in their signaling phenotypes. This heterogeneity is often viewed as an impediment to understanding how information flows within cells. However, we have found recently that cell-to-cell variability can actually be used to infer network topology.

03:30 PM
04:15 PM

Break

04:15 PM
05:45 PM

Poster Chalk Talks

05:45 PM

Shuttle pick-up from MBI

Wednesday, March 25, 2015
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Peng Huang - High Glycolytic Metabolism in Stem-Like Cancer Cells: Regulation by Glucose in the Microenvironment and Therapeutic Implications

Alterations in energy metabolism are associated with malignant transformation, and play a key role in cancer development and adaptation to changes in tumor microenvironment. This presentation will focus on alterations of glucose metabolism in stem-like cancer cells including side population (SP), the effect of glucose on SP cells, and a novel therapeutic strategy to kill CSCs, which are resistant to standard chemotherapeutic agents. Although it has been recognized that tumor tissue niches may significantly affect the stemness of cancer cells, the role of key nutrients such as glucose in the microenvironment to affect stem-like cancer cells largely remains elusive. We show that stem-lime cancer cells isolated from human cancer cells exhibit higher glycolytic activity compared to the non-stem cancer cells. Glucose in the culture environment exerts a profound effect on SP cells as evidenced by its ability to induce a significant increase in the percentage of SP in the overall cancer cell population, while glucose starvation causes a rapid decrease in SP cells. Mechanistically, the up-regulation of SP cells by glucose seems mediated by suppression of AMPK and activation of the Akt pathway, leading to elevated expression of the ATP-dependent efflux pump ABCG2. Importantly, inhibition of glycolysis significantly reduces SP cells in vitro and impairs their ability to form tumors in vivo. Combination of glycolytic inhibition and standard chemotherapeutic drugs was highly effective in eliminating cancer cells and improve anticancer activity. Our data suggests that glucose is an essential regulator of stem-like cancer cells mediated by the Akt pathway, and targeting glycolysis represents an attractive cancer treatment strategy with potential therapeutic benefits.

09:45 AM
10:30 AM
Siv Sivaloganathan - Cancer cell metabolism and it’s impact on the tumour microenvironment

Targeting metabolic pathways in malignant tumours shows increasing promise as an effective therapeutic strategy in clinical oncology. Thus, unravelling details of metabolic pathways used by cancer cells, particularly those pathways that are differentially activated or suppressed in tumours, is of much current interest. In 1997, Helmlinger et al published “in-vivo” experimental results of pH and pO2 levels as functions of distance from a single blood vessel, on the micrometer scale. We show how these results provide unique insights into cancer cell metabolism when combined with an appropriate mathematical model.

10:30 AM
11:00 AM

Break

11:00 AM
11:45 AM
Brian Altman - Oncogenic Myc Disrupts Circadian Rhythm through Upregulation of Rev-erbα

Circadian rhythms are regulated by feedback loops comprising a network of factors that regulate Clock-associated genes. Chronotherapy seeks to take advantage of altered circadian rhythms in some cancers to better time administration of treatments to increase efficacy and reduce toxicity. However, there is currently no basis to identify which cancers have disrupted circadian rhythms and would be amenable to chronotherapy. c- and N-Myc are oncogenic transcription factors translocated or amplified in many cancers. While the role of Myc in circadian rhythm is currently unknown, it may affect circadian rhythm by binding to the same E-box promoter regions used by the central regulators of circadian rhythm, Clock/Bmal1. Here we show in neuroblastoma, osteosarcoma, and hepatocellular carcinoma cells that overexpressed Myc specifically upregulated the circadian regulator Rev-erbα, which in turn decreased expression of Bmal1. Importantly, Myc-expressing cells showed dramatically disrupted circadian oscillations, which could be rescued by inhibiting expression of Rev-erbα and β. Increased Rev-erbα was observed in primary human neuroblastoma and was correlated with poor prognosis. Together, these data suggest that Myc-driven cancers have altered circadian oscillation due to upregulation of Rev-erbα, and that cancers driven by Myc may thus be good candidates for chronotherapy.

11:45 AM
12:30 PM
Rafael Moreno-Sanchez - Kinetic modeling of cancer glycolysis

Glycolysis provides cytosolic ATP and NADH as well as precursors for several anabolic pathways. These are probably the reasons why most cancer cells have an enhanced glycolytic capacity. To have a better understanding of the controlling mechanisms of this essential pathway, and to unveil suitable and alternative therapeutic targets, kinetic models were built up for glycolysis in cancer cells under a variety of experimental conditions. The kinetic models were constructed using : (i) all the kinetic parameters of all enzymes and transporters involved in the pathway, all determined under near-physiological conditions of pH, temperature and medium composition; (ii) the enzyme activities, metabolite concentrations and fluxes determined in living cells; (iii) the description of appropriate rate equations for all pathway steps, including reversibility or equilibrium constants; and (iv) an iterative process of re-experimentation and refinement of the kinetic models. Once the models were validated, by comparing the model predicted pathway behavior regarding intermediary concentrations and fluxes with that determined experimentally in living cells, they were further used to establish the pathway control.

In cancer cells, kinetic modeling indicated that glucose transporter (GLUT), hexokinase (HK), glycogen degradation (GlycDeg) and/or hexosephosphate isomerase (HPI) were the main flux- and ATP concentration-controlling steps. Although the specific contribution of each of these steps varied, depending on the O2 level (normoxia, severe hypoxia), initial external glucose concentration (25 mM, 2.5 mM), or cancer cell type (AS-30D, HeLa), the same steps kept the pathway control, i.e. the mechanisms that govern the control of cancer glycolysis are preserved and are highly robust. The glycolytic flux increased under low glucose (+ normoxia), which was accompanied by no significant variation in total GLUT and HK activities; instead an increased affinity for glucose emerged as a consequence of a shift in activity from low to high affinity isoforms (GLUT-3 over GLUT-1; and HK-I over HK-II). Modeling appointed GLUT as the principal controlling step in HeLa cells exposed to low glucose; indeed, glycolysis in these cells were more sensitive to GLUT inhibition than cells exposed to high glucose. The glycolytic flux also increased under hypoxia (+ high glucose), but in this case most glycolytic proteins were over-expressed, including the low affinity GLUT and HK isoforms, and with no variation in the high affinity isoforms. Thus, pathway flux can be modulated by changing the isoform pattern (low glucose) and over-expressing (hypoxia) the most controlling steps.

Kinetic modeling identified GLUT, HK, GlycDeg and/or HPI as the foremost therapeutic targets; their simultaneous and partial, not complete, inhibition will have greater deleterious effects on cancer glycolysis, and possibly on cell growth and viability.

This work was partly supported by CONACyT-Mexico grants Nos. 180322, 107183, and 178638.

12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:45 PM
Radhakrishnan Mahadevan - Stoichiometric and Ensemble modeling of “Respiro-fermentation�

The simultaneous utilization of efficient respiration and inefficient fermentation even in the presence of abundant oxygen is a puzzling phenomenon commonly observed in bacteria, yeasts, and cancer cells. In the first part of the talk we will present work on the modeling of respiro-fermentation on bacteria. One of the possible explanations is the presence of additional constrains on the metabolic fluxes and we have shown that in the case of overflow metabolism in E. coli, one can simulate respiro-fermentation. In the second part of the talk we will focus on the use of metabolic modeling to analyze such inefficient metabolism in cancer cells.

The metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this talk, we will present how Ensemble Modeling (EM) framework can be used to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire ensemble of models to obtain a sub-set, which is increasingly predictive. The resulting models predicted additional targets that can cause significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of multiple reactions will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets. Finally, in the last part of the talk we will present work done in collaboration with Prof. McGuigan’s group on the development of 3D bioreactor system that can provide improved data for modeling and analysis of metabolism using stoichiometric and kinetic modeling.

02:45 PM
03:30 PM
Osbaldo Resendis-Antonio - Systems Biology and the challenges for elucidating the role of biological networks in cancer

Systems Biology is an emergent science whose main objective is to understand and predict the phenotype of a microorganism through the parallel analysis of high throughput data and computational modeling. This systemic, integrative and quantitative description is a new paradigm in genome sciences that contribute to understand the metabolic profile supporting the phenotype in a variety of organism, ranging from the bacteria to the study of metabolic alterations in human diseases. Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotype states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches in combination with genome scale metabolic reconstructions have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells, such as the Warburg effect. In this talk I will present some formalisms that can serve as a platform to: 1) integrate and interpret high-throughput data; 2) generate biological hypothesis about their metabolic activity; and 3) design experiments to assess the genotype-phenotype relationship. Given the overwhelming complexity in cancer, multidisciplinary approaches are required to construct the bases of a systemic and personalized medicine, which remains as a fundamental task in the medicine of this century.

03:30 PM
04:15 PM

Break

04:15 PM
05:45 PM

Informal Discussion

05:45 PM

Shuttle pick-up from MBI

Thursday, March 26, 2015
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Steven Altschuler - Relating cancer cell heterogeneity to drug resistance

Cancer cell populations can be highly heterogeneous. We will discuss progress in understanding the origins of this heterogeneity and its implications in predicting drug response.

09:45 AM
10:30 AM
R. Brooks Robey - Warburg and Tumor Metabolism Revisited - Hexokinases, Glycolysis, and the Metabolic Gestalt of the Cell

Nearly a century has elapsed since Warburg and colleagues first applied contemporary manometric techniques to the biochemical characterization of cancer metabolism. Their studies identified several cardinal features of tumor metabolism, most notably increased glucose-derived lactate generation in the presence, as well as the absence, of O2 - or so-called aerobic glycolysis. Recent advances in our understanding of the relationship between metabolism and cell survival and a resurgent interest in targeting cancer metabolism for therapeutic benefit have refocused attention on the characteristic features of cancer that Warburg described, as well as their mechanistic underpinnings. Hexokinases catalyze the first committed step of glucose metabolism, are overexpressed in cancer, and have emerged as important mediators of the anti-apoptotic effects of growth factors and Akt. They also directly contribute to the signature glycolytic phenotype of tumors. The ability of hexokinases to prevent apoptosis is mediated, in part, by direct physical and functional interaction with mitochondria and competition with pro-apoptotic Bcl-2 proteins for binding to common mitochondrial target sites. Bound hexokinases also facilitate the exchange of adenine nucleotides and other anionic metabolites into and out of mitochondria, thereby promoting mitochondrial integrity and directly coupling the metabolism of glucose in the cytosol to terminal substrate oxidation and oxidative phosphorylation within mitochondria. This and closely related forms of metabolic crosstalk play important roles in the coordination and control of intra- and extramitochondrial amphibolic metabolism and contribute to the characteristic proliferative and metabolic phenotypes of cancer cells. Considered in the context of the metabolic gestalt of the cell, these coupling mechanisms may also constitute attractive potential targets for therapeutic cancer intervention.

10:30 AM
11:00 AM

Break

11:00 AM
11:45 AM
Avner Friedman - The influence of mathematics on medicine and public health

In this talk I will give two examples: atherosclerosis/cholesterol modeling, and kidney fibrosis, based on two papers we published this year with Wenrui Hao, MBI postdoc, in PLoS One, and in PNAS. The first paper develops cholesterol guidelines (we call it "risk map") more refined than those suggested by the American Heart Association. The second paper opens the possibility of monitoring the disease of kidney fibrosis without the need to do repeated biopsies. Both models are described by systems of PDEs. The models also offer possible treatments, but human data will be needed in order to verify the conclusions of the models.

11:45 AM
12:30 PM
Christian Mazza - Phenotypic diversity and population growth in fluctuating environments

Organisms in fluctuating environments must constantly adapt their behavior to survive. We consider strategies where cells switch their phenotypes randomly or use costly sensing mechanisms to respond optimally to environmental changes . The strategies are compared using net growth rates and Lyapunov exponents for models involving random differential equations and branching processes in random enviroments.

12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:45 PM
Thomas Yankeelov - Quantitative Imaging to Drive Biophysical Models of Tumor Growth

The ability to identify—early in the course of therapy—patients that are not responding to a given therapeutic regimen is highly significant. In addition to limiting patients’ exposure to the toxicities associated with unsuccessful therapies, it would allow patients the opportunity to switch to a potentially more efficacious treatment. In this presentation, we will discuss ongoing efforts at using data available from advanced imaging technologies to initialize and constrain predictive biophysical and biomathematical models of tumor growth and treatment response.

02:45 PM
03:30 PM
Sriram Venneti - Evaluating glutamine addiction in gliomas using PET imaging

Cancer cells commonly undergo metabolic reprograming enabling increased nutrient use to fuel their growth and proliferation. The Warburg effect is the classic example wherein tumors exhibit enhanced glucose uptake and metabolism through aerobic glycolysis. This increase in glucose uptake can be evaluated in vivo using positron emission tomography (PET) imaging with the glucose analogue 18F-fluorodeoxyglucose (18F-FDG). 18F-FDG PET imaging is a valuable clinical tool and is routinely used in diagnosing, grading and staging cancers. However, 18F-FDG is of limited value in evaluating gliomas in vivo due to high background glucose metabolism in the normal brain resulting in suboptimal tumor delineation. Glutamine is the most abundant amino acid in the plasma and many cancers are addicted to glutamine for their survival. We have recently developed 4-18F-(2S,4R)-fluoroglutamine (18F-FGln) for PET imaging in vivo. We evaluated glutamine uptake using PET imaging with 18F-FGln in vivo in glioma animal models to demonstrate that 18F-FGln showed high uptake in gliomas but minimal uptake in the normal brain, enabling clear tumor visualization. We translated these findings to human glioma subjects where 18F-FGln showed high tumor/background ratios in human glioma patients with progressive disease in contrast to that observed with 18F-FDG. These data suggest that 18F-FGln is specifically taken up by gliomas, can be used to assess metabolic nutrient uptake in gliomas in vivo and may serve as a valuable tool in the clinical management of gliomas.

03:30 PM
04:15 PM
Baltz Aguda - Metabolic Pathways and the Restriction Point in the Cell Cycle

The Restriction Point (RP) is a checkpoint in G1 phase that marks the transition from growth factor-dependent to growth factor-independent cell cycle progression. Its core switching mechanism involves cyclin E/CDK2, the retinoblastoma protein, and transcription factors E2F and MYC. Models of RP dynamics that we and others have proposed earlier do not explicitly consider the bioenergetic and biosynthetic processes that drive the cell cycle. In this talk, I will discuss the links among RP, glycolysis and glutaminolysis, and then explore the potential control points in the expanded network.

04:15 PM

Shuttle pick-up from MBI

05:30 PM
06:00 PM

Cash Bar - Crowne Plaza

06:00 PM
07:30 PM

Banquet - Crowne Plaza

Friday, March 27, 2015
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Santiago Schnell - Reverse engineering signaling pathway in cancer cells: Effects of nonokiol on the notch signaling pathway as a case study

The ability to accurately infer an intracellular network from data remains a significant and difficult problem in molecular systems biology. We developed a novel network inference methodology that integrates measurements of protein activation from perturbation experiments. The approach was validated in silico with a set of test networks and applied to investigate the effects of honokiol on the notch signaling pathway in SW480 colon cancer cells. Our methodology relies on logic-based networks to provide a predictive approximation of the transfer of signals in a network. The method can also be leveraged to identify additional perturbation experiments needed to distinguish between a set of possible candidate networks. The development of methodologies that permit the accurate prediction of connectivity in dysregulated pathways may enable more rational determination of what therapy is best for a patient.

09:45 AM
10:30 AM
Rachel Leander - Modeling intermitotic time distributions

Cell division is one of the most fundamental processes of life, yet it is subject to significant random variation. Experiments have shown that, even in a population of homogeneous cells, the distribution of intermitotic times (IMTs) is highly variable. Furthermore, IMT distributions exhibit interesting temporal dynamics, especially in response to perturbations such as drug treatment. Using a top-down approach, we have developed a stochastic model of the cell cycle that is based on the cell cycle check point. This model enables us to frame the problem of determining a cell's IMT as a first exit time problem, through which we derive an expression for the distribution of IMTs. This distribution can be analyzed in order to relate distribution properties and dynamics to model parameters.

10:30 AM
11:00 AM

Break

11:00 AM
12:15 PM

Panel Discussion

12:15 PM

Shuttle pick-up from MBI (One to airport and one back to hotel)

Name Email Affiliation
Agarwal, Gunjan agarwal.60@osu.edu Biomedical Engineering,
Aguda, Baltazar bdaguda@gmail.com Founder & CEO, Disease Pathways, LLC
Altman, Brian altman@mail.med.upenn.edu Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine
Altschuler, Steven Steven.Altschuler@ucsf.edu Pharmaceutical Chemistry,
Anand, Rene anand.20@osu.edu pharmacology, The Ohio State University
Chiang, Michelle khyx2cme@nottingham.edu.my Pharmacy, University of Nottingham
Colombo, Riccardo riccardo.colombo@disco.unimib.it Department of Informatics, Systems and Communication, University of Milan - Bicocca
Dastrange, Nasser dastrange@bvu.edu Mathematics, Buena Vista University
Delgado, Yamixa yami1084@hotmail.com biology, University of Puerto Rico Río Piedras Campus
Dorrance, Adrienne adrienne.dorrance@osumc.edu Hematology, The Ohio State University
Elgamal, Dalia dalia.elgamal@osumc.edu Internal Medicine, The Ohio State University Comprehensive Cancer Center OSU CCC
Fallahi-Sichani, Mohammad Mohammad_FallahiSichani@hms.harvard.edu Systems Biology, Harvard Medical School
Friedman, Avner afriedman@math.ohio-state.edu Department of Mathematics, The Ohio State University
Gaglio, Daniela Institute of Bioimaging and Molecular Physiology,
Goldstein, Ido goldstein.ido@gmail.com Lab of Receptor Biology and Gene Expression, National Cancer Institute
Han, Yang y.han@exeter.ac.uk Medical School, University of Exeter
Hanson, Shalla shalladh@gmail.com Mathematics, Duke University
Huang, Peng phuang@mdanderson.org Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center
Huang, Sui sui.huang@systemsbiology.org ., Institute for Systems Biology
Hwang, Paul hwangp@mail.nih.gov Center for Molecular Medicine, NHLBI-NIH
Koslicki, David david.koslicki@math.oregonstate.edu Department of Mathematics, Oregon State University
Leander, Rachel Rachel.Leander@mtsu.edu Mathematics, Middle Tennessee State University
Mahadevan, Radhakrishnan Chemical Engineering & Applied Chemistry, University of Toronto
Mazza, Christian christian.mazza@unifr.ch Department of mathematics 23 chemin du Musée CH-1700 Fribourg, Department of Mathematics University of Fribourg
Merajver, Sofia smerajve@umich.edu Internal Medicine, Cancer and Molecular Biology Program, University of Michigan
Moreno-Sanchez, Rafael rafael.moreno@cardiologia.org.mx Biochemistry, Instituto Nacional de Cardiologia and Universidad Nacional Autonoma de Mexico
Mukhopadhyay, Partha partha65@gmail.com Oncology, Victoria University Healthcare Center
Niikura, Yohei yohei.niikura@nationwidechildrens.org Center for Childhood Cancer and Blood Diseases, Nationwide Children's Hospital
Perez-Castro, Antonio antonio.perez-castro@osumc.edu molecular virology, immunology and genetics, OSU
Pouyssegur, Jacques pouysseg@unice.fr Institute for Research on Cancer & Aging , nice (IRCAN),
Quaranta, Vito vito.quaranta@vanderbilt.edu Department of Cancer Biology, Vanderbilt University
Rambani, Komal rambani.1@osu.edu Biomedical Sciences, The Ohio State University
Rathmell, Jeffrey jeff.rathmell@duke.edu Pharmacology and Cancer Biology, Duke University
Resendis-Antonio, Osbaldo resendis@ccg.unam.mx Computational Genomics Consortium, Instituto Nacional de Medicina Genomica
Rezaei Yousefi, Mohammadmahdi rezaeiyousefi.1@osu.edu Electrical and Computer Engineering, The Ohio State University
Reznik, Ed reznik.ed@gmail.com Computational Biology, Memorial Sloan Kettering Cancer Center
Robey, R. Brooks R.Brooks.Robey@Dartmouth.edu Research & Development Service, Veterans Afaairs Medical Center
Schnell, Santiago schnells@umich.edu Department of Molecular & Integrative Biology, University of Michigan Medical School
Sharma, Ashwini a.sharma@dkfz.de Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ)
Sivaloganathan, Siv Applied Mathematics, University of Waterloo
Sontag, Eduardo eduardo.sontag@gmail.com Department of Mathematics and BioMaPS Institute for Quantitative Biology, Rutgers University at New Brunswick
Taub, Mary biochtau@buffalo.edu Biochemistry, University at Buffalo
Venneti, Sriram svenneti@med.umich.edu Pathology, University of Michigan
Wang, Jin jin.d.wang@gmail.com Chemistry and Physics, Stony Brook University
Whidden, Mark mwhidden@umich.edu Molecular & Integrative Physiology, University of Michigan Medical School
Wu, Lani Lani.Wu@ucsf.edu Pharmaceutical Chemistry, University of California San Francisco
Wynn, Michelle mlwynn@umich.edu Molecular & Integrative Physiology, University of Michigan
Yankeelov, Thomas thomas.yankeelov@vanderbilt.edu Radiology, Vanderbilt University
Array ( [0] => Array ( [id] => 3969 [first_name] => Baltazar [last_name] => Aguda [affiliation_line] => Founder & CEO, Disease Pathways, LLC [title] => Metabolic Pathways and the Restriction Point in the Cell Cycle [abstract] =>

The Restriction Point (RP) is a checkpoint in G1 phase that marks the transition from growth factor-dependent to growth factor-independent cell cycle progression. Its core switching mechanism involves cyclin E/CDK2, the retinoblastoma protein, and transcription factors E2F and MYC. Models of RP dynamics that we and others have proposed earlier do not explicitly consider the bioenergetic and biosynthetic processes that drive the cell cycle. In this talk, I will discuss the links among RP, glycolysis and glutaminolysis, and then explore the potential control points in the expanded network.

[SCHEDULE_id] => 5451 [registration_type] => Organizer [registration_subtype] => [start_datetime] => 2015-03-26 15:30:00 [end_datetime] => 2015-03-26 16:15:00 ) [1] => Array ( [id] => 3983 [first_name] => Brian [last_name] => Altman [affiliation_line] => Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine [title] => Oncogenic Myc Disrupts Circadian Rhythm through Upregulation of Rev-erbα [abstract] =>

Circadian rhythms are regulated by feedback loops comprising a network of factors that regulate Clock-associated genes. Chronotherapy seeks to take advantage of altered circadian rhythms in some cancers to better time administration of treatments to increase efficacy and reduce toxicity. However, there is currently no basis to identify which cancers have disrupted circadian rhythms and would be amenable to chronotherapy. c- and N-Myc are oncogenic transcription factors translocated or amplified in many cancers. While the role of Myc in circadian rhythm is currently unknown, it may affect circadian rhythm by binding to the same E-box promoter regions used by the central regulators of circadian rhythm, Clock/Bmal1. Here we show in neuroblastoma, osteosarcoma, and hepatocellular carcinoma cells that overexpressed Myc specifically upregulated the circadian regulator Rev-erbα, which in turn decreased expression of Bmal1. Importantly, Myc-expressing cells showed dramatically disrupted circadian oscillations, which could be rescued by inhibiting expression of Rev-erbα and β. Increased Rev-erbα was observed in primary human neuroblastoma and was correlated with poor prognosis. Together, these data suggest that Myc-driven cancers have altered circadian oscillation due to upregulation of Rev-erbα, and that cancers driven by Myc may thus be good candidates for chronotherapy.

[SCHEDULE_id] => 5433 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-25 11:00:00 [end_datetime] => 2015-03-25 11:45:00 ) [2] => Array ( [id] => 3987 [first_name] => Steven [last_name] => Altschuler [affiliation_line] => Pharmaceutical Chemistry, [title] => Relating cancer cell heterogeneity to drug resistance [abstract] =>

Cancer cell populations can be highly heterogeneous. We will discuss progress in understanding the origins of this heterogeneity and its implications in predicting drug response.

[SCHEDULE_id] => 5443 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-26 09:00:00 [end_datetime] => 2015-03-26 09:45:00 ) [3] => Array ( [id] => 3971 [first_name] => Riccardo [last_name] => Colombo [affiliation_line] => Department of Informatics, Systems and Communication, University of Milan - Bicocca [title] => Modeling metabolic networks with an ensemble evolutionary flux balance analysis approach [abstract] =>

Metabolism can be seen as the biochemical factory producing building blocks and energy for cellular functioning. The study of its control mechanisms and dysregulation is today an area of intense application of modeling efforts. Due to the complexity and dimension of metabolic networks, mechanistic modeling of their dynamics becomes unfeasible. For this reason, the computational investigation of metabolic models makes typically use of constraint-based approaches, which exploits the knowledge about the structure of cell metabolism, while disregarding dynamic intracellular behavior, on the basis of a pseudo-steady state assumption.

Assuming also that cell behavior is optimal with respect to a “metabolic objective”, flux balance analysis (FBA) is widely used to calculate a single optimal flux distribution. This approach has proven to be effective in implementing metabolic engineering design goals, such as the maximization of the cell production of metabolites of industrial interest. However, FBA has recently received increasing attention in Systems Biology, to gain novel knowledge about the physiological state of a cell. In this regard, the assumption of maximization of biomass yield as objective function has revealed successful in predicting some phenotypical characteristics of microorganisms. Nevertheless, when dealing with multicellular organisms, the definition of a plausible objective function is not straightforward and, besides, even if we knew the true objective function, we could not still exclude that the systems is in a sub-optimal space. For this reason, new approaches aimed at describing global network properties are emerging for an unbiased analysis of the solutions space. In this context, we proposed an extension of the classic constraint-based modeling approach in order to overcome the problem of defining an objective function and to analyze the space of fluxes distribution using clustering techniques.

In particular, the developed method does not focus on a given flux distribution assuming that it corresponds to the real one, but is rather designed to explore the solution space sampling it by means of several distinct random objective functions and looking for an ensemble solutions that match a given metabolic phenotype (the system expected behavior). The identified ensemble may indeed be capable of greater prediction accuracy than any of their individual members. By analyzing the generic properties of the ensemble of matching solutions we may identify some patterns responsible for the emergent phenotype. If it is the case that a metabolic phenotype may result form alternative sub-phenotypes. By finding clusters of similar solutions and analyzing their common/distinguishing properties we may therefore gain information about such possible sub-phenotypes. The approach reveals even more effective when two metabolic responses, let say for example a physiological against a pathological one, are compared. In this case, two different ensembles will therefore be obtained: one that matches the former conditions and the other that matches the latter. By comparing the generic properties of the two ensembles the pathways mainly involved in the differential response will be disentangled. It goes without saying that the expected behavior must be abstracted and formalized, and we propose to express the definition in terms of a metabolic response to a condition variation (e.g. redistribution of fluxes as a consequence of a variation in nutrient availability). Depending on the specific problem, the match between a solution and the metabolic response definition can be Boolean (the condition is either met or not) or expressed in term of a fitness (how close the condition is) exploiting an evolutionary algorithm.

We tested our method on a yeast core metabolic model, from which we identified two ensembles of solutions, the first in agreement with a definition for “Crabtree-positive yeasts” and the second in agreement with a definition for “Crabtree-negative yeasts”. In a further step we showed how the ensembles can be further characterized and refined by means of a cluster analysis in order to identify sub-phenotypes. Finally the fluxes that significantly differ between the two ensembles have been identified according to a Kolmogorov-Smirnov test. The aim now, in SysBio – Italy, is to apply this method to the experimentally validated network structures of cancer metabolic rewiring.

[SCHEDULE_id] => 5406 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-23 11:00:00 [end_datetime] => 2015-03-23 11:45:00 ) [4] => Array ( [id] => 4120 [first_name] => Mohammad [last_name] => Fallahi-Sichani [affiliation_line] => Systems Biology, Harvard Medical School [title] => Systematic analysis of adaptive resistance and fractional responses of melanoma cells to RAF/MEK inhibition [abstract] =>

Treatment of BRAFV600E melanomas with drugs, such as vemurafenib, that inhibit RAF/MEK signaling is effective in the short term, but remission is not durable. Drug resistance appears to involve short-term adaptive responses that compensate for RAF/MEK inhibition via up-regulation of other pro-growth mechanisms. Understanding and ultimately preventing adaptive responses is a key to durable therapy. Systematic data comparing BRAFV600E tumor cells is generally lacking and it is not known whether adaptation is fundamentally similar across cell types or among individual cells within a cell population.
We apply a systematic approach to studying the responses of human melanoma cell lines to five RAF and MEK inhibitors, with the overall goal of (i) characterizing variability in adaptation with time, dose, cell type and across individual cells, (ii) discovering new or poorly characterized adaptive mechanisms, and (iii) demonstrating the effectiveness of a high-throughput approach involving multiplex measurement, single-cell analysis and computational modeling. The data involves time-course measurement of total level and activity of signaling proteins and cell state markers using array-based methods and single-cell immunofluorescence assays as well as measurement of apoptosis and cell viability under the same conditions. Statistical modeling using partial least squares regression (PLSR) revealed which of the changes in the ~200,000 point dataset were phenotypically consequential.
We found that responses to RAF inhibitors are remarkably diverse and involve multiple pathways that can be up or down-regulated over time, with significant variability across cell types and individual cells. We identified a role for JNK/c-Jun signaling in altering the cell-cycle distribution of melanoma cells, causing apoptosis-resistant cells to accumulate and drug maximal effect (Emax) to fall; co-drugging with RAF and JNK inhibitors or JUN knockdown reverse this effect. The primary effect of JNK inhibitors is to minimize the cell-to-cell variability in pS6 suppression, promoting the induction of apoptosis.
Our study shows that a systems-level approach (combining high density time-dependent measurements, quantitative modeling and single-cell analysis) may provide a general framework for evaluating new drugs with adaptive and paradoxical response, and identifying potentially useful combination therapies.

[SCHEDULE_id] => 5407 [registration_type] => Participant [registration_subtype] => [start_datetime] => 2015-03-23 11:45:00 [end_datetime] => 2015-03-23 12:30:00 ) [5] => Array ( [id] => 4135 [first_name] => Avner [last_name] => Friedman [affiliation_line] => Department of Mathematics, The Ohio State University [title] => The influence of mathematics on medicine and public health [abstract] =>

In this talk I will give two examples: atherosclerosis/cholesterol modeling, and kidney fibrosis, based on two papers we published this year with Wenrui Hao, MBI postdoc, in PLoS One, and in PNAS. The first paper develops cholesterol guidelines (we call it "risk map") more refined than those suggested by the American Heart Association. The second paper opens the possibility of monitoring the disease of kidney fibrosis without the need to do repeated biopsies. Both models are described by systems of PDEs. The models also offer possible treatments, but human data will be needed in order to verify the conclusions of the models.

[SCHEDULE_id] => 5446 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-26 11:00:00 [end_datetime] => 2015-03-26 11:45:00 ) [6] => Array ( [id] => 4046 [first_name] => Daniela [last_name] => Gaglio [affiliation_line] => Institute of Bioimaging and Molecular Physiology, [title] => Institute of Bioimmaging and Molecular Physiology-CNR, Segrate (Milan) [abstract] =>

The investigation on metabolic profiling of normal and cancer cells is recently gaining more interest in molecular oncology due to the understanding that a metabolic rewiring underlies the ability of uncontrolled proliferation of cancer cells. It is not only the well known Warburg effect, but also an increased utilization of glutamine by reductive carboxylation that takes place in cancer cells.

We have developed transcriptional and metabolic analysis in various cancer cell lines, to reach a more detailed definition of the metabolic steps involved in cancer metabolic rewiring and on its redox regulation. These data will be discussed in the logic of a drug discovery attempt. The construction of mathematical models of the indicated cancer metabolic rewiring is in progress.

[SCHEDULE_id] => 5421 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-24 11:45:00 [end_datetime] => 2015-03-24 12:30:00 ) [7] => Array ( [id] => 3973 [first_name] => Ido [last_name] => Goldstein [affiliation_line] => Lab of Receptor Biology and Gene Expression, National Cancer Institute [title] => Massive re-organization of the liver chromatin landscape following metabolic and inflammatory signals [abstract] =>

Diabetes and chronic inflammation are major risk factors for developing various cancer types, including liver cancer. We set out to study the chromatin and transcriptional changes in liver following metabolic and inflammatory signals that are associated with a cancer-promoting phenotype.

One of the hallmarks of diabetes is a hyper-activated, de-regulated response to fasting orchestrated mainly by the liver. In non-pathological states, this response is achieved by eliciting a comprehensive and elaborate transcriptional program leading to fuel production in the form of glucose and ketone bodies. These changes in transcription are mediated by alterations in chromatin structure and transcription factor occupancy. To evaluate the alterations in chromatin landscape and gene expression following fasting, we analyzed livers from fasted mice in three high-throughput experiments. We employed the DNase I Hyper-Sensitivity assay followed by sequencing (DHS-seq) to globally map the accessible regions of chromatin; thus detecting transcriptional regulatory nodes in chromatin (mainly promoters and enhancers). We have found ~4,000 sites in liver chromatin in which accessibility was altered following fasting. These altered regulatory regions were enriched in binding sites for transcription factors known to regulate the fasting response. We obtained similar results by globally mapping active enhancers (by chromatin immunoprecipitation of the active enhancer mark H3K27Ac followed by sequencing – ChIP-seq). Moreover, we determined the alterations in gene expression by profiling the transcriptome of those livers using RNA-seq, with the aim of linking between alterations in chromatin landscape and gene expression. Regions with increased accessibility and increased H3K27 acetylation following fasting (suggesting active transcriptional regulation) were evidenced proximally to fasting induced genes. We present surprising findings showing that a very common metabolic perturbation (i.e. fasting) leads to a massive re-organization of liver chromatin which supports the onset of a complex, temporally organized, mutli-stage transcriptional response.

In a parallel effort, we characterized the chromatin and gene expression patterns of primary hepatocytes in response to three major pro-inflammatory cytokines associated with hepato-carcinogenesis (IL-6, IL-1 beta and TNF alpha). This analysis revealed a complex and diverse pattern of chromatin and gene regulation in which inflammatory cytokines play complementing roles whereby a certain cytokine ‘primes’ an enhancer while the second cytokine induces gene expression.

We believe that understanding the transcriptional response and its underlining chromatin regulation following cancer-promoting stimuli is critical to elucidating the mechanisms initiating carcinogenesis and is expected to aid in combating cancer as well as interconnected metabolic disorders

[SCHEDULE_id] => 5409 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-23 14:00:00 [end_datetime] => 2015-03-23 14:45:00 ) [8] => Array ( [id] => 3974 [first_name] => Sui [last_name] => Huang [affiliation_line] => ., Institute for Systems Biology [title] => Targeting the Cell-Cell Interaction Network in Cancer Therapy: A Fat Chance [abstract] =>

Tumors consist of a heterogeneous community of distinct cell types, including distinct tumor parenchyma cells as well as non-neoplastic stromal cells that jointly form a robust system of cells regulating one another’s proliferation –akin to a stable ecosystem. This communication network may account for higher-level, non-cell autonomous control of tumor resilience to therapy. Targeting this network, as opposed to the intracellular networks may offer a new opportunity to suppress the cell community that isthe tumor. Lipid autacoids, derived from polyunsaturated fatty acids are potent mediators of the cell-cell interaction network between tumor cells and the stroma and can promote and suppress tumor growth, thus may represent a new class of targets. (Fatty acids are often neglected components of metabolism sensu latiore…)

[SCHEDULE_id] => [registration_type] => Speaker [registration_subtype] => [start_datetime] => [end_datetime] => ) [9] => Array ( [id] => 3981 [first_name] => Peng [last_name] => Huang [affiliation_line] => Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center [title] => High Glycolytic Metabolism in Stem-Like Cancer Cells: Regulation by Glucose in the Microenvironment and Therapeutic Implications [abstract] =>

Alterations in energy metabolism are associated with malignant transformation, and play a key role in cancer development and adaptation to changes in tumor microenvironment. This presentation will focus on alterations of glucose metabolism in stem-like cancer cells including side population (SP), the effect of glucose on SP cells, and a novel therapeutic strategy to kill CSCs, which are resistant to standard chemotherapeutic agents. Although it has been recognized that tumor tissue niches may significantly affect the stemness of cancer cells, the role of key nutrients such as glucose in the microenvironment to affect stem-like cancer cells largely remains elusive. We show that stem-lime cancer cells isolated from human cancer cells exhibit higher glycolytic activity compared to the non-stem cancer cells. Glucose in the culture environment exerts a profound effect on SP cells as evidenced by its ability to induce a significant increase in the percentage of SP in the overall cancer cell population, while glucose starvation causes a rapid decrease in SP cells. Mechanistically, the up-regulation of SP cells by glucose seems mediated by suppression of AMPK and activation of the Akt pathway, leading to elevated expression of the ATP-dependent efflux pump ABCG2. Importantly, inhibition of glycolysis significantly reduces SP cells in vitro and impairs their ability to form tumors in vivo. Combination of glycolytic inhibition and standard chemotherapeutic drugs was highly effective in eliminating cancer cells and improve anticancer activity. Our data suggests that glucose is an essential regulator of stem-like cancer cells mediated by the Akt pathway, and targeting glycolysis represents an attractive cancer treatment strategy with potential therapeutic benefits.

[SCHEDULE_id] => 5430 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-25 09:00:00 [end_datetime] => 2015-03-25 09:45:00 ) [10] => Array ( [id] => 3970 [first_name] => Paul [last_name] => Hwang [affiliation_line] => Center for Molecular Medicine, NHLBI-NIH [title] => p53 Regulation of Mitochondria [abstract] =>

p53, one of the most commonly mutated tumor suppressor genes in human cancers, promotes oxidative metabolism by regulating mitochondrial biogenesis through various mechanisms. These include the transactivation of mitochondrial biogenesis genes in the nucleus and the maintenance of mitochondrial genomic DNA by the translocation of p53 protein into the mitochondria. While oxygen is essential for oxidative metabolism, it also serves as the essential substrate for reactive oxygen species (ROS) that can damage the genome. Given the well-established role of p53 in maintaining genomic stability, its promotion of respiration that efficiently converts reactive oxygen to water may serve to contribute to the antioxidant activities of p53. Interestingly, one physiological impact of p53 promoting mitochondrial respiration is increased aerobic exercise capacity that parallels the strong inverse relationship between cardio-respiratory fitness and cancer-free survival observed in large epidemiologic studies. On the other hand, our recent observations from mouse and human studies of Li-Fraumeni syndrome, a premature cancer condition caused by germline mutations of p53, indicate that mutant p53 can also increase oxidative metabolism. We suggest that these disparate findings can be reconciled by the dissociation of the cell cycle and mitochondrial activities of p53. Our observations are also consistent with the growing evidence that cancer cells depend not only on aerobic glycolysis (Warburg effect) but also on mitochondrial metabolism which may contribute to tumorigenesis in the setting of defective cell cycle regulation by mutated p53.

[SCHEDULE_id] => 5404 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-23 09:45:00 [end_datetime] => 2015-03-23 10:30:00 ) [11] => Array ( [id] => 4124 [first_name] => Rachel [last_name] => Leander [affiliation_line] => Mathematics, Middle Tennessee State University [title] => Modeling intermitotic time distributions [abstract] =>

Cell division is one of the most fundamental processes of life, yet it is subject to significant random variation. Experiments have shown that, even in a population of homogeneous cells, the distribution of intermitotic times (IMTs) is highly variable. Furthermore, IMT distributions exhibit interesting temporal dynamics, especially in response to perturbations such as drug treatment. Using a top-down approach, we have developed a stochastic model of the cell cycle that is based on the cell cycle check point. This model enables us to frame the problem of determining a cell's IMT as a first exit time problem, through which we derive an expression for the distribution of IMTs. This distribution can be analyzed in order to relate distribution properties and dynamics to model parameters.

[SCHEDULE_id] => 5458 [registration_type] => Participant [registration_subtype] => [start_datetime] => 2015-03-27 09:45:00 [end_datetime] => 2015-03-27 10:30:00 ) [12] => Array ( [id] => 3985 [first_name] => Radhakrishnan [last_name] => Mahadevan [affiliation_line] => Chemical Engineering & Applied Chemistry, University of Toronto [title] => Stoichiometric and Ensemble modeling of “Respiro-fermentation� [abstract] =>

The simultaneous utilization of efficient respiration and inefficient fermentation even in the presence of abundant oxygen is a puzzling phenomenon commonly observed in bacteria, yeasts, and cancer cells. In the first part of the talk we will present work on the modeling of respiro-fermentation on bacteria. One of the possible explanations is the presence of additional constrains on the metabolic fluxes and we have shown that in the case of overflow metabolism in E. coli, one can simulate respiro-fermentation. In the second part of the talk we will focus on the use of metabolic modeling to analyze such inefficient metabolism in cancer cells.

The metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this talk, we will present how Ensemble Modeling (EM) framework can be used to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire ensemble of models to obtain a sub-set, which is increasingly predictive. The resulting models predicted additional targets that can cause significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of multiple reactions will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets. Finally, in the last part of the talk we will present work done in collaboration with Prof. McGuigan’s group on the development of 3D bioreactor system that can provide improved data for modeling and analysis of metabolism using stoichiometric and kinetic modeling.

[SCHEDULE_id] => 5436 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-25 14:00:00 [end_datetime] => 2015-03-25 14:45:00 ) [13] => Array ( [id] => 4134 [first_name] => Christian [last_name] => Mazza [affiliation_line] => Department of mathematics 23 chemin du Musée CH-1700 Fribourg, Department of Mathematics University of Fribourg [title] => Phenotypic diversity and population growth in fluctuating environments [abstract] =>

Organisms in fluctuating environments must constantly adapt their behavior to survive. We consider strategies where cells switch their phenotypes randomly or use costly sensing mechanisms to respond optimally to environmental changes . The strategies are compared using net growth rates and Lyapunov exponents for models involving random differential equations and branching processes in random enviroments.

[SCHEDULE_id] => 5447 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-26 11:45:00 [end_datetime] => 2015-03-26 12:30:00 ) [14] => Array ( [id] => 3984 [first_name] => Rafael [last_name] => Moreno-Sanchez [affiliation_line] => Biochemistry, Instituto Nacional de Cardiologia and Universidad Nacional Autonoma de Mexico [title] => Kinetic modeling of cancer glycolysis [abstract] =>

Glycolysis provides cytosolic ATP and NADH as well as precursors for several anabolic pathways. These are probably the reasons why most cancer cells have an enhanced glycolytic capacity. To have a better understanding of the controlling mechanisms of this essential pathway, and to unveil suitable and alternative therapeutic targets, kinetic models were built up for glycolysis in cancer cells under a variety of experimental conditions. The kinetic models were constructed using : (i) all the kinetic parameters of all enzymes and transporters involved in the pathway, all determined under near-physiological conditions of pH, temperature and medium composition; (ii) the enzyme activities, metabolite concentrations and fluxes determined in living cells; (iii) the description of appropriate rate equations for all pathway steps, including reversibility or equilibrium constants; and (iv) an iterative process of re-experimentation and refinement of the kinetic models. Once the models were validated, by comparing the model predicted pathway behavior regarding intermediary concentrations and fluxes with that determined experimentally in living cells, they were further used to establish the pathway control.

In cancer cells, kinetic modeling indicated that glucose transporter (GLUT), hexokinase (HK), glycogen degradation (GlycDeg) and/or hexosephosphate isomerase (HPI) were the main flux- and ATP concentration-controlling steps. Although the specific contribution of each of these steps varied, depending on the O2 level (normoxia, severe hypoxia), initial external glucose concentration (25 mM, 2.5 mM), or cancer cell type (AS-30D, HeLa), the same steps kept the pathway control, i.e. the mechanisms that govern the control of cancer glycolysis are preserved and are highly robust. The glycolytic flux increased under low glucose (+ normoxia), which was accompanied by no significant variation in total GLUT and HK activities; instead an increased affinity for glucose emerged as a consequence of a shift in activity from low to high affinity isoforms (GLUT-3 over GLUT-1; and HK-I over HK-II). Modeling appointed GLUT as the principal controlling step in HeLa cells exposed to low glucose; indeed, glycolysis in these cells were more sensitive to GLUT inhibition than cells exposed to high glucose. The glycolytic flux also increased under hypoxia (+ high glucose), but in this case most glycolytic proteins were over-expressed, including the low affinity GLUT and HK isoforms, and with no variation in the high affinity isoforms. Thus, pathway flux can be modulated by changing the isoform pattern (low glucose) and over-expressing (hypoxia) the most controlling steps.

Kinetic modeling identified GLUT, HK, GlycDeg and/or HPI as the foremost therapeutic targets; their simultaneous and partial, not complete, inhibition will have greater deleterious effects on cancer glycolysis, and possibly on cell growth and viability.

This work was partly supported by CONACyT-Mexico grants Nos. 180322, 107183, and 178638.

[SCHEDULE_id] => 5434 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-25 11:45:00 [end_datetime] => 2015-03-25 12:30:00 ) [15] => Array ( [id] => 3989 [first_name] => Jacques [last_name] => Pouyssegur [affiliation_line] => Institute for Research on Cancer & Aging , nice (IRCAN), [title] => Targeting pHi control and Bioenergetics in rapidly growing Hypoxic Tumours [abstract] =>

Intense interest in the “Warburg effect” has been revived by the discovery that hypoxia-inducible factor 1 (HIF1) re-programs pyruvate oxidation to lactic acid conversion. The most aggressive and invasive cancers, which are often hypoxic, rely on exacerbated glycolysis to meet their increased ATP and biosynthetic precursors demands and on robust pHi regulating systems to combat excessive generation of lactic and carbonic acids.

We and others hypothesized (Pouysségur et al., Nature, 2006, 441:437) that disruption of the key pHi regulating systems Na+-H+ Exchangers (NHE), Na-dependent Bicarbonate Transporters (NBTs), Carbonic Anhydrases CAIX, XII (CAs), MonoCarboxylate Transporters (MCT1/4) might offer a H+-mediated killing strategy to eradicate fast growing tumours. This approach has demonstrated efficient tumor growth arrest but a rather limited success as far as tumor cell killing is considered. Here, we will discuss the limitations of this strategy and present the concept of ‘metabolic catastrophe’ as an alternative way to kill tumor cells as recently discussed by Parks et al. in Nature Reviews Cancer, 2013, 13, 611.

We will illustrate, in colon adenocarcinoma and glioblastoma cell lines (LS174 and U87), that blocking lactic acid export by genetic disruption of MCT1 and MCT4 or of basigin/MCTs complexes does compromise tumor growth but not viability since tumor cells rapidly re-activate OXPHOS. However we will demonstrate that blocking lactic acid export provides an efficient anticancer approach when combined with phenformin, a mitochondrial complex I inhibitor (Marchiq I. et al., Cancer Research, 2014, Nov.)

[SCHEDULE_id] => [registration_type] => Speaker [registration_subtype] => [start_datetime] => [end_datetime] => ) [16] => Array ( [id] => 3975 [first_name] => Vito [last_name] => Quaranta [affiliation_line] => Department of Cancer Biology, Vanderbilt University [title] => Modeling targeted therapy response in oncogene addiction [abstract] =>

With a high-throughput colony Fractional Proliferation (cFP) assay, we simultaneously track in real-time the proliferation dynamics of hundreds to thousands of single-cell derived clones in a cell population exposed to perturbations (Frick et al, 2015, DOI: 10.1002/jcp.24888). In the mutant EGFR-addicted PC9 lung cancer cell line treated with erlotinib, cell fates (death, quiescence, continued proliferation) within each clone vary from cell-to-cell, even between siblings. This widespread heterogeneity of drug response is captured by a new metric, the drug-induced proliferation (DIP) rate, which encapsulates single-cell variation into a dynamic measure of drug response outcomes.

DIP rates variation from colony to colony in PC9 is approximately normally distributed, a strong indication it arises from stochastic sources. Measurement error or mixed colony ancestry could not account for this variation, since DIP rates of PC9 sublines isolated from single cells and propagated in long-term culture (PC9-DS1/95) exhibited the same normal distribution and maintained it for over 25 generations. Similar distributions were obtained from many additional oncogene-addicted cell lines, rigorously re-derived from single cells. Thus, a mutated driver oncogene does not ensure cell-to-cell homogeneity of response, even when genetic background diversity is minimized.

To explore whether these distributions are of consequence to treatment, we constructed a Polyclonal Growth (PG) mathematical model able to incorporate theoretical or experimental DIP rates as parameters. Since DIP rate distributions are normal, they are entirely defined by two parameters, mean and variance. Inputting the average DIP rate of parental PC9 predicts that the cell line as a whole will completely succumb to treatment. In contrast, with the DIP rate distribution parameters as input, a completely different result was obtained: the size of the erlotinib treated population rebounded to initial values after ~11 days, after an initial drop to half the value at 5 days. The PG model predicted similar dynamics of erlotinib response for several mutant EGFR-addicted cell lines: in every cell line tested, rebound occurred within days to weeks, after initial drops to varying depths. Time to rebound is affected primarily by the extent to which the right tail of the DIP rate distribution extends into positive territory. Using stochastic simulations of the PG model, we are able to differentiate the effects of clonal heterogeneity from those of stochastic cell fate decisions (intrinsic noise) that cause significant variability in the response trajectories, including response depth and duration. Predictions were validated experimentally in PC9. It is unlikely that conventional acquired resistance was responsible for the rebound, since SNaPshot multigene assays were negative and response dynamics were inconsistent with a model of rare drug resistant clones. These findings suggest that, even in the absence of acquired genetic resistance, heterogeneity of drug response promotes rebound of the treated population. We propose that these experimental and modeling tools (cFP assay and PG model) enable realistic evaluation of depth and duration of response to targeted drug treatment. Expected and unexpected PG model predictions and suggested avenues for treatment, especially drug combinations, will be discussed.

[SCHEDULE_id] => 5417 [registration_type] => Organizer [registration_subtype] => [start_datetime] => 2015-03-24 09:00:00 [end_datetime] => 2015-03-24 09:45:00 ) [17] => Array ( [id] => 3990 [first_name] => Jeffrey [last_name] => Rathmell [affiliation_line] => Pharmacology and Cancer Biology, Duke University [title] => The Metabolism of Proliferating and Leukemic Lymphocytes [abstract] =>

Abstract not submitted.

[SCHEDULE_id] => 5403 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-23 09:00:00 [end_datetime] => 2015-03-23 09:45:00 ) [18] => Array ( [id] => 4085 [first_name] => Osbaldo [last_name] => Resendis-Antonio [affiliation_line] => Computational Genomics Consortium, Instituto Nacional de Medicina Genomica [title] => Systems Biology and the challenges for elucidating the role of biological networks in cancer [abstract] =>

Systems Biology is an emergent science whose main objective is to understand and predict the phenotype of a microorganism through the parallel analysis of high throughput data and computational modeling. This systemic, integrative and quantitative description is a new paradigm in genome sciences that contribute to understand the metabolic profile supporting the phenotype in a variety of organism, ranging from the bacteria to the study of metabolic alterations in human diseases. Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotype states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches in combination with genome scale metabolic reconstructions have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells, such as the Warburg effect. In this talk I will present some formalisms that can serve as a platform to: 1) integrate and interpret high-throughput data; 2) generate biological hypothesis about their metabolic activity; and 3) design experiments to assess the genotype-phenotype relationship. Given the overwhelming complexity in cancer, multidisciplinary approaches are required to construct the bases of a systemic and personalized medicine, which remains as a fundamental task in the medicine of this century.

[SCHEDULE_id] => 5437 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-25 14:45:00 [end_datetime] => 2015-03-25 15:30:00 ) [19] => Array ( [id] => 4121 [first_name] => Ed [last_name] => Reznik [affiliation_line] => Computational Biology, Memorial Sloan Kettering Cancer Center [title] => How Many MTDNA's Does It Take to Make a Tumor? [abstract] =>

In cancer, mitochondrial dysfunction, through mutations, deletions, and changes in copy number of mitochondrial DNA (MTDNA), contributes to the malignant transformation and progression of tumors. Here, we report the first large-scale survey of MTDNA copy number variation across 21 distinct solid tumor types, examining over 8,000 samples of tumor and adjacent normal tissue profiled with next-generation sequencing methods. Our findings uncover a tendency for cancers, especially bladder, breast, and kidney tumors, to be significantly depleted of MTDNA, relative to matched normal tissue. In a subset of tumor types, including kidney chromophobe and adrenocortical carcinomas, MTDNA copy number is significantly associated to patient survival. We show that MTDNA copy number is correlated to the expression of mitochondrially-localized metabolic pathways, suggesting that MTDNA accumlation and depletion reflect gross changes in mitochondrial metabolic activity. Finally, we identify a subset of tumor-type-specific somatic alterations, including IDH1 and NF1 mutations in gliomas, whose incidence is strongly correlated to MTDNA copy number. Our findings point to an intimate connection between MTDNA content and the molecular events underlying the initiation and progression of tumors.

[SCHEDULE_id] => 5418 [registration_type] => Participant [registration_subtype] => [start_datetime] => 2015-03-24 09:45:00 [end_datetime] => 2015-03-24 10:30:00 ) [20] => Array ( [id] => 3988 [first_name] => R. Brooks [last_name] => Robey [affiliation_line] => Research & Development Service, Veterans Afaairs Medical Center [title] => Warburg and Tumor Metabolism Revisited - Hexokinases, Glycolysis, and the Metabolic Gestalt of the Cell [abstract] =>

Nearly a century has elapsed since Warburg and colleagues first applied contemporary manometric techniques to the biochemical characterization of cancer metabolism. Their studies identified several cardinal features of tumor metabolism, most notably increased glucose-derived lactate generation in the presence, as well as the absence, of O2 - or so-called aerobic glycolysis. Recent advances in our understanding of the relationship between metabolism and cell survival and a resurgent interest in targeting cancer metabolism for therapeutic benefit have refocused attention on the characteristic features of cancer that Warburg described, as well as their mechanistic underpinnings. Hexokinases catalyze the first committed step of glucose metabolism, are overexpressed in cancer, and have emerged as important mediators of the anti-apoptotic effects of growth factors and Akt. They also directly contribute to the signature glycolytic phenotype of tumors. The ability of hexokinases to prevent apoptosis is mediated, in part, by direct physical and functional interaction with mitochondria and competition with pro-apoptotic Bcl-2 proteins for binding to common mitochondrial target sites. Bound hexokinases also facilitate the exchange of adenine nucleotides and other anionic metabolites into and out of mitochondria, thereby promoting mitochondrial integrity and directly coupling the metabolism of glucose in the cytosol to terminal substrate oxidation and oxidative phosphorylation within mitochondria. This and closely related forms of metabolic crosstalk play important roles in the coordination and control of intra- and extramitochondrial amphibolic metabolism and contribute to the characteristic proliferative and metabolic phenotypes of cancer cells. Considered in the context of the metabolic gestalt of the cell, these coupling mechanisms may also constitute attractive potential targets for therapeutic cancer intervention.

[SCHEDULE_id] => 5444 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-26 09:45:00 [end_datetime] => 2015-03-26 10:30:00 ) [21] => Array ( [id] => 3993 [first_name] => Santiago [last_name] => Schnell [affiliation_line] => Department of Molecular & Integrative Biology, University of Michigan Medical School [title] => Reverse engineering signaling pathway in cancer cells: Effects of nonokiol on the notch signaling pathway as a case study [abstract] =>

The ability to accurately infer an intracellular network from data remains a significant and difficult problem in molecular systems biology. We developed a novel network inference methodology that integrates measurements of protein activation from perturbation experiments. The approach was validated in silico with a set of test networks and applied to investigate the effects of honokiol on the notch signaling pathway in SW480 colon cancer cells. Our methodology relies on logic-based networks to provide a predictive approximation of the transfer of signals in a network. The method can also be leveraged to identify additional perturbation experiments needed to distinguish between a set of possible candidate networks. The development of methodologies that permit the accurate prediction of connectivity in dysregulated pathways may enable more rational determination of what therapy is best for a patient.

[SCHEDULE_id] => 5457 [registration_type] => Organizer [registration_subtype] => [start_datetime] => 2015-03-27 09:00:00 [end_datetime] => 2015-03-27 09:45:00 ) [22] => Array ( [id] => 4122 [first_name] => Ashwini [last_name] => Sharma [affiliation_line] => Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ) [title] => Linear proximity of cancer causing and metabolic genes in the genome -does it drive metabolic reprogramming via somatic copy number changes? [abstract] =>

Reprogramming of metabolism is an emerging hallmark of cancer. Metabolic genes (MG) have been identified as oncogenes (OG) and tumor suppressor genes (TSG) or targets of oncogenic signaling. Cancer is a direct consequence of genomic aberrations, such as somatic copy number alterations (SCNA) that frequently occur across many cancer types affecting not only OG and TSG, bur also multiple passenger and "potential" co-driver genes at the perturbed loci. l will present our recent work on elucidating how linear proximity of MG and cancer causing genes (CG) in the chromosomes can lead to metabolic remodeling. We have developed the analysis pipeline Identification of Metabolic Cancer Genes (iMetCG) to interrogate such events by integrating data for 19 different cancer types from TCGA that led to the identification of novel metabolic cancer genes.

[SCHEDULE_id] => 5420 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-24 11:00:00 [end_datetime] => 2015-03-24 11:45:00 ) [23] => Array ( [id] => 3982 [first_name] => Siv [last_name] => Sivaloganathan [affiliation_line] => Applied Mathematics, University of Waterloo [title] => Cancer cell metabolism and it’s impact on the tumour microenvironment [abstract] =>

Targeting metabolic pathways in malignant tumours shows increasing promise as an effective therapeutic strategy in clinical oncology. Thus, unravelling details of metabolic pathways used by cancer cells, particularly those pathways that are differentially activated or suppressed in tumours, is of much current interest. In 1997, Helmlinger et al published “in-vivo” experimental results of pH and pO2 levels as functions of distance from a single blood vessel, on the micrometer scale. We show how these results provide unique insights into cancer cell metabolism when combined with an appropriate mathematical model.

[SCHEDULE_id] => 5431 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-25 09:45:00 [end_datetime] => 2015-03-25 10:30:00 ) [24] => Array ( [id] => 4126 [first_name] => Sriram [last_name] => Venneti [affiliation_line] => Pathology, University of Michigan [title] => Evaluating glutamine addiction in gliomas using PET imaging [abstract] =>

Cancer cells commonly undergo metabolic reprograming enabling increased nutrient use to fuel their growth and proliferation. The Warburg effect is the classic example wherein tumors exhibit enhanced glucose uptake and metabolism through aerobic glycolysis. This increase in glucose uptake can be evaluated in vivo using positron emission tomography (PET) imaging with the glucose analogue 18F-fluorodeoxyglucose (18F-FDG). 18F-FDG PET imaging is a valuable clinical tool and is routinely used in diagnosing, grading and staging cancers. However, 18F-FDG is of limited value in evaluating gliomas in vivo due to high background glucose metabolism in the normal brain resulting in suboptimal tumor delineation. Glutamine is the most abundant amino acid in the plasma and many cancers are addicted to glutamine for their survival. We have recently developed 4-18F-(2S,4R)-fluoroglutamine (18F-FGln) for PET imaging in vivo. We evaluated glutamine uptake using PET imaging with 18F-FGln in vivo in glioma animal models to demonstrate that 18F-FGln showed high uptake in gliomas but minimal uptake in the normal brain, enabling clear tumor visualization. We translated these findings to human glioma subjects where 18F-FGln showed high tumor/background ratios in human glioma patients with progressive disease in contrast to that observed with 18F-FDG. These data suggest that 18F-FGln is specifically taken up by gliomas, can be used to assess metabolic nutrient uptake in gliomas in vivo and may serve as a valuable tool in the clinical management of gliomas.

[SCHEDULE_id] => 5450 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-26 14:45:00 [end_datetime] => 2015-03-26 15:30:00 ) [25] => Array ( [id] => 4128 [first_name] => Jin [last_name] => Wang [affiliation_line] => Chemistry and Physics, Stony Brook University [title] => Landscape and Flux of Cell Cycle and Cancer [abstract] =>

Understanding the mechanisms of the cell cycle remains challenging. The cell cycle is regulated by the underlying generegulatory networks. We uncovered the underlying Mexican hat landscape of a mammalian cell cycle network. Three local basins of attraction along the cell cycle loop emerge, corresponding to three distinct cell cycle states: the G1, S/G2, and M phases. Two barriers along the loop characterize G1 and S/G2 checkpoints, respectively, of the cell cycle, which provide a physical explanation for cell cycle checkpoint mechanisms. The cell cycle is determined by two driving forces: curl flux and potential barriers. We uncovered the key gene regulations determining the progression of cell cycle, which can be used to guide the design of new anticancer tactics.

Cancer is a disease regulated by the underlying gene networks. The emergence of normal and cancer states as well as the transformation between them can be thought of as a result of the gene network interactions and associated changes. We developed a global potential landscape and path framework to quantify cancer and associated processes. We constructed a cancer gene regulatory network based on the experimental evidences and uncovered the underlying landscape. The resulting tristable landscape characterizes important biological states: normal, cancer and apoptosis. The landscape topography in terms of barrier heights between stable state attractors quantifies the global stability of the cancer network system. We propose two mechanisms of cancerization: one is by the changes of landscape topography through the changes in regulation strengths of the gene networks. The other is by the fluctuations that help the system to go over the critical barrier at fixed landscape topography. The kinetic paths from least action principle quantify the transition processes among normal state, cancer state and apoptosis state. The kinetic rates provide the quantification of transition speeds among normal, cancer and apoptosis attractors. By the global sensitivity analysis of the gene network parameters on the landscape topography, we uncovered some key gene regulations determining the transitions between cancer and normal states. This can be used to guide the design of new anti-cancer tactics, through cocktail strategy of targeting multiple key regulation links simultaneously, for preventing cancer occurrence or transforming the early cancer state back to normal state.

[SCHEDULE_id] => 5423 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-24 14:00:00 [end_datetime] => 2015-03-24 14:45:00 ) [26] => Array ( [id] => 3980 [first_name] => Lani [last_name] => Wu [affiliation_line] => Pharmaceutical Chemistry, University of California San Francisco [title] => Inferring signaling crosstalk from cellular heterogeneity [abstract] =>

Cancer cell populations can be highly heterogeneous in their signaling phenotypes. This heterogeneity is often viewed as an impediment to understanding how information flows within cells. However, we have found recently that cell-to-cell variability can actually be used to infer network topology.

[SCHEDULE_id] => 5424 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-24 14:45:00 [end_datetime] => 2015-03-24 15:30:00 ) [27] => Array ( [id] => 3991 [first_name] => Thomas [last_name] => Yankeelov [affiliation_line] => Radiology, Vanderbilt University [title] => Quantitative Imaging to Drive Biophysical Models of Tumor Growth [abstract] =>

The ability to identify—early in the course of therapy—patients that are not responding to a given therapeutic regimen is highly significant. In addition to limiting patients’ exposure to the toxicities associated with unsuccessful therapies, it would allow patients the opportunity to switch to a potentially more efficacious treatment. In this presentation, we will discuss ongoing efforts at using data available from advanced imaging technologies to initialize and constrain predictive biophysical and biomathematical models of tumor growth and treatment response.

[SCHEDULE_id] => 5449 [registration_type] => Speaker [registration_subtype] => [start_datetime] => 2015-03-26 14:00:00 [end_datetime] => 2015-03-26 14:45:00 ) )
Metabolic Pathways and the Restriction Point in the Cell Cycle

The Restriction Point (RP) is a checkpoint in G1 phase that marks the transition from growth factor-dependent to growth factor-independent cell cycle progression. Its core switching mechanism involves cyclin E/CDK2, the retinoblastoma protein, and transcription factors E2F and MYC. Models of RP dynamics that we and others have proposed earlier do not explicitly consider the bioenergetic and biosynthetic processes that drive the cell cycle. In this talk, I will discuss the links among RP, glycolysis and glutaminolysis, and then explore the potential control points in the expanded network.

Oncogenic Myc Disrupts Circadian Rhythm through Upregulation of Rev-erbα

Circadian rhythms are regulated by feedback loops comprising a network of factors that regulate Clock-associated genes. Chronotherapy seeks to take advantage of altered circadian rhythms in some cancers to better time administration of treatments to increase efficacy and reduce toxicity. However, there is currently no basis to identify which cancers have disrupted circadian rhythms and would be amenable to chronotherapy. c- and N-Myc are oncogenic transcription factors translocated or amplified in many cancers. While the role of Myc in circadian rhythm is currently unknown, it may affect circadian rhythm by binding to the same E-box promoter regions used by the central regulators of circadian rhythm, Clock/Bmal1. Here we show in neuroblastoma, osteosarcoma, and hepatocellular carcinoma cells that overexpressed Myc specifically upregulated the circadian regulator Rev-erbα, which in turn decreased expression of Bmal1. Importantly, Myc-expressing cells showed dramatically disrupted circadian oscillations, which could be rescued by inhibiting expression of Rev-erbα and β. Increased Rev-erbα was observed in primary human neuroblastoma and was correlated with poor prognosis. Together, these data suggest that Myc-driven cancers have altered circadian oscillation due to upregulation of Rev-erbα, and that cancers driven by Myc may thus be good candidates for chronotherapy.

Relating cancer cell heterogeneity to drug resistance

Cancer cell populations can be highly heterogeneous. We will discuss progress in understanding the origins of this heterogeneity and its implications in predicting drug response.

Modeling metabolic networks with an ensemble evolutionary flux balance analysis approach

Metabolism can be seen as the biochemical factory producing building blocks and energy for cellular functioning. The study of its control mechanisms and dysregulation is today an area of intense application of modeling efforts. Due to the complexity and dimension of metabolic networks, mechanistic modeling of their dynamics becomes unfeasible. For this reason, the computational investigation of metabolic models makes typically use of constraint-based approaches, which exploits the knowledge about the structure of cell metabolism, while disregarding dynamic intracellular behavior, on the basis of a pseudo-steady state assumption.

Assuming also that cell behavior is optimal with respect to a “metabolic objective”, flux balance analysis (FBA) is widely used to calculate a single optimal flux distribution. This approach has proven to be effective in implementing metabolic engineering design goals, such as the maximization of the cell production of metabolites of industrial interest. However, FBA has recently received increasing attention in Systems Biology, to gain novel knowledge about the physiological state of a cell. In this regard, the assumption of maximization of biomass yield as objective function has revealed successful in predicting some phenotypical characteristics of microorganisms. Nevertheless, when dealing with multicellular organisms, the definition of a plausible objective function is not straightforward and, besides, even if we knew the true objective function, we could not still exclude that the systems is in a sub-optimal space. For this reason, new approaches aimed at describing global network properties are emerging for an unbiased analysis of the solutions space. In this context, we proposed an extension of the classic constraint-based modeling approach in order to overcome the problem of defining an objective function and to analyze the space of fluxes distribution using clustering techniques.

In particular, the developed method does not focus on a given flux distribution assuming that it corresponds to the real one, but is rather designed to explore the solution space sampling it by means of several distinct random objective functions and looking for an ensemble solutions that match a given metabolic phenotype (the system expected behavior). The identified ensemble may indeed be capable of greater prediction accuracy than any of their individual members. By analyzing the generic properties of the ensemble of matching solutions we may identify some patterns responsible for the emergent phenotype. If it is the case that a metabolic phenotype may result form alternative sub-phenotypes. By finding clusters of similar solutions and analyzing their common/distinguishing properties we may therefore gain information about such possible sub-phenotypes. The approach reveals even more effective when two metabolic responses, let say for example a physiological against a pathological one, are compared. In this case, two different ensembles will therefore be obtained: one that matches the former conditions and the other that matches the latter. By comparing the generic properties of the two ensembles the pathways mainly involved in the differential response will be disentangled. It goes without saying that the expected behavior must be abstracted and formalized, and we propose to express the definition in terms of a metabolic response to a condition variation (e.g. redistribution of fluxes as a consequence of a variation in nutrient availability). Depending on the specific problem, the match between a solution and the metabolic response definition can be Boolean (the condition is either met or not) or expressed in term of a fitness (how close the condition is) exploiting an evolutionary algorithm.

We tested our method on a yeast core metabolic model, from which we identified two ensembles of solutions, the first in agreement with a definition for “Crabtree-positive yeasts” and the second in agreement with a definition for “Crabtree-negative yeasts”. In a further step we showed how the ensembles can be further characterized and refined by means of a cluster analysis in order to identify sub-phenotypes. Finally the fluxes that significantly differ between the two ensembles have been identified according to a Kolmogorov-Smirnov test. The aim now, in SysBio – Italy, is to apply this method to the experimentally validated network structures of cancer metabolic rewiring.

Systematic analysis of adaptive resistance and fractional responses of melanoma cells to RAF/MEK inhibition

Treatment of BRAFV600E melanomas with drugs, such as vemurafenib, that inhibit RAF/MEK signaling is effective in the short term, but remission is not durable. Drug resistance appears to involve short-term adaptive responses that compensate for RAF/MEK inhibition via up-regulation of other pro-growth mechanisms. Understanding and ultimately preventing adaptive responses is a key to durable therapy. Systematic data comparing BRAFV600E tumor cells is generally lacking and it is not known whether adaptation is fundamentally similar across cell types or among individual cells within a cell population.
We apply a systematic approach to studying the responses of human melanoma cell lines to five RAF and MEK inhibitors, with the overall goal of (i) characterizing variability in adaptation with time, dose, cell type and across individual cells, (ii) discovering new or poorly characterized adaptive mechanisms, and (iii) demonstrating the effectiveness of a high-throughput approach involving multiplex measurement, single-cell analysis and computational modeling. The data involves time-course measurement of total level and activity of signaling proteins and cell state markers using array-based methods and single-cell immunofluorescence assays as well as measurement of apoptosis and cell viability under the same conditions. Statistical modeling using partial least squares regression (PLSR) revealed which of the changes in the ~200,000 point dataset were phenotypically consequential.
We found that responses to RAF inhibitors are remarkably diverse and involve multiple pathways that can be up or down-regulated over time, with significant variability across cell types and individual cells. We identified a role for JNK/c-Jun signaling in altering the cell-cycle distribution of melanoma cells, causing apoptosis-resistant cells to accumulate and drug maximal effect (Emax) to fall; co-drugging with RAF and JNK inhibitors or JUN knockdown reverse this effect. The primary effect of JNK inhibitors is to minimize the cell-to-cell variability in pS6 suppression, promoting the induction of apoptosis.
Our study shows that a systems-level approach (combining high density time-dependent measurements, quantitative modeling and single-cell analysis) may provide a general framework for evaluating new drugs with adaptive and paradoxical response, and identifying potentially useful combination therapies.

The influence of mathematics on medicine and public health

In this talk I will give two examples: atherosclerosis/cholesterol modeling, and kidney fibrosis, based on two papers we published this year with Wenrui Hao, MBI postdoc, in PLoS One, and in PNAS. The first paper develops cholesterol guidelines (we call it "risk map") more refined than those suggested by the American Heart Association. The second paper opens the possibility of monitoring the disease of kidney fibrosis without the need to do repeated biopsies. Both models are described by systems of PDEs. The models also offer possible treatments, but human data will be needed in order to verify the conclusions of the models.

Institute of Bioimmaging and Molecular Physiology-CNR, Segrate (Milan)

The investigation on metabolic profiling of normal and cancer cells is recently gaining more interest in molecular oncology due to the understanding that a metabolic rewiring underlies the ability of uncontrolled proliferation of cancer cells. It is not only the well known Warburg effect, but also an increased utilization of glutamine by reductive carboxylation that takes place in cancer cells.

We have developed transcriptional and metabolic analysis in various cancer cell lines, to reach a more detailed definition of the metabolic steps involved in cancer metabolic rewiring and on its redox regulation. These data will be discussed in the logic of a drug discovery attempt. The construction of mathematical models of the indicated cancer metabolic rewiring is in progress.

Massive re-organization of the liver chromatin landscape following metabolic and inflammatory signals

Diabetes and chronic inflammation are major risk factors for developing various cancer types, including liver cancer. We set out to study the chromatin and transcriptional changes in liver following metabolic and inflammatory signals that are associated with a cancer-promoting phenotype.

One of the hallmarks of diabetes is a hyper-activated, de-regulated response to fasting orchestrated mainly by the liver. In non-pathological states, this response is achieved by eliciting a comprehensive and elaborate transcriptional program leading to fuel production in the form of glucose and ketone bodies. These changes in transcription are mediated by alterations in chromatin structure and transcription factor occupancy. To evaluate the alterations in chromatin landscape and gene expression following fasting, we analyzed livers from fasted mice in three high-throughput experiments. We employed the DNase I Hyper-Sensitivity assay followed by sequencing (DHS-seq) to globally map the accessible regions of chromatin; thus detecting transcriptional regulatory nodes in chromatin (mainly promoters and enhancers). We have found ~4,000 sites in liver chromatin in which accessibility was altered following fasting. These altered regulatory regions were enriched in binding sites for transcription factors known to regulate the fasting response. We obtained similar results by globally mapping active enhancers (by chromatin immunoprecipitation of the active enhancer mark H3K27Ac followed by sequencing – ChIP-seq). Moreover, we determined the alterations in gene expression by profiling the transcriptome of those livers using RNA-seq, with the aim of linking between alterations in chromatin landscape and gene expression. Regions with increased accessibility and increased H3K27 acetylation following fasting (suggesting active transcriptional regulation) were evidenced proximally to fasting induced genes. We present surprising findings showing that a very common metabolic perturbation (i.e. fasting) leads to a massive re-organization of liver chromatin which supports the onset of a complex, temporally organized, mutli-stage transcriptional response.

In a parallel effort, we characterized the chromatin and gene expression patterns of primary hepatocytes in response to three major pro-inflammatory cytokines associated with hepato-carcinogenesis (IL-6, IL-1 beta and TNF alpha). This analysis revealed a complex and diverse pattern of chromatin and gene regulation in which inflammatory cytokines play complementing roles whereby a certain cytokine ‘primes’ an enhancer while the second cytokine induces gene expression.

We believe that understanding the transcriptional response and its underlining chromatin regulation following cancer-promoting stimuli is critical to elucidating the mechanisms initiating carcinogenesis and is expected to aid in combating cancer as well as interconnected metabolic disorders

Targeting the Cell-Cell Interaction Network in Cancer Therapy: A Fat Chance

Tumors consist of a heterogeneous community of distinct cell types, including distinct tumor parenchyma cells as well as non-neoplastic stromal cells that jointly form a robust system of cells regulating one another’s proliferation –akin to a stable ecosystem. This communication network may account for higher-level, non-cell autonomous control of tumor resilience to therapy. Targeting this network, as opposed to the intracellular networks may offer a new opportunity to suppress the cell community that isthe tumor. Lipid autacoids, derived from polyunsaturated fatty acids are potent mediators of the cell-cell interaction network between tumor cells and the stroma and can promote and suppress tumor growth, thus may represent a new class of targets. (Fatty acids are often neglected components of metabolism sensu latiore…)

High Glycolytic Metabolism in Stem-Like Cancer Cells: Regulation by Glucose in the Microenvironment and Therapeutic Implications

Alterations in energy metabolism are associated with malignant transformation, and play a key role in cancer development and adaptation to changes in tumor microenvironment. This presentation will focus on alterations of glucose metabolism in stem-like cancer cells including side population (SP), the effect of glucose on SP cells, and a novel therapeutic strategy to kill CSCs, which are resistant to standard chemotherapeutic agents. Although it has been recognized that tumor tissue niches may significantly affect the stemness of cancer cells, the role of key nutrients such as glucose in the microenvironment to affect stem-like cancer cells largely remains elusive. We show that stem-lime cancer cells isolated from human cancer cells exhibit higher glycolytic activity compared to the non-stem cancer cells. Glucose in the culture environment exerts a profound effect on SP cells as evidenced by its ability to induce a significant increase in the percentage of SP in the overall cancer cell population, while glucose starvation causes a rapid decrease in SP cells. Mechanistically, the up-regulation of SP cells by glucose seems mediated by suppression of AMPK and activation of the Akt pathway, leading to elevated expression of the ATP-dependent efflux pump ABCG2. Importantly, inhibition of glycolysis significantly reduces SP cells in vitro and impairs their ability to form tumors in vivo. Combination of glycolytic inhibition and standard chemotherapeutic drugs was highly effective in eliminating cancer cells and improve anticancer activity. Our data suggests that glucose is an essential regulator of stem-like cancer cells mediated by the Akt pathway, and targeting glycolysis represents an attractive cancer treatment strategy with potential therapeutic benefits.

p53 Regulation of Mitochondria

p53, one of the most commonly mutated tumor suppressor genes in human cancers, promotes oxidative metabolism by regulating mitochondrial biogenesis through various mechanisms. These include the transactivation of mitochondrial biogenesis genes in the nucleus and the maintenance of mitochondrial genomic DNA by the translocation of p53 protein into the mitochondria. While oxygen is essential for oxidative metabolism, it also serves as the essential substrate for reactive oxygen species (ROS) that can damage the genome. Given the well-established role of p53 in maintaining genomic stability, its promotion of respiration that efficiently converts reactive oxygen to water may serve to contribute to the antioxidant activities of p53. Interestingly, one physiological impact of p53 promoting mitochondrial respiration is increased aerobic exercise capacity that parallels the strong inverse relationship between cardio-respiratory fitness and cancer-free survival observed in large epidemiologic studies. On the other hand, our recent observations from mouse and human studies of Li-Fraumeni syndrome, a premature cancer condition caused by germline mutations of p53, indicate that mutant p53 can also increase oxidative metabolism. We suggest that these disparate findings can be reconciled by the dissociation of the cell cycle and mitochondrial activities of p53. Our observations are also consistent with the growing evidence that cancer cells depend not only on aerobic glycolysis (Warburg effect) but also on mitochondrial metabolism which may contribute to tumorigenesis in the setting of defective cell cycle regulation by mutated p53.

Modeling intermitotic time distributions

Cell division is one of the most fundamental processes of life, yet it is subject to significant random variation. Experiments have shown that, even in a population of homogeneous cells, the distribution of intermitotic times (IMTs) is highly variable. Furthermore, IMT distributions exhibit interesting temporal dynamics, especially in response to perturbations such as drug treatment. Using a top-down approach, we have developed a stochastic model of the cell cycle that is based on the cell cycle check point. This model enables us to frame the problem of determining a cell's IMT as a first exit time problem, through which we derive an expression for the distribution of IMTs. This distribution can be analyzed in order to relate distribution properties and dynamics to model parameters.

Stoichiometric and Ensemble modeling of “Respiro-fermentation�

The simultaneous utilization of efficient respiration and inefficient fermentation even in the presence of abundant oxygen is a puzzling phenomenon commonly observed in bacteria, yeasts, and cancer cells. In the first part of the talk we will present work on the modeling of respiro-fermentation on bacteria. One of the possible explanations is the presence of additional constrains on the metabolic fluxes and we have shown that in the case of overflow metabolism in E. coli, one can simulate respiro-fermentation. In the second part of the talk we will focus on the use of metabolic modeling to analyze such inefficient metabolism in cancer cells.

The metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this talk, we will present how Ensemble Modeling (EM) framework can be used to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire ensemble of models to obtain a sub-set, which is increasingly predictive. The resulting models predicted additional targets that can cause significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of multiple reactions will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets. Finally, in the last part of the talk we will present work done in collaboration with Prof. McGuigan’s group on the development of 3D bioreactor system that can provide improved data for modeling and analysis of metabolism using stoichiometric and kinetic modeling.

Phenotypic diversity and population growth in fluctuating environments

Organisms in fluctuating environments must constantly adapt their behavior to survive. We consider strategies where cells switch their phenotypes randomly or use costly sensing mechanisms to respond optimally to environmental changes . The strategies are compared using net growth rates and Lyapunov exponents for models involving random differential equations and branching processes in random enviroments.

Kinetic modeling of cancer glycolysis

Glycolysis provides cytosolic ATP and NADH as well as precursors for several anabolic pathways. These are probably the reasons why most cancer cells have an enhanced glycolytic capacity. To have a better understanding of the controlling mechanisms of this essential pathway, and to unveil suitable and alternative therapeutic targets, kinetic models were built up for glycolysis in cancer cells under a variety of experimental conditions. The kinetic models were constructed using : (i) all the kinetic parameters of all enzymes and transporters involved in the pathway, all determined under near-physiological conditions of pH, temperature and medium composition; (ii) the enzyme activities, metabolite concentrations and fluxes determined in living cells; (iii) the description of appropriate rate equations for all pathway steps, including reversibility or equilibrium constants; and (iv) an iterative process of re-experimentation and refinement of the kinetic models. Once the models were validated, by comparing the model predicted pathway behavior regarding intermediary concentrations and fluxes with that determined experimentally in living cells, they were further used to establish the pathway control.

In cancer cells, kinetic modeling indicated that glucose transporter (GLUT), hexokinase (HK), glycogen degradation (GlycDeg) and/or hexosephosphate isomerase (HPI) were the main flux- and ATP concentration-controlling steps. Although the specific contribution of each of these steps varied, depending on the O2 level (normoxia, severe hypoxia), initial external glucose concentration (25 mM, 2.5 mM), or cancer cell type (AS-30D, HeLa), the same steps kept the pathway control, i.e. the mechanisms that govern the control of cancer glycolysis are preserved and are highly robust. The glycolytic flux increased under low glucose (+ normoxia), which was accompanied by no significant variation in total GLUT and HK activities; instead an increased affinity for glucose emerged as a consequence of a shift in activity from low to high affinity isoforms (GLUT-3 over GLUT-1; and HK-I over HK-II). Modeling appointed GLUT as the principal controlling step in HeLa cells exposed to low glucose; indeed, glycolysis in these cells were more sensitive to GLUT inhibition than cells exposed to high glucose. The glycolytic flux also increased under hypoxia (+ high glucose), but in this case most glycolytic proteins were over-expressed, including the low affinity GLUT and HK isoforms, and with no variation in the high affinity isoforms. Thus, pathway flux can be modulated by changing the isoform pattern (low glucose) and over-expressing (hypoxia) the most controlling steps.

Kinetic modeling identified GLUT, HK, GlycDeg and/or HPI as the foremost therapeutic targets; their simultaneous and partial, not complete, inhibition will have greater deleterious effects on cancer glycolysis, and possibly on cell growth and viability.

This work was partly supported by CONACyT-Mexico grants Nos. 180322, 107183, and 178638.

Targeting pHi control and Bioenergetics in rapidly growing Hypoxic Tumours

Intense interest in the “Warburg effect” has been revived by the discovery that hypoxia-inducible factor 1 (HIF1) re-programs pyruvate oxidation to lactic acid conversion. The most aggressive and invasive cancers, which are often hypoxic, rely on exacerbated glycolysis to meet their increased ATP and biosynthetic precursors demands and on robust pHi regulating systems to combat excessive generation of lactic and carbonic acids.

We and others hypothesized (Pouysségur et al., Nature, 2006, 441:437) that disruption of the key pHi regulating systems Na+-H+ Exchangers (NHE), Na-dependent Bicarbonate Transporters (NBTs), Carbonic Anhydrases CAIX, XII (CAs), MonoCarboxylate Transporters (MCT1/4) might offer a H+-mediated killing strategy to eradicate fast growing tumours. This approach has demonstrated efficient tumor growth arrest but a rather limited success as far as tumor cell killing is considered. Here, we will discuss the limitations of this strategy and present the concept of ‘metabolic catastrophe’ as an alternative way to kill tumor cells as recently discussed by Parks et al. in Nature Reviews Cancer, 2013, 13, 611.

We will illustrate, in colon adenocarcinoma and glioblastoma cell lines (LS174 and U87), that blocking lactic acid export by genetic disruption of MCT1 and MCT4 or of basigin/MCTs complexes does compromise tumor growth but not viability since tumor cells rapidly re-activate OXPHOS. However we will demonstrate that blocking lactic acid export provides an efficient anticancer approach when combined with phenformin, a mitochondrial complex I inhibitor (Marchiq I. et al., Cancer Research, 2014, Nov.)

Modeling targeted therapy response in oncogene addiction

With a high-throughput colony Fractional Proliferation (cFP) assay, we simultaneously track in real-time the proliferation dynamics of hundreds to thousands of single-cell derived clones in a cell population exposed to perturbations (Frick et al, 2015, DOI: 10.1002/jcp.24888). In the mutant EGFR-addicted PC9 lung cancer cell line treated with erlotinib, cell fates (death, quiescence, continued proliferation) within each clone vary from cell-to-cell, even between siblings. This widespread heterogeneity of drug response is captured by a new metric, the drug-induced proliferation (DIP) rate, which encapsulates single-cell variation into a dynamic measure of drug response outcomes.

DIP rates variation from colony to colony in PC9 is approximately normally distributed, a strong indication it arises from stochastic sources. Measurement error or mixed colony ancestry could not account for this variation, since DIP rates of PC9 sublines isolated from single cells and propagated in long-term culture (PC9-DS1/95) exhibited the same normal distribution and maintained it for over 25 generations. Similar distributions were obtained from many additional oncogene-addicted cell lines, rigorously re-derived from single cells. Thus, a mutated driver oncogene does not ensure cell-to-cell homogeneity of response, even when genetic background diversity is minimized.

To explore whether these distributions are of consequence to treatment, we constructed a Polyclonal Growth (PG) mathematical model able to incorporate theoretical or experimental DIP rates as parameters. Since DIP rate distributions are normal, they are entirely defined by two parameters, mean and variance. Inputting the average DIP rate of parental PC9 predicts that the cell line as a whole will completely succumb to treatment. In contrast, with the DIP rate distribution parameters as input, a completely different result was obtained: the size of the erlotinib treated population rebounded to initial values after ~11 days, after an initial drop to half the value at 5 days. The PG model predicted similar dynamics of erlotinib response for several mutant EGFR-addicted cell lines: in every cell line tested, rebound occurred within days to weeks, after initial drops to varying depths. Time to rebound is affected primarily by the extent to which the right tail of the DIP rate distribution extends into positive territory. Using stochastic simulations of the PG model, we are able to differentiate the effects of clonal heterogeneity from those of stochastic cell fate decisions (intrinsic noise) that cause significant variability in the response trajectories, including response depth and duration. Predictions were validated experimentally in PC9. It is unlikely that conventional acquired resistance was responsible for the rebound, since SNaPshot multigene assays were negative and response dynamics were inconsistent with a model of rare drug resistant clones. These findings suggest that, even in the absence of acquired genetic resistance, heterogeneity of drug response promotes rebound of the treated population. We propose that these experimental and modeling tools (cFP assay and PG model) enable realistic evaluation of depth and duration of response to targeted drug treatment. Expected and unexpected PG model predictions and suggested avenues for treatment, especially drug combinations, will be discussed.

The Metabolism of Proliferating and Leukemic Lymphocytes

Abstract not submitted.

Systems Biology and the challenges for elucidating the role of biological networks in cancer

Systems Biology is an emergent science whose main objective is to understand and predict the phenotype of a microorganism through the parallel analysis of high throughput data and computational modeling. This systemic, integrative and quantitative description is a new paradigm in genome sciences that contribute to understand the metabolic profile supporting the phenotype in a variety of organism, ranging from the bacteria to the study of metabolic alterations in human diseases. Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotype states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches in combination with genome scale metabolic reconstructions have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells, such as the Warburg effect. In this talk I will present some formalisms that can serve as a platform to: 1) integrate and interpret high-throughput data; 2) generate biological hypothesis about their metabolic activity; and 3) design experiments to assess the genotype-phenotype relationship. Given the overwhelming complexity in cancer, multidisciplinary approaches are required to construct the bases of a systemic and personalized medicine, which remains as a fundamental task in the medicine of this century.

How Many MTDNA's Does It Take to Make a Tumor?

In cancer, mitochondrial dysfunction, through mutations, deletions, and changes in copy number of mitochondrial DNA (MTDNA), contributes to the malignant transformation and progression of tumors. Here, we report the first large-scale survey of MTDNA copy number variation across 21 distinct solid tumor types, examining over 8,000 samples of tumor and adjacent normal tissue profiled with next-generation sequencing methods. Our findings uncover a tendency for cancers, especially bladder, breast, and kidney tumors, to be significantly depleted of MTDNA, relative to matched normal tissue. In a subset of tumor types, including kidney chromophobe and adrenocortical carcinomas, MTDNA copy number is significantly associated to patient survival. We show that MTDNA copy number is correlated to the expression of mitochondrially-localized metabolic pathways, suggesting that MTDNA accumlation and depletion reflect gross changes in mitochondrial metabolic activity. Finally, we identify a subset of tumor-type-specific somatic alterations, including IDH1 and NF1 mutations in gliomas, whose incidence is strongly correlated to MTDNA copy number. Our findings point to an intimate connection between MTDNA content and the molecular events underlying the initiation and progression of tumors.

Warburg and Tumor Metabolism Revisited - Hexokinases, Glycolysis, and the Metabolic Gestalt of the Cell

Nearly a century has elapsed since Warburg and colleagues first applied contemporary manometric techniques to the biochemical characterization of cancer metabolism. Their studies identified several cardinal features of tumor metabolism, most notably increased glucose-derived lactate generation in the presence, as well as the absence, of O2 - or so-called aerobic glycolysis. Recent advances in our understanding of the relationship between metabolism and cell survival and a resurgent interest in targeting cancer metabolism for therapeutic benefit have refocused attention on the characteristic features of cancer that Warburg described, as well as their mechanistic underpinnings. Hexokinases catalyze the first committed step of glucose metabolism, are overexpressed in cancer, and have emerged as important mediators of the anti-apoptotic effects of growth factors and Akt. They also directly contribute to the signature glycolytic phenotype of tumors. The ability of hexokinases to prevent apoptosis is mediated, in part, by direct physical and functional interaction with mitochondria and competition with pro-apoptotic Bcl-2 proteins for binding to common mitochondrial target sites. Bound hexokinases also facilitate the exchange of adenine nucleotides and other anionic metabolites into and out of mitochondria, thereby promoting mitochondrial integrity and directly coupling the metabolism of glucose in the cytosol to terminal substrate oxidation and oxidative phosphorylation within mitochondria. This and closely related forms of metabolic crosstalk play important roles in the coordination and control of intra- and extramitochondrial amphibolic metabolism and contribute to the characteristic proliferative and metabolic phenotypes of cancer cells. Considered in the context of the metabolic gestalt of the cell, these coupling mechanisms may also constitute attractive potential targets for therapeutic cancer intervention.

Reverse engineering signaling pathway in cancer cells: Effects of nonokiol on the notch signaling pathway as a case study

The ability to accurately infer an intracellular network from data remains a significant and difficult problem in molecular systems biology. We developed a novel network inference methodology that integrates measurements of protein activation from perturbation experiments. The approach was validated in silico with a set of test networks and applied to investigate the effects of honokiol on the notch signaling pathway in SW480 colon cancer cells. Our methodology relies on logic-based networks to provide a predictive approximation of the transfer of signals in a network. The method can also be leveraged to identify additional perturbation experiments needed to distinguish between a set of possible candidate networks. The development of methodologies that permit the accurate prediction of connectivity in dysregulated pathways may enable more rational determination of what therapy is best for a patient.

Linear proximity of cancer causing and metabolic genes in the genome -does it drive metabolic reprogramming via somatic copy number changes?

Reprogramming of metabolism is an emerging hallmark of cancer. Metabolic genes (MG) have been identified as oncogenes (OG) and tumor suppressor genes (TSG) or targets of oncogenic signaling. Cancer is a direct consequence of genomic aberrations, such as somatic copy number alterations (SCNA) that frequently occur across many cancer types affecting not only OG and TSG, bur also multiple passenger and "potential" co-driver genes at the perturbed loci. l will present our recent work on elucidating how linear proximity of MG and cancer causing genes (CG) in the chromosomes can lead to metabolic remodeling. We have developed the analysis pipeline Identification of Metabolic Cancer Genes (iMetCG) to interrogate such events by integrating data for 19 different cancer types from TCGA that led to the identification of novel metabolic cancer genes.

Cancer cell metabolism and it’s impact on the tumour microenvironment

Targeting metabolic pathways in malignant tumours shows increasing promise as an effective therapeutic strategy in clinical oncology. Thus, unravelling details of metabolic pathways used by cancer cells, particularly those pathways that are differentially activated or suppressed in tumours, is of much current interest. In 1997, Helmlinger et al published “in-vivo” experimental results of pH and pO2 levels as functions of distance from a single blood vessel, on the micrometer scale. We show how these results provide unique insights into cancer cell metabolism when combined with an appropriate mathematical model.

Evaluating glutamine addiction in gliomas using PET imaging

Cancer cells commonly undergo metabolic reprograming enabling increased nutrient use to fuel their growth and proliferation. The Warburg effect is the classic example wherein tumors exhibit enhanced glucose uptake and metabolism through aerobic glycolysis. This increase in glucose uptake can be evaluated in vivo using positron emission tomography (PET) imaging with the glucose analogue 18F-fluorodeoxyglucose (18F-FDG). 18F-FDG PET imaging is a valuable clinical tool and is routinely used in diagnosing, grading and staging cancers. However, 18F-FDG is of limited value in evaluating gliomas in vivo due to high background glucose metabolism in the normal brain resulting in suboptimal tumor delineation. Glutamine is the most abundant amino acid in the plasma and many cancers are addicted to glutamine for their survival. We have recently developed 4-18F-(2S,4R)-fluoroglutamine (18F-FGln) for PET imaging in vivo. We evaluated glutamine uptake using PET imaging with 18F-FGln in vivo in glioma animal models to demonstrate that 18F-FGln showed high uptake in gliomas but minimal uptake in the normal brain, enabling clear tumor visualization. We translated these findings to human glioma subjects where 18F-FGln showed high tumor/background ratios in human glioma patients with progressive disease in contrast to that observed with 18F-FDG. These data suggest that 18F-FGln is specifically taken up by gliomas, can be used to assess metabolic nutrient uptake in gliomas in vivo and may serve as a valuable tool in the clinical management of gliomas.

Landscape and Flux of Cell Cycle and Cancer

Understanding the mechanisms of the cell cycle remains challenging. The cell cycle is regulated by the underlying generegulatory networks. We uncovered the underlying Mexican hat landscape of a mammalian cell cycle network. Three local basins of attraction along the cell cycle loop emerge, corresponding to three distinct cell cycle states: the G1, S/G2, and M phases. Two barriers along the loop characterize G1 and S/G2 checkpoints, respectively, of the cell cycle, which provide a physical explanation for cell cycle checkpoint mechanisms. The cell cycle is determined by two driving forces: curl flux and potential barriers. We uncovered the key gene regulations determining the progression of cell cycle, which can be used to guide the design of new anticancer tactics.

Cancer is a disease regulated by the underlying gene networks. The emergence of normal and cancer states as well as the transformation between them can be thought of as a result of the gene network interactions and associated changes. We developed a global potential landscape and path framework to quantify cancer and associated processes. We constructed a cancer gene regulatory network based on the experimental evidences and uncovered the underlying landscape. The resulting tristable landscape characterizes important biological states: normal, cancer and apoptosis. The landscape topography in terms of barrier heights between stable state attractors quantifies the global stability of the cancer network system. We propose two mechanisms of cancerization: one is by the changes of landscape topography through the changes in regulation strengths of the gene networks. The other is by the fluctuations that help the system to go over the critical barrier at fixed landscape topography. The kinetic paths from least action principle quantify the transition processes among normal state, cancer state and apoptosis state. The kinetic rates provide the quantification of transition speeds among normal, cancer and apoptosis attractors. By the global sensitivity analysis of the gene network parameters on the landscape topography, we uncovered some key gene regulations determining the transitions between cancer and normal states. This can be used to guide the design of new anti-cancer tactics, through cocktail strategy of targeting multiple key regulation links simultaneously, for preventing cancer occurrence or transforming the early cancer state back to normal state.

Inferring signaling crosstalk from cellular heterogeneity

Cancer cell populations can be highly heterogeneous in their signaling phenotypes. This heterogeneity is often viewed as an impediment to understanding how information flows within cells. However, we have found recently that cell-to-cell variability can actually be used to infer network topology.

Quantitative Imaging to Drive Biophysical Models of Tumor Growth

The ability to identify—early in the course of therapy—patients that are not responding to a given therapeutic regimen is highly significant. In addition to limiting patients’ exposure to the toxicities associated with unsuccessful therapies, it would allow patients the opportunity to switch to a potentially more efficacious treatment. In this presentation, we will discuss ongoing efforts at using data available from advanced imaging technologies to initialize and constrain predictive biophysical and biomathematical models of tumor growth and treatment response.