Workshop 2: Signal transduction and gene regulatory networks

(November 2,2009 - November 6,2009 )

Organizers


Andre Levchenko
Institute for Computation Medicine, Johns Hopkins University

nformation about the environment, which organisms collect by membrane receptors, is processed by a complex network of signaling reactions to generate appropriate responses in terms of gene expression, development and differentiation, motility, cell growth and division, and programmed cell death. To survive and exist in harmony with its environment, the cell has to arrive at responses that are robust, specific and consistent with its role in a cell ensemble. The information processing system is replete with nonlinear interactions, which create bistable switches, signal relaying, adaptation, limit cycle oscillations, and other exotic responses. The purpose of this workshop is to survey recent advances in our understanding of the signal-response characteristics of living cells, and to foster deeper and more fruitful collaborations between theorists and experimentalists.

The application of sophisticated methods of biochemistry and molecular genetics in a variety of experimentally convenient organisms - budding yeast (Saccharomyces), fruit flies (Drosophila), green plants (Arabidopsis), nematodes (Caenorhabditis), and mammals (mice and men) - have provided many clues about the molecular mechanisms underlying signal processing and response regulation. Experimental studies of in vitro chemical and biochemical reaction networks have shown surprisingly similar dynamic behaviors, such as excitability, oscillations, multiple steady states, and signal propagation. During recent years mathematical models, based on realistic biochemistry and biophysics, have delivered useful insights into the dynamical principles underlying information processing by switches and clocks in living organisms. In addition, theoretical models have drawn attention to unexpected properties, such as hysteresis and critical slowing-down, which can be tested in the laboratory.

Mathematical analysis of large-scale transcriptome and interactome maps use graph theory, discrete mathematics, dynamical systems and signal processing theory, and elements of statistical mechanics. Modeling the dynamics of gene-protein regulatory networks involves all the tools from nonlinear dynamical systems theory: bifurcations of vector fields, numerical simulation, parameter estimation, hybrid systems (continuous-discrete, and deterministic-stochastic), sensitivity analysis, robust design, and multi-scale modeling. In most situations, new approaches are needed to adapt tools developed for engineering applications (such as control theory) to life science problems. Of crucial importance are algorithms and software to enable modelers to build larger, more complex and realistic models of information processing in cells.

Accepted Speakers

Reka Albert
Department of Physics, Pennsylvania State University
Shuki Bruck
Caltech
Frederick Cross
Laboratory of Yeast Molecular Genetics, The Rockefeller University
Leah Edelstein-Keshet
Mathematics Department, UBC
Timothy Elston
Department of Pharmacology, University of North Carolina, Chapel Hill
Jason Haugh
Department of Chemical & Biomolecular Engineering, North Carolina State University
Liwu Li
Laboratory of Innate Immunity and Inflammation; Department of Biology, Virginia Polytechnic Institute and State University
James Liao
Department of Chemical and Biomolecular Engineering, University of California, Los Angeles
Ilya Nemenman
Departments of Physics and Biology, Emory University
Zoltán Oltvai
Department of Pathology, School of Medicine, University of Pittsburgh
Joe Pomerening
Department of Biology, Indiana University
Karen Sachs
School of Medicine, Stanford University
Guy Shinar
Department of Molecular Cell Biology, Weizmann Institute of Science
Stanislav Shvartsman
Lewis-Sigler Institute for Integrative Genomics, Princeton University
Victor Sourjik
ZMBH, University of Heidelberg
John Tyson
Computational Cell Biology, Virginia Polytechnic Institute and State University
Leor Weinberger
Dept of Chemistry and Biochemistry, UCSD
Monday, November 2, 2009
Time Session
10:30 AM
11:30 AM
Jason Haugh - Dynamic Regulation of the PDGF Receptor Signaling Network

Historically, intracellular signal transduction has been characterized in terms of linear pathways, exemplified by the canonical mitogen-activated protein kinase cascades; e.g., the Ras -> Raf -> MEK -> extracellular signal-regulated kinase (ERK) pathway in mammals. Our conceptual understanding of signal transduction networks now includes more complex interactions, including those between the classically defined pathways (crosstalk) and those responsible for feedback regulation or reinforcement; however, little has been done to move beyond hand-waving models of signaling networks to systematically quantify the relative magnitudes of classical, crosstalk, and feedback interactions.


Through quantitative measurements and computational modeling, we recently characterized crosstalk mechanisms in the platelet-derived growth factor (PDGF) receptor signaling network, in which phosphoinositide 3-kinase (PI3K) and Ras/ERK pathways are prominently activated [Wang C-C, Cirit M, Haugh JM. PI3K-dependent crosstalk interactions converge with Ras as quantifiable inputs integrated by Erk. Mol Syst Biol, 5: 246 (2009)]. Unique in its coverage of time, dose, and molecular perturbation conditions, our data set was comprised of >3,000 biochemical measurements, yielding > 150 processed data points that were used to constrain the accompanying model.


We have since refined this approach with additional measurements that push even further the boundary of data-driven kinetic modeling. With nearly double the number of data constraints, we have identified and parsed four distinct modes of negative regulation affecting ERK signaling and pinned down with even greater precision the magnitude of crosstalk from PI3K-dependent signaling to the Ras/ERK pathway. We further demonstrate that the current model is a predictive tool that successfully forecasts outcomes of experiments that perturb the feedback structure of the network. The goal now is to map the finer, molecular-level details (which have yet to be measured quantitatively) onto the dynamic, system-level properties that we have characterized.

01:30 PM
02:30 PM
Liwu Li - Positive and Negative Molecular Signaling Networks Controlling the Fate of Macrophages

Macrophages have built-in negative and positive regulatory loops that finely control the expression of pro- and anti-inflammatory genes. In particular, our laboratory has revealed that both feed forward and feedback controls exist in macrophages that regulate NFkB-mediated expression of pro-inflammatory cytokines, as well as nuclear-receptor (NR) mediated expression of anti-inflammatory genes. Furthermore, the cross-inhibition of NFkB pathway and NR pathway could potentially give rise to two bi-stable anti- and pro-inflammatory states. Knocking-out of several key molecular players can skew the bi-stable state to the direction of anti-inflammatory flavor, and serve as viable targets for the development of anti-inflammatory therapies.

03:00 PM
04:00 PM
Timothy Elston - A systems biology analysis of yeast chemotrophic growth

An important property of Saccharomyces cerevisiae (yeast) is their ability to propagate as haploids. Haploid *a*- and /a/-cells secrete type-specific pheromones that promote cell fusion and the formation of an *a*//a/ diploid. Pheromone stimulation leads to a well-defined series of events required for mating, including readily-assayed responses, such as MAPK phosphorylation, new gene transcription and morphological changes. In particular, *a*-cells undergo chemotrophic growth in which they elongate in the direction of increasing pheromone concentration. Thus yeast is an attractive model system for studying cell differentiation and gradient sensing. We present recent computational and experimental investigations designed to elucidate the signaling events that lead to chemotrophic growth.

04:00 PM
05:00 PM
Victor Sourjik - Noise, robustness and memory in bacterial chemotaxis

Motile bacteria navigate in chemical gradients by performing temporal comparisons of ligand concentrations. In the adapted state with no gradients present, cells perform a random walk that consists of runs interrupted by short tumbles, which allows them to efficiently explore their environment. Such random work is ensured by a precise adjustment of the tumble signal, intracellular phosphorylation level of the response regulator CheY, to the sensitive range of flagellar motor. In presence of a gradient, the random walk becomes biased: an increase in attractant concentration - as experienced by cells swimming up the gradient - rapidly suppresses tumbles and thus results in longer runs in the favourable direction. This initial response is counteracted on a longer time scale by an adaptation system that regulates pathway activity through chemoreceptor methylation. Difference in the time scales of initial response and subsequent adaptation allows a swimming cell to compare concentrations at different points in the gradient.


Chemotactic performance of bacteria is affected by several types of noise, from stochastic ligand binding to Brownian motion to stochastic protein expression, and much of the pathway evolution appears to have been driven by the selection for robust signal processing under these conditions. We combined experiments, bioinformatics and computer modelling to investigate effects of the most prominent type of noise, stochastic variations in the levels of chemotaxis proteins in a population. We showed that such gene expression noise is compensated both by the robust pathway topology and by the chromosomal organization of chemotaxis genes. At the same time, the pathway also appears to utilize noise in the expression of adaptation enzymes to broaden the range of environmental gradients that a chemotactic population as a whole can follow.

Tuesday, November 3, 2009
Time Session
09:00 AM
10:00 PM
10:30 AM
11:30 AM
Zoltán Oltvai - An integrated approach for drug development and customized therapy

Integration of advances in genome sequencing and analysis, network biology, structural biology and computational chemistry may have the potential to revolutionize drug discovery and may allow customization of drug therapy. Here we describe an initial example for this potential using bacterial infections as a case study.

01:30 PM
02:30 PM
Leor Weinberger - An Endogenous Gene Expression Level-to-Rate Converter Provides a Fitness Advantage

Signal transduction circuits have long been known to differentiate between signals by amplifying inputs to different levels1. Here, we describe a novel transcriptional circuitry that dynamically converts greater input levels into faster rates, without increasing the final equilibrium level (i.e. a level-to-rate converter circuit). We utilize time-lapse microscopy to study human herpesvirus (cytomegalovirus) infection of live cells in real time. Strikingly, our results show that transcriptional activators accelerate viral gene expression in single cells without amplifying the steady-state levels in these cells. This level-to-rate conversion operates by dynamically manipulating the traditional 'gain-bandwidth' feedback relationship from electrical circuit theory2 to convert greater input levels into faster rates, without increasing the final equilibrium level. Combining experimental approaches with mathematical modeling, we show that level-to-rate conversion results from a highly self-cooperative transcriptional auto-regulatory loop encoded by the virus's essential transcriptional transactivator, IE23. There is a significant fitness advantage provided by level-to-rate conversion and abrogating IE2 auto-regulation eliminates level-to-rate conversion and severely impairs viral replication. Even minimal IE2 feedback circuits, lacking all other viral elements, maintain this fitness advantage via level-to-rate conversion. In general, level-to-rate converters may provide a mechanism for signal transduction circuits to rapidly respond to, and discriminate between, a diversity of signals without increasing steady-state levels of potentially cytotoxic molecules.

03:00 PM
04:00 PM
Guy Shinar - Structural sources of robustness in biochemical reaction networks

In consideration of biological design principles, it is now generally recognized that a central role must be played bysystem robustness - that is, by the capacity for sustained and precise function even in the presence of environmental disruption. Lacking, however, is a clear picture of common network features that otherwise-different biochemical modules might incorporate to ensure the robustness required. Our general interest is in what we call absolute concentration-robustness (ACR): A biochemical system is said to exhibit ACR relative to an active molecular species if the concentration of that species is identical in every positive steady-state the system might admit. In this way, the function of an ACR-possessing system can be protected even against large changes in the overall supply of the system's components - changes that might arise from cell-to-cell variability or from variations in the same cell over time. Here, mathematics and chemistry come together to identify quite subtle structural attributes that will impart ACR to any mass action network possessing them. For example, these core network features provide a common source for the strong concentration robustness observed experimentally in the markedly-different E. coliEnvZ/OmpR osmoregulation and IDHKP/IDH glyoxylate-bypass-control systems. We believe that the same structural foundation will undergird a large variety of biochemical networks for which strong concentration robustness is essential.

04:00 PM
05:00 PM
Christian Darabos - Are cells really operating at the edge of chaos? A case study of two real-life regulatory networks

Taking into account recent years' advances in the field of cellular biology, we have proposed to identify under what conditions Kauffmann's hypothesis that living organism cells operate in a region bordering order and chaos holds. This property confers to living organisms both the stability to resist transcriptional errors and external disruptions, and, at the same time, the flexibility necessary to evolution. We studied two particular cases of genetic regulatory networks found in literature in terms of complex dynamical systems derived from the original RBN model. In order to do that, we compared the behavior of these systems under the original update function and the novel additive function that we believe is closer to the actual role of living organisms. We successfully identify contexts in which our model's response can be interpreted as critical, thus most biologically plausible. Results of numerical simulations show that there exist values in both update functions that allow the models to operate in the critical region, and that these values are comparable in two different real-life GRNs.

04:00 PM
05:00 PM
Jaewook Joo - Stochastic and heterogeneous dynamical response of NF-kB upon Lipopolysaccharide insult to live macrophages

The kinetics and key controlling components of the Toll-Like Receptor 4(TLR4)-mediated innate immune response to infectious stimuli are poorly understood. Using computational modeling and live cell imaging, we investigated how different Lipopolysaccharide (LPS) dosage levels elicit different immune responses in individual immune cells. Due to the complexity of the TLR4 signaling pathways, our study was focused on the LPS-induced nucleo-cytoplasmic translocation dynamics of NF-kB, one of endpoint proteins in the TLR4 signaling pathways. An integrative approach of computational modeling and time-lapse fluorescence microscopy was employed to elucidate the kinetic mechanisms of NF-kB translocation dynamics in single cells. We built a stochastic model of NF-kB signaling pathway tightly regulated by multiple negative and positive feedback loops and stably constructed a green fluorescence reporter of RelA (a subunit of heterodimeric NF-kB) into murine macrophages for real time monitoring of the nulceo-cytoplasmic translocation of NF-kB in individual live cells. Computationally, we predicted that the NF-kB nucleo-cytoplasmic translocation would be oscillatory in LPS-stimulated individual cells, mainly due to intrinsic stochasticity in the circuitry of NF-κB signaling pathway. Our second prediction was that the extrinsic noise-originated cell-to-cell variability, modeled as the different kinetic conditions of the individual cells prior to the LPS stimulation, would diversify the shuttling patterns of NF-kB. Both of our model predictions were experimentally validated: Upon high LPS dosage stimulation, NF-kB translocation dynamics were predominantly oscillatory among the cells while upon low LPS dosage stimulation, NF-kB shuttling patterns were highly heterogeneous. While the biological functionality of NF-kB oscillatory shuttling remains to be proven, this present systems biology study of LPS-induced NF-kB dynamics revealed us the highly stochastic and heterogeneous/individualist nature of the immune response in single cells.

Wednesday, November 4, 2009
Time Session
09:00 AM
10:00 AM
Reka Albert - Discrete dynamic modeling of signal transduction networks: Survival signaling in T-LGL leukemia

Modeling the dynamics of complex biological systems is challenging even when well-established biochemical frameworks are applicable. In the case of regulatory and signaling systems that include heterogeneous components and interactions, and/or are sparsely documented in terms of quantitative information, modeling is often thought impossible. This talk will argue for the usefulness of a discrete dynamic framework in incorporating qualitative interaction information into a predictive model. I will focus on a model of the signaling network responsible for the survival and long-term competence of cytotoxic T cells in the blood cancer T-LGL leukemia. Our model suggests that the persistence of IL-15 and PDGF is sufficient to reproduce all known deregulations in leukemic T-LGL. It also predicts the key nodes whose (in)activity is necessary to induce the apoptosis of T cells and reverse the disease. We experimentally validated several of these predictions. The model will be useful in identifying potential therapeutic targets for T-LGL leukemia and generating long-term competent CTL necessary for tumor and cancer vaccine development. The success of this and other similar models indicates that network-based discrete dynamic modeling is a promising framework that allows system-level analysis and predictions that would not be possible using traditional methods.


Reference: R. Zhang, M. V. Shah, J. Yang, S. B. Nyland, X. Liu, J. K. Yun, R. Albert and T. P. Loughran, Jr., Network Model of Survival Signaling in LGL Leukemia, PNAS 105, 16308-16313 (2008).

03:00 PM
04:00 PM
Ilya Nemenman - Phenomenological models of regulatory networks

Even the simplest biochemical networks often have more degrees of freedoms than one can (or should!) analyze. Can we ever hope to do the physicists' favorite trick of coarse-graining, simplifying the networks to a much smaller set of effective dynamical variables that still capture the relevant aspects of the kinetics? I will argue then that methods of statistical physics and statistical model selection provide hints at the existence of rigorous coarse-grained methodologies in modeling biological information processing systems, allowing to identify features of the systems that are relevant to their functions. While a general solution is still far away, I will focus on two specific examples illustrating the two approaches. First, for a general stochastic network exhibiting the kinetic proofreading behavior, I will show that the microscopic parameters of the system are largely important only to the extent that they contribute to a single aggregate parameter, the mean first passage time through the network. Thus a phenomenological model with a single parameter does a good job explaining all of the observable data generated by this complex system. Second, building an "as simple as possible, but not simpler" model of heat avoidance response of C. elegans, we show that a phenomenological model with a single hidden "memory" node is capable of reproducing all of the observed data, hinting strongly that the worm's thermotaxis behavior resembles that of a chemotaxing E. coli.

04:00 PM
05:00 PM
- Patterns of Oscillations in Coupled Systems

A coupled system is a network of interacting dynamical systems. In this talk we focus on networks where all nodes represent identical systems of differential equations and we discuss only time periodic solutions. For these networks we discuss those features of solutions that are the product of network architecture?



 
Thursday, November 5, 2009
Time Session
09:00 AM
10:00 AM
Leah Edelstein-Keshet - Modeling cell polarity and motility: signaling to actin

Remodeling of the actin cytoskeleton is recognized to be an important process underlying eukaryotic cell motility. However, regulation of the spatio-temporal dynamics of actin is essential in order for the cell to orient and move correctly in response to chemoattractive stimuli. Here I will survey efforts in my group over the last years to understand this process. We show that a module of switch-like proteins (Rho GTPases) can set up robust cell polarity, leading to increased actin nucleation (via Arp2/3) at a cell "front" and increased contraction at the opposite pole ("rear"). A combination of 2D cell motility simulations and analytic treatment of reduced versions of the mathematical model lead to insights about the underlying mechanism. We also study how a membrane lipid module (phosphoinositides) interacts in the signaling network, and how this can fine-tune the response, eliminating confusion due to multiple conflicting stimuli. This talk represents work joint with Adriana Dawes, Alexandra Jilkine, Stan Maree, Yoichiro Mori, Veronica Grieneisen, and Ben Vanderlei.

01:30 PM
02:30 PM
John Tyson - Stochastic Models of Cell Cycle Regulation in Eukaryotes

The DNA replication-division cycle in eukaryotic cells is controlled by a complex network of regulatory proteins (called cyclin-dependent kinases, Cdk's) and their activators and inhibitors. A comprehensive deterministic model of Cdk regulation in budding yeast is available (Chen et al., 2004) that accurately accounts for the average phenotypic properties of wild-type cells and 150+ mutant strains. However, the deterministic model cannot account for the considerable variabilities in cell cycle properties that have been observed among genetically identical cells. These variabilities are due in large part to small numbers of molecules in yeast cells: 100's - 1000's of molecules of each specific protein and only 10's of molecules of each specific mRNA species per yeast cell. How can the cell cycle function reliably in the face of the large intrinsic molecular fluctuations implied by such numbers? We have addressed this question by constructing a realistic model (on the scale: toy < realistic < comprehensive) of Cdk regulation in budding yeast that is suitable for exact stochastic simulation by Gillepie's algorithm. The results of this model compare favorably to the extensive statistical properties of budding yeast cell cycle progression collected recently in Fred Cross's laboratory (Di Talia et al., 2007; Skotheim et al., 2008; Di Talia et al., 2009).



  • Chen et al. (2004) Mol Biol Cell 15:3841.

  • Di Talia et al. (2007) Nature 448:947.

  • Di Talia et al. (2009) PLoS Biology, in press.

  • Skotheim et al. (2008) Nature 454:291.

03:00 PM
04:30 PM
Richard Yamada - Molecular Noise Enhances Oscillations in the Supra-Chiasmatic Nuclei Network

In this talk, we will discuss a detailed mathematical model for circadian timekeeping within the SCN. Our proposed model consists of a large population of SCN neurons, with each neuron containing a network of biochemical reactions involving the core circadian components. Using mathematical modeling, our results show that both intracellular molecular noise and intercellular coupling (nonlinear in nature) are required to sustain stochastic oscillations in the SCN oscillator network. Our work focuses on the problem of overcoming noise in oscillator systems, and our results highlight the importance of transcriptional noise in enhancing oscillations rather than dampening them. Surprisingly, our predictions from our model have been confirmed experimentally; we conclude with a short discussion of these results.

03:00 PM
04:30 PM
Shinya Kuroda - Temporal coding of Akt signaling networks

In cellular signal transduction, information in external stimulus is coded as temporal patterns of signaling activities; however, temporal coding mechanism has been poorly investigated. Here we show how the Akt pathway, involved in cell growth, serves as low-pass filters and specifically transfers temporal information of upstream signals to downstream. We modeled the epidermal growth factor (EGF)-dependent Akt pathway in PC12 cells based on experimental results. We found counterintuitive results that peak amplitudes of receptor and downstream phosphorylation are decoupled; weak sustained EGF receptor phosphorylation, rather than strong transient phosphorylation, strongly induced S6 phosphorylation, a downstream molecule of Akt. By use of frequency response analysis, we found that the Akt pathway exhibits low-pass filter characteristics, and that this characteristic of the Akt pathway can explain the decoupling effect of peak amplitudes between receptor and downstream phosphorylation. Because low-pass filter characteristic is an intrinsic feature of biochemical reactions, our finding raises a caution in interpreting biological data without temporal information.

03:00 PM
04:30 PM
Attila Csikasz-Nagy - Role of protein removal in signaling

N/A

03:00 PM
04:30 PM
- Dynamic Simulations of Single-Molecule Enzyme Networks

Along with the growth of technologies allowing accurate visualization of biochemical reactions to the scale of individual molecules has arisen an appreciation of the role of statistical fluctuations in intracellular biochemistry. The stochastic nature of metabolism can no longer be ignored. It can be probed empirically, and theoretical studies have established its importance. Traditional methods for modeling stochastic biochemistry are derived from an elegant and physically satisfying theory developed by Gillespie. However, although Gillespie's algorithm and its derivatives efficiently model small-scale systems, complex networks are harder to manage on easily available computer systems. Here we present a novel method of simulating stochastic biochemical networks using discrete events simulation techniques borrowed from manufacturing production systems. The method is very general and can be mapped to an arbitrarily complex network. As an illustration, we apply the technique to the glucose phosphorylation steps of the Embden-Meyerhof-Parnas pathway in E. coli. We show that a deterministic version of the discrete event simulation reproduces the behavior of an analogous deterministic differential equation model. The stochastic version of the same model predicts that catastrophic bottlenecks in the system are more likely than one would expect from deterministic theory.

Friday, November 6, 2009
Time Session
10:30 AM
11:30 AM
Joe Pomerening - Elucidating the Architecture of the CDK1-APC Oscillator

Computational and experimental studies together have yielded a compendium of insights into the signal transduction involved in eukaryotic cell cycle regulation. Our present work aims to describe the biochemical mechanisms that underlie mitotic progression, while also uncovering the molecular basis of the developmental transitions that accompany early embryogenesis. Some of these studies are focused upon understanding the dynamical behavior of the (cyclin-dependent kinase 1) CDK1 - (anaphase-promoting complex) APC oscillator - the driving force behind the rapid and unimpeded cleavages that occur prior to the midblastula transition (MBT) in the early embryo of Xenopus laevis. While protein synthesis, proteolysis, and phosphorylation-dephosphorylation events drive this system in general, it remains unclear how these inputs together confer the overall output of this oscillator. Indeed, current mathematical models do not reproduce all of the dynamic features observed during a CDK1 activity oscillation. A closer look at the Wee1-Cdc25-CDK1 module hints at the possible regulation by players that are involved in other aspects of mitotic control, and recent evidence has confirmed relationships between these regulators. Might the involvement of other M-phase kinases in the activation of CDK1 serve to tune the output of this kinase as a function of cyclin stimulus? To answer this question, we have initiated a systematic analysis of the activities of M-phase kinases in relation to the pattern of cyclin stimulus and CDK1 activity in Xenopus egg extracts. Our overall goal is to map and dissect experimentally the connections of the embryonic M-phase activation network, and to gather and apply these quantitative details towards the refinement of our mathematical model of the CDK1-APC oscillatory system.

Name Affiliation
Laomettachit, Teeraphan laomett@vt.edu Biological Sciences, Virginia Polytechnic Institute and State University
Albert, Reka reka.albert@gmail.com Department of Physics, Pennsylvania State University
Assmann, Sarah sma3@psu.edu Biology Department, Penn State University
Balázsi, Gábor gbalazsi@mdanderson.org Department of Systems Biology, The University of Texas M. D. Anderson Cancer Center
Barik, Debashis dbarik@vt.edu Department of Biological Sciences, Virginia Polytechnic Institute and State University
Bruck, Shuki bruck@caltech.edu Caltech
Bundschuh, Ralf bundschuh@mbi.osu.edu Department of Physics, The Ohio State University
Campbell, Colin cec220@psu.edu Physics, Penn State University
Carson, Matthew mcarso2@uic.edu Bioengineering/Bioinformatics, University Of Illinois at Chicago
Chamberlin, Helen chamberlin.27@osu.edu Molecular Genetics, The Ohio State University
Chen, Chun river6@gmail.com GBCB, virginia tech
Chou, Ching-Shan chou@math.ohio-state.edu Mathematics, The Ohio State University
Cross, Frederick fcross@rockefeller.edu Laboratory of Yeast Molecular Genetics, The Rockefeller University
Csikasz-Nagy, Attila csikasz@cosbi.eu Center for Computation and Systems Biology, University of Trento
Darabos, Christian christian.darabos@unil.ch Information Systems Institute, Faculty of Business and Econo, University of Lausanne, Switzerland
Edelstein-Keshet, Leah keshet@math.ubc.ca Mathematics Department, UBC
Elston, Timothy telston@amath.unc.edu Department of Pharmacology, University of North Carolina, Chapel Hill
Fan, Yue yue@bu.edu Department of Mathematics and Statistics, Boston University
Feinberg, Martin feinberg.14@osu.edu Chemical Engineering and Mathematics, The Ohio State University
Foster, Mark foster.281@osu.edu Dept. of Biochemistry, Ohio State University
Fu, Yan fuyan@vt.edu Genetics, Bioinformatics, and Computational Biology Program, Virginia Polytechnic Institute and State University
Ganju, Ramesh ramesh.ganju@osumc.edu Pathology, The Ohio State University
Hancioglu, Baris hbaris@vt.edu Bilogical Sciences, Virginia Polytechnic Institute and State University
Haugh, Jason jason_haugh@ncsu.edu Department of Chemical & Biomolecular Engineering, North Carolina State University
Herman, Paul herman.81@osu.edu Molecular Genetics, The Ohio State University
Hong, Tian hongtian@vt.edu Department of Biological Sciences, Virginia Tech
Hopper, Jim hopper.65@osu.edu Dept. of Biochemistry/Molecular Genetics, The Ohio State University
Igoshin, Oleg igoshin@rice.edu Bioengineering, Rice University
Jayaprakash, Ciriyam jay@mps.ohio-state.edu Physics, The Ohio State University
Jilkine , Alexandra jilkine@math.ubc.ca Department of Mathematics, University of British Columbia
Jin, Ruoming jinr@cse.ohio-state.edu Computer Science, Kent State University
Johnson, Eric johnson.3347@osu.edu Dept. of Biochemistry, The Ohio State University
Joo, Jaewook jjoo1@utk.edu Physics/NIMBioS, University of Tennessee
Kar, Sandip skar@vt.edu Department of Biological Sciences, Virginia Tech polytechnic and state university
Kim, Sohyoung National Cancer Institute, National Institute of Health
Kleckner, Ian kleckner.5@osu.edu Biophysics Program, The Ohio State University
Kon, Mark mkon@bu.edu Dept. of Math and Statistics, Boston University
Kuroda, Shinya skuroda@bi.s.u-tokyo.ac.jp Department of Biophysics and Biochemistry, University of Tokyo
Levchenko, Andre alev@jhu.edu Institute for Computation Medicine, Johns Hopkins University
Li , Liwu lwli@vt.edu Laboratory of Innate Immunity and Inflammation; Department of Biology, Virginia Polytechnic Institute and State University
Li, Xiaofan lix@iit.edu Applied Mathematics, Illinois Institute of Technology
Li, Yongfeng yonli@ima.umn.edu IMA, University of Minnesota
Liao, James liaoj@ucla.edu Department of Chemical and Biomolecular Engineering, University of California, Los Angeles
Liu, Yang ya.liu@neu.edu Center for Complex Network Research, Northeastern University
Lu, James james.lu@oeaw.ac.at Mathematical Methods in Molecular & Systems Biology, RICAM
Mao, Yi maoyi0@gmail.com NIMBIOS, University of Tennessee
Meier, Iris meier.56@osu.edu MG/PCMB, The Ohio State University
Mukherjee, Sayak sayak.mukherjee@nationwidechildrens.org BCMM, Nationwide Childrens Hospital
Nagy, John john.nagy@sccmail.maricopa.edu Life Sciences, Scottsdale Community College
Nemenman, Ilya nemenman@physics.emory.edu Departments of Physics and Biology, Emory University
Oltvai, Zoltán oltvai@pitt.edu Department of Pathology, School of Medicine, University of Pittsburgh
Park, Hay-Oak park.294@osu.edu Molecular Genetics, The Ohio State University
Pomerening, Joe jpomeren@indiana.edu Department of Biology, Indiana University
Prasad , Ashok ashokp@engr.colostate.edu Chemical and Biological Engineering, Colorado State University
Rabello, Sabrina sabrina.rabello@gmail.com Physics, Northeastern University
Ramachandran, Vidhya ramachandran.23@buckeyemail.osu.edu Molecular Genetics, The Ohio State University
Ravi, Janani janani@vt.edu Biological Sciences, Virginia Tech
Saadatpour, Assieh axs1022@psu.edu Mathematics, Pennsylvania State University
Sachs , Karen karens1@stanford.edu School of Medicine, Stanford University
Salama, Samir salama.3@buckeyemail.osu.edu Radiology, The Ohio State University
Santos, Silvia Chemical and Systems Biology, Stanford School of Medicine
Satchell, Shalanda shalanda.satchell@vanderbilt.edu Biology, Fisk University
Shinar, Guy guy.shinar@weizmann.ac.il Department of Molecular Cell Biology, Weizmann Institute of Science
Shvartsman, Stanislav stas@princeton.edu Lewis-Sigler Institute for Integrative Genomics, Princeton University
Singhania, Rajat rajats@vt.edu Biological Sciences, Virginia Polytechnic Institute and State University
Somers, David somers.24@osu.edu PCMB, The Ohio State University
Sourjik, Victor v.sourjik@zmbh.uni-heidelberg.de ZMBH, University of Heidelberg
Stites, Edward edstites@virginia.edu School of Medicine, University of Virginia
Subramanian, Kartik skartik@vt.edu Biological Sciences, Virginia Polytechnic Institute and State University
Sun, Zhongyao zxs130@psu.edu Dept. of Physics, Pennsylvania State University
Tavassoly, Iman tavassoly@vt.edu Genetics, Bioinformatics and Computational Biology Program, Virginia Polytechnic Institute and State University
Thomas, Peter pjthomas@cwru.edu Department of Mathematics, Case Western Reserve University
Tyson, John tyson@vt.edu Computational Cell Biology, Virginia Polytechnic Institute and State University
Van Leeuwen, Ingeborg ingeborg.vanleeuwen.ki.se Dept. of Microbiology, Tumor and Cell Biology, Karolinska Institute
Van Zwieten, Dirk d.a.j.v.zwieten@student.tue.nl Systems Engineering, Technical University of Eindhoven
Verdugo, Anael verdugo@vt.edu Biological Sciences, Virginia Polytechnic Institute and State University
Wang, Ruisheng rxw34@psu.edu Department of Physics, Pennsylvania State University
Weinberger, Leor lsw@ucsd.edu Dept of Chemistry and Biochemistry, UCSD
Wu, Zhanghan zwu07@vt.edu Biological Scicences/ GBCB, Virginia Polytechnic Institute and State University
Xing, Jianhua jxing@vt.edu Biological Sciences, Virginia Tech
Yamada, Richard yryamada@umich.edu Mathematics, University of Michigan
Yeh, Yuh-Ying yeh.96@osu.edu Molecular Genetics, The Ohio State University
You, Yuncheng you@math.usf.edu Mathematics and Statistics, University of South Florida
Young, Jon JonYoung84@gmail.com Mathematics and Statistics, Arizona State University
Discrete dynamic modeling of signal transduction networks: Survival signaling in T-LGL leukemia

Modeling the dynamics of complex biological systems is challenging even when well-established biochemical frameworks are applicable. In the case of regulatory and signaling systems that include heterogeneous components and interactions, and/or are sparsely documented in terms of quantitative information, modeling is often thought impossible. This talk will argue for the usefulness of a discrete dynamic framework in incorporating qualitative interaction information into a predictive model. I will focus on a model of the signaling network responsible for the survival and long-term competence of cytotoxic T cells in the blood cancer T-LGL leukemia. Our model suggests that the persistence of IL-15 and PDGF is sufficient to reproduce all known deregulations in leukemic T-LGL. It also predicts the key nodes whose (in)activity is necessary to induce the apoptosis of T cells and reverse the disease. We experimentally validated several of these predictions. The model will be useful in identifying potential therapeutic targets for T-LGL leukemia and generating long-term competent CTL necessary for tumor and cancer vaccine development. The success of this and other similar models indicates that network-based discrete dynamic modeling is a promising framework that allows system-level analysis and predictions that would not be possible using traditional methods.


Reference: R. Zhang, M. V. Shah, J. Yang, S. B. Nyland, X. Liu, J. K. Yun, R. Albert and T. P. Loughran, Jr., Network Model of Survival Signaling in LGL Leukemia, PNAS 105, 16308-16313 (2008).

Random Ideas About Biology

Why does the functioning of biological systems seem miraculous? One reason is that we do not know how to design systems that do what cells do, namely molecular computing. In contrast, we know how to design highly complex information systems. The fundamental reason for the successful evolution of information systems is the development of mathematical abstractions that enable efficient and robust design processes. In particular, Claude Shannon in his classical 1938 Master Thesis demonstrated that all Boolean functions can be computed by relay circuits, leading to the development of digital logic and resulting in computer chips with over a billion transistors. Motivated by the challenge of analyzing stochastic gene regulatory networks, we generalize the notion of logic design to probabilistic logic design. Specifically, we consider relay circuits where deterministic switches are replaced by probabilistic switches. We present efficient algorithms for synthesizing probabilistic relay circuits that can compute probability distributions.

Role of protein removal in signaling

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Are cells really operating at the edge of chaos? A case study of two real-life regulatory networks

Taking into account recent years' advances in the field of cellular biology, we have proposed to identify under what conditions Kauffmann's hypothesis that living organism cells operate in a region bordering order and chaos holds. This property confers to living organisms both the stability to resist transcriptional errors and external disruptions, and, at the same time, the flexibility necessary to evolution. We studied two particular cases of genetic regulatory networks found in literature in terms of complex dynamical systems derived from the original RBN model. In order to do that, we compared the behavior of these systems under the original update function and the novel additive function that we believe is closer to the actual role of living organisms. We successfully identify contexts in which our model's response can be interpreted as critical, thus most biologically plausible. Results of numerical simulations show that there exist values in both update functions that allow the models to operate in the critical region, and that these values are comparable in two different real-life GRNs.

Modeling cell polarity and motility: signaling to actin

Remodeling of the actin cytoskeleton is recognized to be an important process underlying eukaryotic cell motility. However, regulation of the spatio-temporal dynamics of actin is essential in order for the cell to orient and move correctly in response to chemoattractive stimuli. Here I will survey efforts in my group over the last years to understand this process. We show that a module of switch-like proteins (Rho GTPases) can set up robust cell polarity, leading to increased actin nucleation (via Arp2/3) at a cell "front" and increased contraction at the opposite pole ("rear"). A combination of 2D cell motility simulations and analytic treatment of reduced versions of the mathematical model lead to insights about the underlying mechanism. We also study how a membrane lipid module (phosphoinositides) interacts in the signaling network, and how this can fine-tune the response, eliminating confusion due to multiple conflicting stimuli. This talk represents work joint with Adriana Dawes, Alexandra Jilkine, Stan Maree, Yoichiro Mori, Veronica Grieneisen, and Ben Vanderlei.

A systems biology analysis of yeast chemotrophic growth

An important property of Saccharomyces cerevisiae (yeast) is their ability to propagate as haploids. Haploid *a*- and /a/-cells secrete type-specific pheromones that promote cell fusion and the formation of an *a*//a/ diploid. Pheromone stimulation leads to a well-defined series of events required for mating, including readily-assayed responses, such as MAPK phosphorylation, new gene transcription and morphological changes. In particular, *a*-cells undergo chemotrophic growth in which they elongate in the direction of increasing pheromone concentration. Thus yeast is an attractive model system for studying cell differentiation and gradient sensing. We present recent computational and experimental investigations designed to elucidate the signaling events that lead to chemotrophic growth.

Dynamic Regulation of the PDGF Receptor Signaling Network

Historically, intracellular signal transduction has been characterized in terms of linear pathways, exemplified by the canonical mitogen-activated protein kinase cascades; e.g., the Ras -> Raf -> MEK -> extracellular signal-regulated kinase (ERK) pathway in mammals. Our conceptual understanding of signal transduction networks now includes more complex interactions, including those between the classically defined pathways (crosstalk) and those responsible for feedback regulation or reinforcement; however, little has been done to move beyond hand-waving models of signaling networks to systematically quantify the relative magnitudes of classical, crosstalk, and feedback interactions.


Through quantitative measurements and computational modeling, we recently characterized crosstalk mechanisms in the platelet-derived growth factor (PDGF) receptor signaling network, in which phosphoinositide 3-kinase (PI3K) and Ras/ERK pathways are prominently activated [Wang C-C, Cirit M, Haugh JM. PI3K-dependent crosstalk interactions converge with Ras as quantifiable inputs integrated by Erk. Mol Syst Biol, 5: 246 (2009)]. Unique in its coverage of time, dose, and molecular perturbation conditions, our data set was comprised of >3,000 biochemical measurements, yielding > 150 processed data points that were used to constrain the accompanying model.


We have since refined this approach with additional measurements that push even further the boundary of data-driven kinetic modeling. With nearly double the number of data constraints, we have identified and parsed four distinct modes of negative regulation affecting ERK signaling and pinned down with even greater precision the magnitude of crosstalk from PI3K-dependent signaling to the Ras/ERK pathway. We further demonstrate that the current model is a predictive tool that successfully forecasts outcomes of experiments that perturb the feedback structure of the network. The goal now is to map the finer, molecular-level details (which have yet to be measured quantitatively) onto the dynamic, system-level properties that we have characterized.

Stochastic and heterogeneous dynamical response of NF-kB upon Lipopolysaccharide insult to live macrophages

The kinetics and key controlling components of the Toll-Like Receptor 4(TLR4)-mediated innate immune response to infectious stimuli are poorly understood. Using computational modeling and live cell imaging, we investigated how different Lipopolysaccharide (LPS) dosage levels elicit different immune responses in individual immune cells. Due to the complexity of the TLR4 signaling pathways, our study was focused on the LPS-induced nucleo-cytoplasmic translocation dynamics of NF-kB, one of endpoint proteins in the TLR4 signaling pathways. An integrative approach of computational modeling and time-lapse fluorescence microscopy was employed to elucidate the kinetic mechanisms of NF-kB translocation dynamics in single cells. We built a stochastic model of NF-kB signaling pathway tightly regulated by multiple negative and positive feedback loops and stably constructed a green fluorescence reporter of RelA (a subunit of heterodimeric NF-kB) into murine macrophages for real time monitoring of the nulceo-cytoplasmic translocation of NF-kB in individual live cells. Computationally, we predicted that the NF-kB nucleo-cytoplasmic translocation would be oscillatory in LPS-stimulated individual cells, mainly due to intrinsic stochasticity in the circuitry of NF-κB signaling pathway. Our second prediction was that the extrinsic noise-originated cell-to-cell variability, modeled as the different kinetic conditions of the individual cells prior to the LPS stimulation, would diversify the shuttling patterns of NF-kB. Both of our model predictions were experimentally validated: Upon high LPS dosage stimulation, NF-kB translocation dynamics were predominantly oscillatory among the cells while upon low LPS dosage stimulation, NF-kB shuttling patterns were highly heterogeneous. While the biological functionality of NF-kB oscillatory shuttling remains to be proven, this present systems biology study of LPS-induced NF-kB dynamics revealed us the highly stochastic and heterogeneous/individualist nature of the immune response in single cells.

Temporal coding of Akt signaling networks

In cellular signal transduction, information in external stimulus is coded as temporal patterns of signaling activities; however, temporal coding mechanism has been poorly investigated. Here we show how the Akt pathway, involved in cell growth, serves as low-pass filters and specifically transfers temporal information of upstream signals to downstream. We modeled the epidermal growth factor (EGF)-dependent Akt pathway in PC12 cells based on experimental results. We found counterintuitive results that peak amplitudes of receptor and downstream phosphorylation are decoupled; weak sustained EGF receptor phosphorylation, rather than strong transient phosphorylation, strongly induced S6 phosphorylation, a downstream molecule of Akt. By use of frequency response analysis, we found that the Akt pathway exhibits low-pass filter characteristics, and that this characteristic of the Akt pathway can explain the decoupling effect of peak amplitudes between receptor and downstream phosphorylation. Because low-pass filter characteristic is an intrinsic feature of biochemical reactions, our finding raises a caution in interpreting biological data without temporal information.

Positive and Negative Molecular Signaling Networks Controlling the Fate of Macrophages

Macrophages have built-in negative and positive regulatory loops that finely control the expression of pro- and anti-inflammatory genes. In particular, our laboratory has revealed that both feed forward and feedback controls exist in macrophages that regulate NFkB-mediated expression of pro-inflammatory cytokines, as well as nuclear-receptor (NR) mediated expression of anti-inflammatory genes. Furthermore, the cross-inhibition of NFkB pathway and NR pathway could potentially give rise to two bi-stable anti- and pro-inflammatory states. Knocking-out of several key molecular players can skew the bi-stable state to the direction of anti-inflammatory flavor, and serve as viable targets for the development of anti-inflammatory therapies.

Phenomenological models of regulatory networks

Even the simplest biochemical networks often have more degrees of freedoms than one can (or should!) analyze. Can we ever hope to do the physicists' favorite trick of coarse-graining, simplifying the networks to a much smaller set of effective dynamical variables that still capture the relevant aspects of the kinetics? I will argue then that methods of statistical physics and statistical model selection provide hints at the existence of rigorous coarse-grained methodologies in modeling biological information processing systems, allowing to identify features of the systems that are relevant to their functions. While a general solution is still far away, I will focus on two specific examples illustrating the two approaches. First, for a general stochastic network exhibiting the kinetic proofreading behavior, I will show that the microscopic parameters of the system are largely important only to the extent that they contribute to a single aggregate parameter, the mean first passage time through the network. Thus a phenomenological model with a single parameter does a good job explaining all of the observable data generated by this complex system. Second, building an "as simple as possible, but not simpler" model of heat avoidance response of C. elegans, we show that a phenomenological model with a single hidden "memory" node is capable of reproducing all of the observed data, hinting strongly that the worm's thermotaxis behavior resembles that of a chemotaxing E. coli.

An integrated approach for drug development and customized therapy

Integration of advances in genome sequencing and analysis, network biology, structural biology and computational chemistry may have the potential to revolutionize drug discovery and may allow customization of drug therapy. Here we describe an initial example for this potential using bacterial infections as a case study.

Elucidating the Architecture of the CDK1-APC Oscillator

Computational and experimental studies together have yielded a compendium of insights into the signal transduction involved in eukaryotic cell cycle regulation. Our present work aims to describe the biochemical mechanisms that underlie mitotic progression, while also uncovering the molecular basis of the developmental transitions that accompany early embryogenesis. Some of these studies are focused upon understanding the dynamical behavior of the (cyclin-dependent kinase 1) CDK1 - (anaphase-promoting complex) APC oscillator - the driving force behind the rapid and unimpeded cleavages that occur prior to the midblastula transition (MBT) in the early embryo of Xenopus laevis. While protein synthesis, proteolysis, and phosphorylation-dephosphorylation events drive this system in general, it remains unclear how these inputs together confer the overall output of this oscillator. Indeed, current mathematical models do not reproduce all of the dynamic features observed during a CDK1 activity oscillation. A closer look at the Wee1-Cdc25-CDK1 module hints at the possible regulation by players that are involved in other aspects of mitotic control, and recent evidence has confirmed relationships between these regulators. Might the involvement of other M-phase kinases in the activation of CDK1 serve to tune the output of this kinase as a function of cyclin stimulus? To answer this question, we have initiated a systematic analysis of the activities of M-phase kinases in relation to the pattern of cyclin stimulus and CDK1 activity in Xenopus egg extracts. Our overall goal is to map and dissect experimentally the connections of the embryonic M-phase activation network, and to gather and apply these quantitative details towards the refinement of our mathematical model of the CDK1-APC oscillatory system.

Structural sources of robustness in biochemical reaction networks

In consideration of biological design principles, it is now generally recognized that a central role must be played bysystem robustness - that is, by the capacity for sustained and precise function even in the presence of environmental disruption. Lacking, however, is a clear picture of common network features that otherwise-different biochemical modules might incorporate to ensure the robustness required. Our general interest is in what we call absolute concentration-robustness (ACR): A biochemical system is said to exhibit ACR relative to an active molecular species if the concentration of that species is identical in every positive steady-state the system might admit. In this way, the function of an ACR-possessing system can be protected even against large changes in the overall supply of the system's components - changes that might arise from cell-to-cell variability or from variations in the same cell over time. Here, mathematics and chemistry come together to identify quite subtle structural attributes that will impart ACR to any mass action network possessing them. For example, these core network features provide a common source for the strong concentration robustness observed experimentally in the markedly-different E. coliEnvZ/OmpR osmoregulation and IDHKP/IDH glyoxylate-bypass-control systems. We believe that the same structural foundation will undergird a large variety of biochemical networks for which strong concentration robustness is essential.

Noise, robustness and memory in bacterial chemotaxis

Motile bacteria navigate in chemical gradients by performing temporal comparisons of ligand concentrations. In the adapted state with no gradients present, cells perform a random walk that consists of runs interrupted by short tumbles, which allows them to efficiently explore their environment. Such random work is ensured by a precise adjustment of the tumble signal, intracellular phosphorylation level of the response regulator CheY, to the sensitive range of flagellar motor. In presence of a gradient, the random walk becomes biased: an increase in attractant concentration - as experienced by cells swimming up the gradient - rapidly suppresses tumbles and thus results in longer runs in the favourable direction. This initial response is counteracted on a longer time scale by an adaptation system that regulates pathway activity through chemoreceptor methylation. Difference in the time scales of initial response and subsequent adaptation allows a swimming cell to compare concentrations at different points in the gradient.


Chemotactic performance of bacteria is affected by several types of noise, from stochastic ligand binding to Brownian motion to stochastic protein expression, and much of the pathway evolution appears to have been driven by the selection for robust signal processing under these conditions. We combined experiments, bioinformatics and computer modelling to investigate effects of the most prominent type of noise, stochastic variations in the levels of chemotaxis proteins in a population. We showed that such gene expression noise is compensated both by the robust pathway topology and by the chromosomal organization of chemotaxis genes. At the same time, the pathway also appears to utilize noise in the expression of adaptation enzymes to broaden the range of environmental gradients that a chemotactic population as a whole can follow.

Stochastic Models of Cell Cycle Regulation in Eukaryotes

The DNA replication-division cycle in eukaryotic cells is controlled by a complex network of regulatory proteins (called cyclin-dependent kinases, Cdk's) and their activators and inhibitors. A comprehensive deterministic model of Cdk regulation in budding yeast is available (Chen et al., 2004) that accurately accounts for the average phenotypic properties of wild-type cells and 150+ mutant strains. However, the deterministic model cannot account for the considerable variabilities in cell cycle properties that have been observed among genetically identical cells. These variabilities are due in large part to small numbers of molecules in yeast cells: 100's - 1000's of molecules of each specific protein and only 10's of molecules of each specific mRNA species per yeast cell. How can the cell cycle function reliably in the face of the large intrinsic molecular fluctuations implied by such numbers? We have addressed this question by constructing a realistic model (on the scale: toy < realistic < comprehensive) of Cdk regulation in budding yeast that is suitable for exact stochastic simulation by Gillepie's algorithm. The results of this model compare favorably to the extensive statistical properties of budding yeast cell cycle progression collected recently in Fred Cross's laboratory (Di Talia et al., 2007; Skotheim et al., 2008; Di Talia et al., 2009).



  • Chen et al. (2004) Mol Biol Cell 15:3841.

  • Di Talia et al. (2007) Nature 448:947.

  • Di Talia et al. (2009) PLoS Biology, in press.

  • Skotheim et al. (2008) Nature 454:291.

An Endogenous Gene Expression Level-to-Rate Converter Provides a Fitness Advantage

Signal transduction circuits have long been known to differentiate between signals by amplifying inputs to different levels1. Here, we describe a novel transcriptional circuitry that dynamically converts greater input levels into faster rates, without increasing the final equilibrium level (i.e. a level-to-rate converter circuit). We utilize time-lapse microscopy to study human herpesvirus (cytomegalovirus) infection of live cells in real time. Strikingly, our results show that transcriptional activators accelerate viral gene expression in single cells without amplifying the steady-state levels in these cells. This level-to-rate conversion operates by dynamically manipulating the traditional 'gain-bandwidth' feedback relationship from electrical circuit theory2 to convert greater input levels into faster rates, without increasing the final equilibrium level. Combining experimental approaches with mathematical modeling, we show that level-to-rate conversion results from a highly self-cooperative transcriptional auto-regulatory loop encoded by the virus's essential transcriptional transactivator, IE23. There is a significant fitness advantage provided by level-to-rate conversion and abrogating IE2 auto-regulation eliminates level-to-rate conversion and severely impairs viral replication. Even minimal IE2 feedback circuits, lacking all other viral elements, maintain this fitness advantage via level-to-rate conversion. In general, level-to-rate converters may provide a mechanism for signal transduction circuits to rapidly respond to, and discriminate between, a diversity of signals without increasing steady-state levels of potentially cytotoxic molecules.

Molecular Noise Enhances Oscillations in the Supra-Chiasmatic Nuclei Network

In this talk, we will discuss a detailed mathematical model for circadian timekeeping within the SCN. Our proposed model consists of a large population of SCN neurons, with each neuron containing a network of biochemical reactions involving the core circadian components. Using mathematical modeling, our results show that both intracellular molecular noise and intercellular coupling (nonlinear in nature) are required to sustain stochastic oscillations in the SCN oscillator network. Our work focuses on the problem of overcoming noise in oscillator systems, and our results highlight the importance of transcriptional noise in enhancing oscillations rather than dampening them. Surprisingly, our predictions from our model have been confirmed experimentally; we conclude with a short discussion of these results.