Workshop 3: Robustness in Biological Systems

(February 6,2012 - February 10,2012 )

Organizers


Frank Doyle
Rick Durrett
Department of Mathematics, Duke University
Arthur Lander
Center for Complex Biological Systems, University of California, Irvine
Andreas Wagner
Evolutionary Biology and Environmental Studies, University of Zurich

Most biological systems from the subcellular to the population population level must solve the following difficult problem. They must continue to function reliably despite continually changing environmental inputs and despite individual differences in internal parameters due to genetic polymorphisms. That is, their functions should be robust to natural biological variability. On the other hand, these systems must also respond robustly and change their functions in response to biologically important external or internal signals. The biological systems that we see have evolved elaborate regulatory mechanisms in order that they can accomplish both tasks. Examples include metabolic and neural networks, development, adaptation, and population diversity. Most of these systems are not resting at equilibria, but rather exist in a dynamic stochastic equilibrium. Thus the mathematical issues involve understanding when this stochastic equilibrium is relatively stable and when it makes large shifts in response to appropriate biological signals.

Accepted Speakers

Ricardo Azevedo
Biology & Biochemstriy, University of Houston
Neda Bagheri
Chemical & Biological Engineering, Northwestern University
Declan Bates
College of Engineering, Mathematics and Physical Sciences, University of Exeter
Buzz Baum
Laboratory for Molecular Cell Biology, University College London
Aviv Bergman
Systems and Computational Biology,
Suzanne Gaudet
Genetics, Harvard Medical School
Kunihiko Kaneko
Basic Science,
Joanna Masel
Ecology & Evolutionary Biology,
Chris Myers
Department of Physics, Cornell University
Ilya Nemenman
Physics and Biology, Emory University
Qing Nie
Department of Mathematics, University of California, Irvine
Frederik Nijhout
Biology, Duke University
Mark Siegal
Department of Biology, New York University
Jorg Stelling
D-BSSE,
Stephanie Taylor
Computer Science, Colby College
Paul Turner
Ecology and Evolutionary Biology, Yale University
Jon Wilkins
N/A, Santa Fe Institute
Monday, February 6, 2012
Time Session
09:00 AM
09:50 AM
Frederik Nijhout - Multiple and Diverse Homeostatic Mechanisms in a Complex Metabolic Network
Cells accomplish a great diversity of biochemical and molecular functions and many of these must be performed simultaneously. Metabolic systems are subject to large and irregular hourly and daily fluctuations in inputs, and are subject to continually changing demands for particular metabolites. Since many metabolic pathways share enzymes, metabolites and cofactors, there must be mechanisms that ensure that large variation in a particular region of a pathway does not compromise functions elsewhere.

We study these questions using a physiologically-based mathematical model for folate-mediated one-carbon metabolism. This complex network provides the first steps in nucleotide synthesis, and the synthesis of S-adenosylmethionine, the universal donor of methyl groups for a host of methyl transfer reactions such as DNA and histone methylation, and the synthesis of glutathione, the universal antioxidant in animals.

Regulation of stability in this network has features that resemble physiological homeostasis. Homeostasis of critical biological functions requires a surprisingly diverse set of dynamic biochemical regulatory mechanisms which cannot be deduced from examination of the biochemical reaction diagram. We have elucidated ten different homeostatic mechanisms that operate simultaneously to stabilize function against fluctuations in input and demand. The homeostasis of critical biological functions (e.g., methylation capacity, nucleotide synthesis, response to oxidative stress) is not the consequence of a stable steady-state, but instead requires continuous large, adaptive changes in some fluxes and metabolites.

Work done in collaboration with Mike Reed.
09:50 AM
07:00 PM
Neda Bagheri - A dynamical systems approach to resolve cytokine signaling responses by human T cells
An immune response involves the release of many cytokines with various immunomodulatory functions. Its efficacy depends, in part, on the types of cytokines secreted by activated T cells and their corresponding kinetic profiles. Through serial microengraving [1], the Love Lab is able to quantify single and multiple T cell cytokine secretion dynamics, offering a unique multidimensional perspective to study time-dependent functional differences specific to immunophenotypes. We have employed dynamical systems strategies to these data to investigate the temporal evolution of specific cytokine responses that are indicative of a healthy immune system. Further experiments using defined stimulatory perturbations provide additional insight on the regulatory mechanisms that control T cell function. As a result, we are able to better resolve and predict the nonlinear functional dynamics governing qualitatively different T-cell responses. This systems-level methodology should enable the identification of unique time-dependent cytokine signatures indicative of productive immune function, and may offer metrics to design more effective and personalized treatment strategies.

[1] Qing Han et al., Lab Chip, 2010, 10: 1391-1400.
11:20 AM
12:10 PM
Qing Nie - Noise Attenuation and Spatial Dynamics in Biological Systems
Qing Nie, Departments of Mathematics and Biomedical Engineering Center for Complex Biological Systems, Center for Mathematical and Computational Biology University of California, Irvine

The focus of the talk will be on stochastic effects for spatial dynamics of complex biology systems. Through modeling and simulations, we will study several general principles underlining noise attenuation and robustness in multiscale systems involving both intracellular and extracellular components. For several cases, existing and new experimental evidences that support our theoretical and computational results will also be presented.
02:00 PM
02:50 PM
Kunihiko Kaneko - Evolution of Robustness formulated in terms of Phenotypic Variances
Kunihiko Kaneko, University of Tokyo, Research Center for Complex Systems Biology

Characterization of plasticity, robustness, and evolvability is an important issue in biology. First, proportionality among evolution speed, phenotypic plasticity, and isogenic phenotypic fluctuation is derived by borrowing and extending fluctuation-response relationship in physics. Following an evolutionary stability hypothesis we derive a general proportionality relationship between the phenotypic fluctuations of epigenetic and genetic origins; The former is given by the variance of phenotype due to noise in developmental process, and the latter due to genetic mutation. The relationship suggests a link between robustness to noise and to mutation, since robustness can be defined by the sharpness of the distribution of phenotype. Second, the proportionality between the variances is demonstrated to hold also over different phenotypic traits, when the system acquires robustness through the evolution. Third, adaptation to environmental variation is studied, which is shown to require a certain degree of phenotypic fluctuations. Indeed, the highest adaptability is achieved when the system is near the transition point to lose the robustness. Here, change in phenotypes (i.e., in the gene expression pattern) induced by environmental change is reduced later as a result of genetic evolution. All the obtained relationships are confirmed in models of gene expression dynamics, as well as in laboratory experiments. Based on our results, we revisit Waddington's canalization and genetic assimilation, and discuss how consistency between evolutionary and developmental scales constrains developmental process and leads to universal laws on phenotypic fluctuations.

References: K.K. Life: An Introduction to Complex Systems Biology, Springer (2006); PLoS One(2007) 2 e434, J Biosci.34 (2009) 529: BMC Evolutionary Biology 11(2011) 27; Ito et al., Mol. Sys. Biol. 5 (2009) 264
02:50 PM
03:40 PM
Ilya Nemenman - Robust completion time distributions in complex biological networks
Ilya Nemenman, Physics, Emory University

Biochemical processes typically involve huge numbers of individual reversible steps, each with its own dynamical rate constants. For example, kinetic proofreading processes rely upon numerous sequential reactions in order to guarantee the precise construction of specific macromolecules. I will present a characterization of the first passage (completion) time distributions for such processes. I will argue that, for a wide class of biochemical kinetics systems related to kinetic proofreading, the completion time time behavior simplifies as the system size grows: it becomes either deterministic or exponentially distributed, with a very narrow transition between the two regimes. In both regimes, the dynamical complexity of the full system is trivial compared to its apparent structural complexity. This robust simplification of completion time distributions is independent of many microscopic details of the signaling systems and can be utilized for efficient control of cellular response properties. Even further simplifications are possible when one considers dynamics of many coupled kinetic proofreading enabled receptors, which can attain low activation noise with robust mean time to activation.
04:10 PM
04:35 PM
Maxim Artyomov - Systematic identification of topologically essential interactions in regulatory networks
Screens monitoring the effects of deletion, knock-down or over expression of regulatory genes on the expression of their target genes are critical for deciphering the organization of complex regulatory networks. However, since perturbation assays cannot distinguish direct from indirect effects, the derived networks are significantly more complex than the true underlying one. Discovery of the true network organization is a long-standing challenge and several approaches have been developed to infer regulatory networks based on gene expression data. Recent studies indicate [ref] that information obtained from perturbation screens is critical for the ability to identify network structure accurately. This information is typically incorporated into network inference algorithms in two ways: first, the strength of the perturbation effect is translated into interaction confidence values and, second, topological analysis of the experimental networks is performed to find interactions that are most important to preserve network structure and, hence, are more likely to be biological. This underscores importance of having accurate methodology for accurate inference of network topology. In this work we present Exigo, an approach for systematic analysis of network topology. Exigo provides the means to identify core network structure for an input network of any topology with an arbitrary number of activating and inhibiting interactions. We further show that Exigo allows for significant improvement in the network inference. To illustrate this, we constructed a chimeric network inference method that incorporates Exigo into existing inference pipeline [ref], benchmarked it against DREAM challenge networks and found significant improvement in network inference compared to DREAM top performers.
04:35 PM
05:00 PM
- Using noisy inputs to prevent infant apnea
Infant apnea, defined as a pause in breathing for more than 20 seconds, can lead to oxygen desaturation and the need for resuscitation and assisted ventilation. A recent study has demonstrated that continuously applied stochastic (randomly fluctuating) somatosensory stimulation stabilizes breathing patterns in preterm infants and can reduce apnea by approximately 65%. I hypothesize that stochastic inputs to the respiratory central pattern generator (CPG) increase the dynamic range of the breathing rhythm in neonates. In this talk, I will discuss a proposal to test this hypothesis through a combination of in vitro electrophysiology and computational modeling to understand the role of noise in the immature respiratory CPG.
Tuesday, February 7, 2012
Time Session
09:00 AM
09:50 AM
Aviv Bergman - (Systems & Comput Biology, Albert Einstein College of Med), Genetic versus Environmental Robustness
Not available.
11:20 AM
12:10 PM
Joanna Masel - Robustness to gene expression errors, and the consequences for evolvability
Making genes into gene products is subject to predictable errors, each with a phenotypic effect that depends on a normally cryptic sequence. The distribution of fitness effects of these cryptic sequences, like that of new mutations, is bimodal. For example, a cryptic sequence might be strongly deleterious if it causes protein misfolding, or it might have only a minor effect if it preserves the protein fold and tweaks function. Few sequences have effect sizes that fall in between.

Strongly deleterious sequences can be subject to some selection even while they are cryptic, and expressed only at low levels that depend on a molecular error. Robustness to the potentially deleterious effects of cryptic sequences can be achieved globally by avoiding making errors (e.g., via proofreading machinery) or locally by ensuring that each cryptic sequence has a relatively benign effect. The local solution requires powerful selection acting on every cryptic site, and so evolves only in large populations. Small populations with less effective selection evolve global proofreading solutions. However, we also find that for a large range of realistic intermediate population sizes, the evolutionary dynamics are bistable and either solution may result. The local solution, which does not occur in very small populations, facilitates the co-option of cryptic sequences and therefore substantially increases evolvability. This can occur even in genetically uniform populations, illustrating that neighbourhood richness or "quality" in genotype space can be more important to evolvability than the quantity of genotypes in a population spread across that space.
02:00 PM
02:50 PM
Jon Wilkins - Robustness and Intragenomic Conflict
When natural selection is acting at the level of the the individual phenotype, we expect selection to favor more robust phenotypes. However, selection acting at the level of the gene can undermine adaptation of the individual organism, and lead to the fixation of suboptimal traits. Genomic imprinting, the phenomenon where the pattern of expression of an allele depends on its parental origin, is thought to result from and evolutionary intragenomic conflict, where maternally and paternally inherited alleles favor different optimal phenotypes. Using a simple model, I will illustrate how this can lead to the systematic unravelling of phenotypic robustness.
02:50 PM
03:15 PM
Emilie Snell-Rood - Developmental selection as a mechanism of robustness: implications for genetic assimilation and life history evolution
Not available.
04:00 PM
04:25 PM
David Holloway - The Control of Gene Expression Noise in Embryonic Spatial Patterning
Fruit flies are models for understanding the genetic regulation involved in specifying the complex body plans of higher animals. The head-to-tail (anterior-posterior) axis of the fly (Drosophila) is established in the first hours of development. Maternally supplied factors form concentration gradients which direct embryonic (zygotic) genes where to be activated to express proteins. These protein patterns specify the positions and cell types of the body's tissues. Recent research has shown, comparing between embryos, that the zygotic gene products are much more precisely positioned than the maternal gradients, indicating an embryonic error reduction mechanism. Within embryos, there is the additional aspect that DNA and mRNA operate at very low copy number, and the associated high relative noise has the potential to strongly affect protein expression patterns. In recent work, we have focused on the noise aspects of positional specification within individual embryos, and what molecular mechanisms confer robustness to the process.

We simulate activation of hunchback (hb), a primary target of the maternal Bicoid (Bcd) protein gradient, which forms an expression pattern dividing the embryo into anterior and posterior halves. We use a master equation approach to simulate the stochastic dynamics of hb regulation, including the binding/unbinding of Bcd molecules at the hb promoter (the portion of DNA regulating hb transcription), hb transcription, subsequent translation to Hb protein, binding/unbinding of Hb at the promoter (self-regulation), and diffusion of the Bcd and Hb proteins. Model parameters were set by deterministically matching large scale pattern features for a series of experimental expression patterns: wild-type (WT) embryos; hb mutants lacking self-regulation; and constructs in which portions of the hb promoter were used to express a reporter gene (lacZ). The model was then solved stochastically to predict the noise output in these different experiments. In subsequent noise measurements we experimentally corroborated several predictions: including that mRNA is noisier than protein and that Hb self-regulation reduces noise.

Results indicate that WT (self-regulatory) Hb output noise is predominantly dependent on the transcription and translation dynamics of its own expression, and is uncorrelated with Bcd fluctuations. In the constructs and mutant, which lack self-regulation, we find that increasing the number and strength of Bcd binding sites (there are 6 in the core hb promoter) provides a rudimentary level of noise reduction. We have recently incorporated a known inhibitor of hb, Kr�ppel (Kr), into the model � preliminary indications are that the Hb-Kr activator-inhibitor dynamics increase precision at the mid-embryo boundary.

The analysis of hb shows common modes of gene regulation (e.g. multiple regulatory sites, self-regulation) that are involved in noise reduction, which can be applied generally to reproducibility and determinacy of spatial patterning in other developmental phenomena.
Wednesday, February 8, 2012
Time Session
09:00 AM
09:50 AM
Ricardo Azevedo - Sex, Robustness, and Evolvability
Evolution is the movement of populations through a space of genotypes. This space can be modeled as an undirected network connecting genotypes that can be reached through mutation. In this view, the mutational robustness of a genotype is the proportion of its mutational neighbors that are viable. Robustness can facilitate the exploration of genotype networks, or evolvability. Sexual reproduction is also widely believed to promote evolvability, for two reasons. First, because it allows long jumps through genotype space. Second, because it selects for mutational robustness. Here, I show that, depending on the structure of the genotype network, sexual reproduction may not select for the highest mutational robustness, and can actually reduce evolvability.
10:40 AM
11:30 AM
Arthur Lander - What price robustness?
There are at least three classes of question we can ask about the robustness of biological systems. "What are the strategies for robustness (i.e. what mechanisms make systems robust)?", "How does robustness evolve (i.e. what evolutionary mechanisms drive biological systems to become robust)?", and "What are the consequences of robustness (i.e. how does robustness, or the evolutionary paths that lead to it, constrain the organization and behavior of biological systems)? My talk concerns the last of these classes of question which, arguably, has been the least well explored. Work from several groups indicates that robustness may constrain biological systems in at least two ways: through trade-offs (whereby the need to overcome interference between strategies for achieving different types of robustness constrains system organization and drives the evolution of complexity), and through inadvertent selection for epistasis (whereby the evolution of robustness drives a net increase in the nonlinearity of interactions between system components). From a practical standpoint, it is not enough that we know that such constraints arise; we'd like to know when and where in any given system they will arise, and how large the effects will be. To this end, we have been building and analyzing models of developmental systems, exploring tradeoffs in pattern formation and growth control engendered by interference among strategies for parametric robustness, "microenvironmental" robustness, robustness to somatic mutation, etc. We have also been exploring models in which selection for robustness drives increased synergistic epistasis, in order to learn how the combinatorial fragility that such an effect produces is distributed among the different components of a network. Among other things, the results of such studies have direct bearing on our expectations for the kinds of genetic interactions and allele frequencies that underlie human genetic disease.
01:20 PM
01:45 PM
Ryan Gutenkunst - Protein domain evolution is constrained by? network robustness to rate constant changes.
Not available
01:45 PM
02:10 PM
Jeremy Draghi - Epistasis links robustness to adaptation
Not available.
03:00 PM
03:50 PM
Suzanne Gaudet - Lyapunov exponent analysis of an extrinsic apoptosis signaling network
In multicellular organisms, the appropriate control of apoptosis, or cell death, is essential to homeostasis and health. There is a striking variability in behavior as cells respond to death-inducing ligands: even within a clonal population of cancer cells, cells die at different times and some cells survive. In addition, some dying cells require a signal transduction pathway involving the permeabilization of the mitochondrial outer membrane (MOMP), while others can commit to apoptosis without that pathway. Using a combination of single-cell measurements and analysis of computational models of the relevant protein signaling networks, we are uncovering the relationships between multiple factors determining the timing of ligand-induced cell death and the requirement for MOMP. Although the cell death leads to an inert state, the cell death decision process is a dynamic process which is not amenable to steady-state analysis. This talk will focus on our use of Lyapunov analysis and phase-diagrams to explore which initial cellular states result in cell death or survival following treatment with death ligands and which regions of phase-space show homogeneous (robust) or heterogeneous (sensitive) cell behavior.
03:50 PM
04:40 PM
Andreas Wagner - Robustness and the space of possible metabolisms
Not available
Thursday, February 9, 2012
Time Session
09:00 AM
09:50 AM
Paul Turner - Robustness and evolvability in RNA viruses
Laboratory experiments with RNA bacteriophage phi-6 were used to examine the evolution of mutational robustness (phenotypic constancy in the face of mutation input), and the relationship between robustness and evolvability (adaptive potential). We previously showed that selection for robustness was relaxed under virus co-infection, because complementation between virus genotypes automatically buffers mutational effects. Recent results showed that lineages founded by robust viruses evolved faster than those initiated by their brittle (non-robust) counterparts, in novel environments where heat-shock survival is an adaptive challenge; these data demonstrated a positive link between robustness and evolvability. Here we show that the phenomenon is bi-directional: prior selection for heat-shock survival caused viruses to evolve robustness as a correlated trait. This tight relationship between robustness and heat-shock evolvability may be explained by a single amino-acid substitution in the lysis enzyme that governs virus entry/exit from the cell. A combination of structural-biology, crystallography and fitness data suggested that the mutant enzyme was favored under heat shock, but deleterious under benign conditions. Using a systems-biology approach, we discuss why the phi-6 lysis-enzyme gene may be a global robustness regulator for the virus.
09:50 AM
10:40 AM
Mark Siegal - The robustness continuum: yeast cells hedge their bets against unpredictable environmental change
Genetically identical cells grown in the same culture display striking cell-to-cell heterogeneity in gene expression and other traits. A crucial challenge is to understand how much of this heterogeneity reflects the noise tolerance of a robust system and how much serves a biological function. In some circumstances, heterogeneous traits might be favored over robust ones. For example, in bacteria cell-to-cell heterogeneity can serve as a bet-hedging mechanism, allowing a few cells to survive acute antibiotic stress while the others perish. Where a population of organisms falls on the continuum from uniformity to bet-hedging depends on the environmental regime it experiences. We describe a bet-hedging phenomenon in the yeast Saccharomyces cerevisiae, which occupies a range of natural and human-associated environments. We use a novel, high-throughput microscopy assay that monitors variable protein expression, growth rate and survival outcomes of tens of thousands of yeast microcolonies simultaneously. Clonal yeast populations display broad distributions of growth rates, and slow growth predicts resistance to acute heat stress. Expression of Tsl1, a trehalose-synthesis regulator, marks slow-growing cells and contributes to this resistance. I will present these results and discuss them in the context of the evolutionary forces shaping robust and heterogeneous traits.
11:20 AM
12:10 PM
Stephanie Taylor - Points of sensitivity in circadian cells and networks
Daily behaviors of mammals are regulated by the so-called "master" circadian clock in the hypothalamic suprachiasmatic nucleus. It is composed of thousands of neurons, each of which contains the transcriptional feedback loops that are the clockworks. Isolated, these neurons show low-amplitude or no oscillations. In the tissue, they show high-amplitude, coordinated rhythms. The intercellular signaling network transforms the behavior of neurons from sloppy to precise oscillators. For the clock to function, individual cells must correctly adjust their phase and amplitude to signals and the tissue as a whole must arrive at a predictable consensus of in-phase oscillations at a period of nearly 24 hours. Thus, robust oscillations of the clock as a whole depend on features both of the cells and of the network.

In this talk, I will present analyses of data taken from SCN slices and relate it to simulation and analyses of mathematical (ordinary differential equation) models of the SCN. I will discuss oscillator-specific measures of sensitivity and relate them to network behaviors. For example, when the phase response magnitude of each cell is dynamic (higher magnitude when the cell is lower amplitude), networks of cells synchronize quickly.
01:30 PM
02:45 PM
Frederik Nijhout - Multiple and Diverse Homeostatic Mechanisms in a Complex Metabolic Network
Cells accomplish a great diversity of biochemical and molecular functions and many of these must be performed simultaneously. Metabolic systems are subject to large and irregular hourly and daily fluctuations in inputs, and are subject to continually changing demands for particular metabolites. Since many metabolic pathways share enzymes, metabolites and cofactors, there must be mechanisms that ensure that large variation in a particular region of a pathway does not compromise functions elsewhere.

We study these questions using a physiologically-based mathematical model for folate-mediated one-carbon metabolism. This complex network provides the first steps in nucleotide synthesis, and the synthesis of S-adenosylmethionine, the universal donor of methyl groups for a host of methyl transfer reactions such as DNA and histone methylation, and the synthesis of glutathione, the universal antioxidant in animals.

Regulation of stability in this network has features that resemble physiological homeostasis. Homeostasis of critical biological functions requires a surprisingly diverse set of dynamic biochemical regulatory mechanisms which cannot be deduced from examination of the biochemical reaction diagram. We have elucidated ten different homeostatic mechanisms that operate simultaneously to stabilize function against fluctuations in input and demand. The homeostasis of critical biological functions (e.g., methylation capacity, nucleotide synthesis, response to oxidative stress) is not the consequence of a stable steady-state, but instead requires continuous large, adaptive changes in some fluxes and metabolites.

Work done in collaboration with Mike Reed.
03:20 PM
03:45 PM
Kathryn Miller-Jensen - Analysis of context-dependent stochastic phenotypes in HIV-1 latency
Analysis of context-dependent stochastic phenotypes in HIV-1 latency.
03:45 PM
04:10 PM
Sayak Mukherjee - Robustness of the Cell Signaling Network as a Means to Discriminate Among the Di erent Models of Itk Kinase Regulation in T cells
The process that warrants the generation of self tolerant peripheral T cells is called the thymocyte selection. During this maturation process, the overtly self reactive as well as unresponsive thymocytes are deleted from the cell population. The thymocytes equipped with T cell receptors (TCRs), capable of responding moderately to the self peptides are allowed to survive. Recently water soluble second messenger, inositol(1,3,4,5) tetrakisphosphate (IP4), has been implicated to play a crucial role in thymocyte positive selection (Huang et al.). It has been suggested that these IP4 molecules regulate the transient activation of the Tec- family protein tyrosine kinase Itk through a competing positive and negative feedbacks. The exact molecular mechanism involved in this feedback is however unclear. It is possible to con- struct more than one model with contrasting molecular mechanisms to explain the present body of experimental observations. This calls for criteria to choose among these models.

Robustness in face of the variation of the parameters in a model has been ubiquitously used as a criterion for model discrimination. Here we have used the maximum entropy, calculated with the constraints imposed by the experiments as a measure of robustness. Our data indicates that the models which are maximally robust share a cooperative allosteric mode of Itk regulation involving dimeric PH domains.

Joint work with Stephanie Rigaud, S. Seok, Agnieszka Prochenka, Guo Fu, Michael Dworkin, Nicholas R. J. Gascoigne, Veronika J. Vieland, Karsten Sauer, and Jayajit Das.
04:10 PM
05:00 PM
Chris Myers - Detection, evasion, robustness and redundancy in plant-pathogen interactions
Plants and pathogens are engaged in ongoing battles of detection and evasion, which are recognized to be crucial in driving their coevolution. Simple genetic models of plant-pathogen interactions have been examined for decades, but recent experimental results point to a rich interplay involving partially redundant pathogen effector repertoires, sector-switching plant defense networks, convergent evolution of pathogen effector targeting, and interactions among guards, guardees and decoys. Robustness and evolvability of biological systems are increasingly recognized to be supported in part by nontrivial geometries in high-dimensional configuration spaces that span the many levels of organization from genotype to phenotype. Building on some previous work in characterizing the shapes of such spaces and their implications for biological function and evolution, I am exploring the geometric structure of detection and evasion in models of plant-pathogen interactions, and its ramifications for robustness, redundancy, and plant-pathogen coevolution.
Friday, February 10, 2012
Time Session
09:00 AM
09:50 AM
Declan Bates - Polyamine biosynthesis and translational frameshifting in yeast: characterising a cellular feedback controller
The polyamine molecules putrescine, spermidine and spermine are involved in a number of important cellular processes such as transcriptional silencing, translation, protection from reactive oxygen species and coenzyme A synthesis. Components of the polyamine pathway are also potential targets for cancer therapeutics, as unregulated polyamine synthesis can trigger uncontrolled cell proliferation. Conversely, polyamine depletion can cause apoptosis, and during development, defects leading to mental retardation in humans (Snyder-Robinson Syndrome). Controlling polyamine concentrations is thus a significant regulatory challenge for the cell, because there are multiple cellular requirements for polyamines as well as a need to homeostatically maintain their concentration within a certain non-toxic range. In the cell, polyamine concentrations are regulated by multiple mechanisms, the most important of which is a negative feedback control loop involving Spe1 (the enzyme catalysing the first step in the polyamine biosynthesis pathway) and the protein antizyme, which is synthesised via a +1 ribosomal frameshift during translation of the antizyme mRNA. In this talk I will present the first predictive model of the polyamine feedback controller, which has been developed and validated using a Systems Biology approach incorporating enzyme kinetics, control engineering and experimental molecular biology.
09:50 AM
10:40 AM
Jorg Stelling - Biological Robustness: Implications for Systems Identification
Widespread robustness of cellular networks, in principle, could make the systems biology task of identifying and characterizing cellular networks difficult because it implies potentially large uncertainties in parameters or topologies of mathematical models that quantitatively capture the network behavior. Yet, robustness may be exploited in systems identification as an additional criterion for the plausibility of a given model. In both cases, quantitative characterizations of system robustness to perturbations are needed. This talk focuses on implications of robustness on systems identification with a focus on metabolic networks. Using the network structure and assumptions on optimal rejection of perturbations alone, we developed a method to analyze sensitivities in metabolic reaction networks that can be directly linked to, for instance, variability in gene expression. In an application to global data sets on the reaction of Bacillus subtilis to changes in nutrient source, the method revealed qualitatively different, large-scale responses to apparently simple, symmetric perturbations.

Model-based analysis enabled us to interpret the qualitative control scheme in an evolutionary context, and to reason about optimality of the bacterium's responses. In extending the sensitivity-based approach, we propose a method that infers possible structures of control mechanisms for metabolic networks directly by analyzing trade-offs between robust control and its associated costs, to yield experimentally testable predictions on transcription factor - gene interactions. General methods for quantification of robustness, in addition, are becoming feasible by using probabilistic approaches, which may ultimately provide principled insight into the role of robustness for the identification of cellular systems.
11:20 AM
12:10 PM
- Robustness in intracellular transport
Robustness in intracellular transport.
Name Affiliation
Artyomov, Maxim maxim@broadinstitute.org Immunology and Pathology, Washington University in St.Louis
Azevedo, Ricardo razevedo@uh.edu Biology & Biochemstriy, University of Houston
Bagheri, Neda nbagheri@mit.edu Chemical & Biological Engineering, Northwestern University
Bates, Declan D.G.Bates@exeter.ac.uk College of Engineering, Mathematics and Physical Sciences, University of Exeter
Bauer, Christopher cbauer@nyu.edu Biology, New York University
Baum, Buzz b.baum@ucl.ac.uk Laboratory for Molecular Cell Biology, University College London
Bergman, Aviv aviv@aecom.yu.edu Systems and Computational Biology,
Birtwistle, Marc marc.birtwistle@gmail.com Cancer Research Center, Georgia Health Sciences University
Buzzard, Greg buzzard@math.purdue.edu Mathematics, Purdue University
Chu, Hui-Yi chu.164@osu.edu Molecular Genetics, The Ohio State University
Conlisk, Terry conlisk.1@osu.edu Mechanical Engineering, The Ohio State University
Cordero, Otto ottox@mit.edu Civil and Environmental Engineering, Massachusetts Institute of Technology
Dinh, Vu vdinh@math.purdue.edu Math, Purdue University
Draghi, Jeremy jdraghi@gmail.com Biology, University of Pennsylvania
Durrett, Rick rtd@math.duke.edu Department of Mathematics, Duke University
Dworkin, Michael dworkin.11@osu.edu Mathematics, The Ohio State University
Ewool, Richard rce2m@mtmail.mtsu.edu Computational Science, Middle Tennessee State University
Gaudet, Suzanne suzanne_gaudet@hms.harvard.edu Genetics, Harvard Medical School
Geiler-Samerotte, Kerry kas25@nyu.edu Systems Biology, New York University
Goldhill, Daniel daniel.goldhill@yale.edu Ecology and Evolutionary Biology, Yale University
Gutenkunst, Ryan rgutenk@email.arizona.edu Molecular and Cellular Biology, University of Arizona
Holloway, David David_Holloway@bcit.ca Mathematics, British Columbia Institute of Technology
Kaneko, Kunihiko kaneko@complex.c.u-tokyo.ac.jp Basic Science,
Lander, Arthur adlander@uci.edu Center for Complex Biological Systems, University of California, Irvine
Lawley, Sean lawley@math.duke.edu Mathematics, Duke University
Liu, Xinfeng xfliu@math.sc.edu Mathematics, University of South Carolina
Masel, Joanna masel@u.arizona.edu Ecology & Evolutionary Biology,
Miller-Jensen, Kathryn kathryn.miller-jensen@yale.edu Biomedical Engineering, Yale University
Mukherjee, Sayak smukhe04@vt.edu BCMM, Nationwide Childrens Hospital
Murrugarra, David davidmur@vbi.vt.edu Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University
Myers, Chris crm17@cornell.edu Department of Physics, Cornell University
Nemenman, Ilya ilya.nemenman@emory.edu Physics and Biology, Emory University
Nguyen, Tristan tristan.nguyen@afosr.af.mil Mathematics, Information, and Life Sciences, Air Force Office of Scientific Research
Nie, Qing qnie@math.uci.edu Department of Mathematics, University of California, Irvine
Nijhout, Frederik hfn@duke.edu Biology, Duke University
Pooyaei Mehr, Fatemeh Shaadi fpooyaei_mehr@amnh.org Biology, City University of New York (CUNY)
Rajon, Etienne rajon@email.arizona.edu Department of Biology, University of Pennsylvania
Rorick, Mary rorick@umich.edu Ecology and Evolutionary Biology/HHMI, University of Michigan
Saito, Nen saito@complex.c.u-tokyo.ac.jp Graduate School of Arts and Sciences, The University of Tokyo
Shih, Yu-Keng shihy@cse.ohio-state.edu Computer Science and Engineering, The Ohio State University
Siegal, Mark mark.siegal@nyu.edu Department of Biology, New York University
Snell-Rood, Emilie emilies@umn.edu Ecology, Evolution and Behavior, University of Minnesota
Srinivasan, Manoj srinivasan.88@osu.edu Mechanical and Aerospace Engineering , The Ohio State University
Stelling, Joerg joerg.stelling@bsse.ethz.ch D-BSSE,
Swirydowicz, Katarzyna kswirydo@vt.edu Mathematics, Virginia Polytechnic Institute and State University
Taylor, Stephanie srtaylor@colby.edu Computer Science, Colby College
Torres-Sosa, Christian chtorso@gmail.com Fenomenos no lineales y complejidad, National Autonomous University of Mexico (UNAM)
Towfic, Fadi towfic@broadinstitute.org Medical and Population Genetics, The Broad Institute
Turner, Paul paul.turner@yale.edu Ecology and Evolutionary Biology, Yale University
Wagner, Andreas andreas.wagner@ieu.uzh.ch Evolutionary Biology and Environmental Studies, University of Zurich
Warren, Keith warren.193@osu.edu Social Work, The Ohio State University
Wilkins, Jon wilkins@santafe.edu N/A, Santa Fe Institute
Wilson, Ben bawilson@stanford.edu Biology, Stanford University
Xu, Lin lx37@cornell.edu MBG, Cornell University
Zhu, Quanyan zhu31@illinois.edu Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Systematic identification of topologically essential interactions in regulatory networks
Screens monitoring the effects of deletion, knock-down or over expression of regulatory genes on the expression of their target genes are critical for deciphering the organization of complex regulatory networks. However, since perturbation assays cannot distinguish direct from indirect effects, the derived networks are significantly more complex than the true underlying one. Discovery of the true network organization is a long-standing challenge and several approaches have been developed to infer regulatory networks based on gene expression data. Recent studies indicate [ref] that information obtained from perturbation screens is critical for the ability to identify network structure accurately. This information is typically incorporated into network inference algorithms in two ways: first, the strength of the perturbation effect is translated into interaction confidence values and, second, topological analysis of the experimental networks is performed to find interactions that are most important to preserve network structure and, hence, are more likely to be biological. This underscores importance of having accurate methodology for accurate inference of network topology. In this work we present Exigo, an approach for systematic analysis of network topology. Exigo provides the means to identify core network structure for an input network of any topology with an arbitrary number of activating and inhibiting interactions. We further show that Exigo allows for significant improvement in the network inference. To illustrate this, we constructed a chimeric network inference method that incorporates Exigo into existing inference pipeline [ref], benchmarked it against DREAM challenge networks and found significant improvement in network inference compared to DREAM top performers.
Sex, Robustness, and Evolvability
Evolution is the movement of populations through a space of genotypes. This space can be modeled as an undirected network connecting genotypes that can be reached through mutation. In this view, the mutational robustness of a genotype is the proportion of its mutational neighbors that are viable. Robustness can facilitate the exploration of genotype networks, or evolvability. Sexual reproduction is also widely believed to promote evolvability, for two reasons. First, because it allows long jumps through genotype space. Second, because it selects for mutational robustness. Here, I show that, depending on the structure of the genotype network, sexual reproduction may not select for the highest mutational robustness, and can actually reduce evolvability.
A dynamical systems approach to resolve cytokine signaling responses by human T cells
An immune response involves the release of many cytokines with various immunomodulatory functions. Its efficacy depends, in part, on the types of cytokines secreted by activated T cells and their corresponding kinetic profiles. Through serial microengraving [1], the Love Lab is able to quantify single and multiple T cell cytokine secretion dynamics, offering a unique multidimensional perspective to study time-dependent functional differences specific to immunophenotypes. We have employed dynamical systems strategies to these data to investigate the temporal evolution of specific cytokine responses that are indicative of a healthy immune system. Further experiments using defined stimulatory perturbations provide additional insight on the regulatory mechanisms that control T cell function. As a result, we are able to better resolve and predict the nonlinear functional dynamics governing qualitatively different T-cell responses. This systems-level methodology should enable the identification of unique time-dependent cytokine signatures indicative of productive immune function, and may offer metrics to design more effective and personalized treatment strategies.

[1] Qing Han et al., Lab Chip, 2010, 10: 1391-1400.
Polyamine biosynthesis and translational frameshifting in yeast: characterising a cellular feedback controller
The polyamine molecules putrescine, spermidine and spermine are involved in a number of important cellular processes such as transcriptional silencing, translation, protection from reactive oxygen species and coenzyme A synthesis. Components of the polyamine pathway are also potential targets for cancer therapeutics, as unregulated polyamine synthesis can trigger uncontrolled cell proliferation. Conversely, polyamine depletion can cause apoptosis, and during development, defects leading to mental retardation in humans (Snyder-Robinson Syndrome). Controlling polyamine concentrations is thus a significant regulatory challenge for the cell, because there are multiple cellular requirements for polyamines as well as a need to homeostatically maintain their concentration within a certain non-toxic range. In the cell, polyamine concentrations are regulated by multiple mechanisms, the most important of which is a negative feedback control loop involving Spe1 (the enzyme catalysing the first step in the polyamine biosynthesis pathway) and the protein antizyme, which is synthesised via a +1 ribosomal frameshift during translation of the antizyme mRNA. In this talk I will present the first predictive model of the polyamine feedback controller, which has been developed and validated using a Systems Biology approach incorporating enzyme kinetics, control engineering and experimental molecular biology.
(Systems & Comput Biology, Albert Einstein College of Med), Genetic versus Environmental Robustness
Not available.
Epistasis links robustness to adaptation
Not available.
Lyapunov exponent analysis of an extrinsic apoptosis signaling network
In multicellular organisms, the appropriate control of apoptosis, or cell death, is essential to homeostasis and health. There is a striking variability in behavior as cells respond to death-inducing ligands: even within a clonal population of cancer cells, cells die at different times and some cells survive. In addition, some dying cells require a signal transduction pathway involving the permeabilization of the mitochondrial outer membrane (MOMP), while others can commit to apoptosis without that pathway. Using a combination of single-cell measurements and analysis of computational models of the relevant protein signaling networks, we are uncovering the relationships between multiple factors determining the timing of ligand-induced cell death and the requirement for MOMP. Although the cell death leads to an inert state, the cell death decision process is a dynamic process which is not amenable to steady-state analysis. This talk will focus on our use of Lyapunov analysis and phase-diagrams to explore which initial cellular states result in cell death or survival following treatment with death ligands and which regions of phase-space show homogeneous (robust) or heterogeneous (sensitive) cell behavior.
Protein domain evolution is constrained by? network robustness to rate constant changes.
Not available
The Control of Gene Expression Noise in Embryonic Spatial Patterning
Fruit flies are models for understanding the genetic regulation involved in specifying the complex body plans of higher animals. The head-to-tail (anterior-posterior) axis of the fly (Drosophila) is established in the first hours of development. Maternally supplied factors form concentration gradients which direct embryonic (zygotic) genes where to be activated to express proteins. These protein patterns specify the positions and cell types of the body's tissues. Recent research has shown, comparing between embryos, that the zygotic gene products are much more precisely positioned than the maternal gradients, indicating an embryonic error reduction mechanism. Within embryos, there is the additional aspect that DNA and mRNA operate at very low copy number, and the associated high relative noise has the potential to strongly affect protein expression patterns. In recent work, we have focused on the noise aspects of positional specification within individual embryos, and what molecular mechanisms confer robustness to the process.

We simulate activation of hunchback (hb), a primary target of the maternal Bicoid (Bcd) protein gradient, which forms an expression pattern dividing the embryo into anterior and posterior halves. We use a master equation approach to simulate the stochastic dynamics of hb regulation, including the binding/unbinding of Bcd molecules at the hb promoter (the portion of DNA regulating hb transcription), hb transcription, subsequent translation to Hb protein, binding/unbinding of Hb at the promoter (self-regulation), and diffusion of the Bcd and Hb proteins. Model parameters were set by deterministically matching large scale pattern features for a series of experimental expression patterns: wild-type (WT) embryos; hb mutants lacking self-regulation; and constructs in which portions of the hb promoter were used to express a reporter gene (lacZ). The model was then solved stochastically to predict the noise output in these different experiments. In subsequent noise measurements we experimentally corroborated several predictions: including that mRNA is noisier than protein and that Hb self-regulation reduces noise.

Results indicate that WT (self-regulatory) Hb output noise is predominantly dependent on the transcription and translation dynamics of its own expression, and is uncorrelated with Bcd fluctuations. In the constructs and mutant, which lack self-regulation, we find that increasing the number and strength of Bcd binding sites (there are 6 in the core hb promoter) provides a rudimentary level of noise reduction. We have recently incorporated a known inhibitor of hb, Kr�ppel (Kr), into the model � preliminary indications are that the Hb-Kr activator-inhibitor dynamics increase precision at the mid-embryo boundary.

The analysis of hb shows common modes of gene regulation (e.g. multiple regulatory sites, self-regulation) that are involved in noise reduction, which can be applied generally to reproducibility and determinacy of spatial patterning in other developmental phenomena.
Evolution of Robustness formulated in terms of Phenotypic Variances
Kunihiko Kaneko, University of Tokyo, Research Center for Complex Systems Biology

Characterization of plasticity, robustness, and evolvability is an important issue in biology. First, proportionality among evolution speed, phenotypic plasticity, and isogenic phenotypic fluctuation is derived by borrowing and extending fluctuation-response relationship in physics. Following an evolutionary stability hypothesis we derive a general proportionality relationship between the phenotypic fluctuations of epigenetic and genetic origins; The former is given by the variance of phenotype due to noise in developmental process, and the latter due to genetic mutation. The relationship suggests a link between robustness to noise and to mutation, since robustness can be defined by the sharpness of the distribution of phenotype. Second, the proportionality between the variances is demonstrated to hold also over different phenotypic traits, when the system acquires robustness through the evolution. Third, adaptation to environmental variation is studied, which is shown to require a certain degree of phenotypic fluctuations. Indeed, the highest adaptability is achieved when the system is near the transition point to lose the robustness. Here, change in phenotypes (i.e., in the gene expression pattern) induced by environmental change is reduced later as a result of genetic evolution. All the obtained relationships are confirmed in models of gene expression dynamics, as well as in laboratory experiments. Based on our results, we revisit Waddington's canalization and genetic assimilation, and discuss how consistency between evolutionary and developmental scales constrains developmental process and leads to universal laws on phenotypic fluctuations.

References: K.K. Life: An Introduction to Complex Systems Biology, Springer (2006); PLoS One(2007) 2 e434, J Biosci.34 (2009) 529: BMC Evolutionary Biology 11(2011) 27; Ito et al., Mol. Sys. Biol. 5 (2009) 264
What price robustness?
There are at least three classes of question we can ask about the robustness of biological systems. "What are the strategies for robustness (i.e. what mechanisms make systems robust)?", "How does robustness evolve (i.e. what evolutionary mechanisms drive biological systems to become robust)?", and "What are the consequences of robustness (i.e. how does robustness, or the evolutionary paths that lead to it, constrain the organization and behavior of biological systems)? My talk concerns the last of these classes of question which, arguably, has been the least well explored. Work from several groups indicates that robustness may constrain biological systems in at least two ways: through trade-offs (whereby the need to overcome interference between strategies for achieving different types of robustness constrains system organization and drives the evolution of complexity), and through inadvertent selection for epistasis (whereby the evolution of robustness drives a net increase in the nonlinearity of interactions between system components). From a practical standpoint, it is not enough that we know that such constraints arise; we'd like to know when and where in any given system they will arise, and how large the effects will be. To this end, we have been building and analyzing models of developmental systems, exploring tradeoffs in pattern formation and growth control engendered by interference among strategies for parametric robustness, "microenvironmental" robustness, robustness to somatic mutation, etc. We have also been exploring models in which selection for robustness drives increased synergistic epistasis, in order to learn how the combinatorial fragility that such an effect produces is distributed among the different components of a network. Among other things, the results of such studies have direct bearing on our expectations for the kinds of genetic interactions and allele frequencies that underlie human genetic disease.
Robustness to gene expression errors, and the consequences for evolvability
Making genes into gene products is subject to predictable errors, each with a phenotypic effect that depends on a normally cryptic sequence. The distribution of fitness effects of these cryptic sequences, like that of new mutations, is bimodal. For example, a cryptic sequence might be strongly deleterious if it causes protein misfolding, or it might have only a minor effect if it preserves the protein fold and tweaks function. Few sequences have effect sizes that fall in between.

Strongly deleterious sequences can be subject to some selection even while they are cryptic, and expressed only at low levels that depend on a molecular error. Robustness to the potentially deleterious effects of cryptic sequences can be achieved globally by avoiding making errors (e.g., via proofreading machinery) or locally by ensuring that each cryptic sequence has a relatively benign effect. The local solution requires powerful selection acting on every cryptic site, and so evolves only in large populations. Small populations with less effective selection evolve global proofreading solutions. However, we also find that for a large range of realistic intermediate population sizes, the evolutionary dynamics are bistable and either solution may result. The local solution, which does not occur in very small populations, facilitates the co-option of cryptic sequences and therefore substantially increases evolvability. This can occur even in genetically uniform populations, illustrating that neighbourhood richness or "quality" in genotype space can be more important to evolvability than the quantity of genotypes in a population spread across that space.
Analysis of context-dependent stochastic phenotypes in HIV-1 latency
Analysis of context-dependent stochastic phenotypes in HIV-1 latency.
Robustness of the Cell Signaling Network as a Means to Discriminate Among the Di erent Models of Itk Kinase Regulation in T cells
The process that warrants the generation of self tolerant peripheral T cells is called the thymocyte selection. During this maturation process, the overtly self reactive as well as unresponsive thymocytes are deleted from the cell population. The thymocytes equipped with T cell receptors (TCRs), capable of responding moderately to the self peptides are allowed to survive. Recently water soluble second messenger, inositol(1,3,4,5) tetrakisphosphate (IP4), has been implicated to play a crucial role in thymocyte positive selection (Huang et al.). It has been suggested that these IP4 molecules regulate the transient activation of the Tec- family protein tyrosine kinase Itk through a competing positive and negative feedbacks. The exact molecular mechanism involved in this feedback is however unclear. It is possible to con- struct more than one model with contrasting molecular mechanisms to explain the present body of experimental observations. This calls for criteria to choose among these models.

Robustness in face of the variation of the parameters in a model has been ubiquitously used as a criterion for model discrimination. Here we have used the maximum entropy, calculated with the constraints imposed by the experiments as a measure of robustness. Our data indicates that the models which are maximally robust share a cooperative allosteric mode of Itk regulation involving dimeric PH domains.

Joint work with Stephanie Rigaud, S. Seok, Agnieszka Prochenka, Guo Fu, Michael Dworkin, Nicholas R. J. Gascoigne, Veronika J. Vieland, Karsten Sauer, and Jayajit Das.
Detection, evasion, robustness and redundancy in plant-pathogen interactions
Plants and pathogens are engaged in ongoing battles of detection and evasion, which are recognized to be crucial in driving their coevolution. Simple genetic models of plant-pathogen interactions have been examined for decades, but recent experimental results point to a rich interplay involving partially redundant pathogen effector repertoires, sector-switching plant defense networks, convergent evolution of pathogen effector targeting, and interactions among guards, guardees and decoys. Robustness and evolvability of biological systems are increasingly recognized to be supported in part by nontrivial geometries in high-dimensional configuration spaces that span the many levels of organization from genotype to phenotype. Building on some previous work in characterizing the shapes of such spaces and their implications for biological function and evolution, I am exploring the geometric structure of detection and evasion in models of plant-pathogen interactions, and its ramifications for robustness, redundancy, and plant-pathogen coevolution.
Robust completion time distributions in complex biological networks
Ilya Nemenman, Physics, Emory University

Biochemical processes typically involve huge numbers of individual reversible steps, each with its own dynamical rate constants. For example, kinetic proofreading processes rely upon numerous sequential reactions in order to guarantee the precise construction of specific macromolecules. I will present a characterization of the first passage (completion) time distributions for such processes. I will argue that, for a wide class of biochemical kinetics systems related to kinetic proofreading, the completion time time behavior simplifies as the system size grows: it becomes either deterministic or exponentially distributed, with a very narrow transition between the two regimes. In both regimes, the dynamical complexity of the full system is trivial compared to its apparent structural complexity. This robust simplification of completion time distributions is independent of many microscopic details of the signaling systems and can be utilized for efficient control of cellular response properties. Even further simplifications are possible when one considers dynamics of many coupled kinetic proofreading enabled receptors, which can attain low activation noise with robust mean time to activation.
Noise Attenuation and Spatial Dynamics in Biological Systems
Qing Nie, Departments of Mathematics and Biomedical Engineering Center for Complex Biological Systems, Center for Mathematical and Computational Biology University of California, Irvine

The focus of the talk will be on stochastic effects for spatial dynamics of complex biology systems. Through modeling and simulations, we will study several general principles underlining noise attenuation and robustness in multiscale systems involving both intracellular and extracellular components. For several cases, existing and new experimental evidences that support our theoretical and computational results will also be presented.
Multiple and Diverse Homeostatic Mechanisms in a Complex Metabolic Network
Cells accomplish a great diversity of biochemical and molecular functions and many of these must be performed simultaneously. Metabolic systems are subject to large and irregular hourly and daily fluctuations in inputs, and are subject to continually changing demands for particular metabolites. Since many metabolic pathways share enzymes, metabolites and cofactors, there must be mechanisms that ensure that large variation in a particular region of a pathway does not compromise functions elsewhere.

We study these questions using a physiologically-based mathematical model for folate-mediated one-carbon metabolism. This complex network provides the first steps in nucleotide synthesis, and the synthesis of S-adenosylmethionine, the universal donor of methyl groups for a host of methyl transfer reactions such as DNA and histone methylation, and the synthesis of glutathione, the universal antioxidant in animals.

Regulation of stability in this network has features that resemble physiological homeostasis. Homeostasis of critical biological functions requires a surprisingly diverse set of dynamic biochemical regulatory mechanisms which cannot be deduced from examination of the biochemical reaction diagram. We have elucidated ten different homeostatic mechanisms that operate simultaneously to stabilize function against fluctuations in input and demand. The homeostasis of critical biological functions (e.g., methylation capacity, nucleotide synthesis, response to oxidative stress) is not the consequence of a stable steady-state, but instead requires continuous large, adaptive changes in some fluxes and metabolites.

Work done in collaboration with Mike Reed.
Discussion
Discussion led by Frederik Nijhout
The robustness continuum: yeast cells hedge their bets against unpredictable environmental change
Genetically identical cells grown in the same culture display striking cell-to-cell heterogeneity in gene expression and other traits. A crucial challenge is to understand how much of this heterogeneity reflects the noise tolerance of a robust system and how much serves a biological function. In some circumstances, heterogeneous traits might be favored over robust ones. For example, in bacteria cell-to-cell heterogeneity can serve as a bet-hedging mechanism, allowing a few cells to survive acute antibiotic stress while the others perish. Where a population of organisms falls on the continuum from uniformity to bet-hedging depends on the environmental regime it experiences. We describe a bet-hedging phenomenon in the yeast Saccharomyces cerevisiae, which occupies a range of natural and human-associated environments. We use a novel, high-throughput microscopy assay that monitors variable protein expression, growth rate and survival outcomes of tens of thousands of yeast microcolonies simultaneously. Clonal yeast populations display broad distributions of growth rates, and slow growth predicts resistance to acute heat stress. Expression of Tsl1, a trehalose-synthesis regulator, marks slow-growing cells and contributes to this resistance. I will present these results and discuss them in the context of the evolutionary forces shaping robust and heterogeneous traits.
Developmental selection as a mechanism of robustness: implications for genetic assimilation and life history evolution
Not available.
Biological Robustness: Implications for Systems Identification
Widespread robustness of cellular networks, in principle, could make the systems biology task of identifying and characterizing cellular networks difficult because it implies potentially large uncertainties in parameters or topologies of mathematical models that quantitatively capture the network behavior. Yet, robustness may be exploited in systems identification as an additional criterion for the plausibility of a given model. In both cases, quantitative characterizations of system robustness to perturbations are needed. This talk focuses on implications of robustness on systems identification with a focus on metabolic networks. Using the network structure and assumptions on optimal rejection of perturbations alone, we developed a method to analyze sensitivities in metabolic reaction networks that can be directly linked to, for instance, variability in gene expression. In an application to global data sets on the reaction of Bacillus subtilis to changes in nutrient source, the method revealed qualitatively different, large-scale responses to apparently simple, symmetric perturbations.

Model-based analysis enabled us to interpret the qualitative control scheme in an evolutionary context, and to reason about optimality of the bacterium's responses. In extending the sensitivity-based approach, we propose a method that infers possible structures of control mechanisms for metabolic networks directly by analyzing trade-offs between robust control and its associated costs, to yield experimentally testable predictions on transcription factor - gene interactions. General methods for quantification of robustness, in addition, are becoming feasible by using probabilistic approaches, which may ultimately provide principled insight into the role of robustness for the identification of cellular systems.
Points of sensitivity in circadian cells and networks
Daily behaviors of mammals are regulated by the so-called "master" circadian clock in the hypothalamic suprachiasmatic nucleus. It is composed of thousands of neurons, each of which contains the transcriptional feedback loops that are the clockworks. Isolated, these neurons show low-amplitude or no oscillations. In the tissue, they show high-amplitude, coordinated rhythms. The intercellular signaling network transforms the behavior of neurons from sloppy to precise oscillators. For the clock to function, individual cells must correctly adjust their phase and amplitude to signals and the tissue as a whole must arrive at a predictable consensus of in-phase oscillations at a period of nearly 24 hours. Thus, robust oscillations of the clock as a whole depend on features both of the cells and of the network.

In this talk, I will present analyses of data taken from SCN slices and relate it to simulation and analyses of mathematical (ordinary differential equation) models of the SCN. I will discuss oscillator-specific measures of sensitivity and relate them to network behaviors. For example, when the phase response magnitude of each cell is dynamic (higher magnitude when the cell is lower amplitude), networks of cells synchronize quickly.
Robustness and evolvability in RNA viruses
Laboratory experiments with RNA bacteriophage phi-6 were used to examine the evolution of mutational robustness (phenotypic constancy in the face of mutation input), and the relationship between robustness and evolvability (adaptive potential). We previously showed that selection for robustness was relaxed under virus co-infection, because complementation between virus genotypes automatically buffers mutational effects. Recent results showed that lineages founded by robust viruses evolved faster than those initiated by their brittle (non-robust) counterparts, in novel environments where heat-shock survival is an adaptive challenge; these data demonstrated a positive link between robustness and evolvability. Here we show that the phenomenon is bi-directional: prior selection for heat-shock survival caused viruses to evolve robustness as a correlated trait. This tight relationship between robustness and heat-shock evolvability may be explained by a single amino-acid substitution in the lysis enzyme that governs virus entry/exit from the cell. A combination of structural-biology, crystallography and fitness data suggested that the mutant enzyme was favored under heat shock, but deleterious under benign conditions. Using a systems-biology approach, we discuss why the phi-6 lysis-enzyme gene may be a global robustness regulator for the virus.
Robustness and the space of possible metabolisms
Not available
Robustness and Intragenomic Conflict
When natural selection is acting at the level of the the individual phenotype, we expect selection to favor more robust phenotypes. However, selection acting at the level of the gene can undermine adaptation of the individual organism, and lead to the fixation of suboptimal traits. Genomic imprinting, the phenomenon where the pattern of expression of an allele depends on its parental origin, is thought to result from and evolutionary intragenomic conflict, where maternally and paternally inherited alleles favor different optimal phenotypes. Using a simple model, I will illustrate how this can lead to the systematic unravelling of phenotypic robustness.
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Robust completion time distributions in complex biological networks
Ilya Nemenman Ilya Nemenman, Physics, Emory University

Biochemical processes typically involve huge numbers of individual reversible steps, each with its own dynamical rate constants. For example, kinetic proofreading processes rely upon numerous se

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Sex, Robustness, and Evolvability
Ricardo Azevedo Evolution is the movement of populations through a space of genotypes. This space can be modeled as an undirected network connecting genotypes that can be reached through mutation. In this view, the mutational robustness of a genotype is the proporti

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Robustness and Intragenomic Conflict
Jon Wilkins When natural selection is acting at the level of the the individual phenotype, we expect selection to favor more robust phenotypes. However, selection acting at the level of the gene can undermine adaptation of the individual organism, and lead to th

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Systematic identification of topologically essential interactions in regulatory networks
Maxim Artyomov Screens monitoring the effects of deletion, knock-down or over expression of regulatory genes on the expression of their target genes are critical for deciphering the organization of complex regulatory networks. However, since perturbation assays can

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The Control of Gene Expression Noise in Embryonic Spatial Patterning
David Holloway Fruit flies are models for understanding the genetic regulation involved in specifying the complex body plans of higher animals. The head-to-tail (anterior-posterior) axis of the fly (Drosophila) is established in the first hours of development. Mate

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The robustness continuum: yeast cells hedge their bets against unpredictable environmental change
Mark Siegal Genetically identical cells grown in the same culture display striking cell-to-cell heterogeneity in gene expression and other traits. A crucial challenge is to understand how much of this heterogeneity reflects the noise tolerance of a robust system

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Robustness to gene expression errors, and the consequences for evolvability
Joanna Masel Making genes into gene products is subject to predictable errors, each with a phenotypic effect that depends on a normally cryptic sequence. The distribution of fitness effects of these cryptic sequences, like that of new mutations, is bimodal. For e