Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis Suzanne Gaudet 1,2. *, Sabrina L. Spencer 3.¤ , William W. Chen 3 , Peter K. Sorger 3 * 1 Department of Cancer Biology and Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, 2 Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America, 3 Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America Abstract Stochastic fluctuations in gene expression give rise to cell-to-cell variability in protein levels which can potentially cause variability in cellular phenotype. For TRAIL (TNF-related apoptosis-inducing ligand) variability manifests itself as dramatic differences in the time between ligand exposure and the sudden activation of the effector caspases that kill cells. However, the contribution of individual proteins to phenotypic variability has not been explored in detail. In this paper we use feature-based sensitivity analysis as a means to estimate the impact of variation in key apoptosis regulators on variability in the dynamics of cell death. We use Monte Carlo sampling from measured protein concentration distributions in combination with a previously validated ordinary differential equation model of apoptosis to simulate the dynamics of receptor-mediated apoptosis. We find that variation in the concentrations of some proteins matters much more than variation in others and that precisely which proteins matter depends both on the concentrations of other proteins and on whether correlations in protein levels are taken into account. A prediction from simulation that we confirm experimentally is that variability in fate is sensitive to even small increases in the levels of Bcl-2. We also show that sensitivity to Bcl-2 levels is itself sensitive to the levels of interacting proteins. The contextual dependency is implicit in the mathematical formulation of sensitivity, but our data show that it is also important for biologically relevant parameter values. Our work provides a conceptual and practical means to study and understand the impact of cell-to-cell variability in protein expression levels on cell fate using deterministic models and sampling from parameter distributions. Citation: Gaudet S, Spencer SL, Chen WW, Sorger PK (2012) Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor- Mediated Apoptosis. PLoS Comput Biol 8(4): e1002482. doi:10.1371/journal.pcbi.1002482 Editor: Richard Bonneau, New York University, United States of America Received September 2, 2011; Accepted February 29, 2012; Published April 26, 2012 Copyright: ß 2012 Gaudet et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by NIH grants CA139980 to PKS and SG and GM68762 to PKS (http://www.nih.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: suzanne_gaudet@dfci.harvard.edu (SG); peter_sorger@hms.harvard.edu (PKS) ¤ Current address: Department of Chemical and Systems Biology, Stanford University, California, United States of America. . These authors contributed equally to this work. Introduction Variability in the responses of tumor cells to biological stimuli is often ascribed to genetic differences. However, it has become increasingly clear that even genetically identical cells growing in a homogenous environment respond differently to ligands, drugs, or other stimuli. Non-genetic variability at the single-cell level has been demonstrated in the activation of immune responses [1,2,3,4], viral infectivity [5,6,7], developmental fate [8,9,10,11], antibiotic resistance [12], and sensitivity to therapeutic drugs [13,14,15]. Such variability can arise from relatively long-lasting ‘‘epigenetic’’ changes that have their origins in stable and heritable programs of gene expression [16] and can be sensitive to histone deactylase inhibitors that disrupt the histone code [14]. Substantial phenotypic variability also arises from fluctuation in the levels or activities of proteins (or other biomolecules) that control cell fate; the current paper is concerned with this type of variability. Two sources of non-genetic variability can be distinguished. The first, often called ‘‘intrinsic noise’’, arises when the copy number of molecules participating in a reaction under study is sufficiently small that probabilistic fluctuations in protein-protein interactions or biochemical reactions have observable effects [17]. Such processes are modeled using stochastic methods. The second source of variation, often called ‘‘extrinsic noise,’’ arises when protein concentrations in individual cells are high enough that single-cell reaction trajectories are well approximated by mass- action kinetics, but ‘‘external’’ or pre-existing cell-to-cell differ- ences in the activities or concentrations of biomolecules have an effect [17]. With either intrinsic or extrinsic noise, phenotypes vary from one cell to the next but the processes that cause cells to differ are either part of or external to the biological process under study. When clonal cell populations are treated with TNF-related apoptosis inducing ligand (TRAIL), their response is dramatically different from cell to cell: some cells die with 45 min, some die after as long as 12 hr, and some do not die at all [15,18]. We have investigated the contributions of intrinsic and extrinsic noise to this variability by studying sister cells [15]. Were cell-to-cell variability to arise predominantly from intrinsic noise, we would expect sister cells to be no more correlated phenotypically than two cells selected at random from a population: intrinsic noise cannot be PLoS Computational Biology | www.ploscompbiol.org 1 April 2012 | Volume 8 | Issue 4 | e1002482