Purely stochastic binary decisions in cell signaling models without underlying deterministic bistabilities

@article{citeulike:2255308, abstract = {Detection of different extracellular stimuli leading to functionally distinct outcomes is ubiquitous in cell biology, and is often mediated by differential regulation of positive and negative feedback loops that are a part of the signaling network. In some instances, these cellular responses are stimulated by small numbers of molecules, and so stochastic effects could be important. Therefore, we studied the influence of stochastic fluctuations on a simple signaling model with dueling positive and negative feedback loops. The class of models we have studied is characterized by single deterministic steady states for all parameter values, but the stochastic response is bimodal; a behavior that is distinctly different from models studied in the context of gene regulation. For example, when positive and negative regulation is roughly balanced, a unique deterministic steady state with an intermediate value for the amount of a downstream signaling product is found. However, for small numbers of signaling molecules, stochastic effects result in a bimodal distribution for this quantity, with neither mode corresponding to the deterministic solution; i.e., cells are in "on" or "off" states, not in some intermediate state. For a large number of molecules, the stochastic solution converges to the mean-field result. When fluctuations are important, we find that signal output scales with control parameters "anomalously" compared with mean-field predictions. The necessary and sufficient conditions for the phenomenon we report are quite common. So, our findings are expected to be of broad relevance, and suggest that stochastic effects can enable binary cellular decisions. 10.1073/pnas.0706110104}, author = {Artyomov, Maxim N. and Das, Jayajit and Kardar, Mehran and Chakraborty, Arup K. }, citeulike-article-id = {2255308}, doi = {http://dx.doi.org/10.1073/pnas.0706110104}, journal = {Proceedings of the National Academy of Sciences}, keywords = {bistability, desicion\_making, noise}, month = {November}, number = {48}, pages = {18958--18963}, posted-at = {2008-03-27 18:28:45}, priority = {2}, title = {Purely stochastic binary decisions in cell signaling models without underlying deterministic bistabilities}, url = {http://dx.doi.org/10.1073/pnas.0706110104}, volume = {104}, year = {2007} }

TY - JOUR ID - citeulike:2255308 L3 - citeulike-article-id:2255308 N2 - Detection of different extracellular stimuli leading to functionally distinct outcomes is ubiquitous in cell biology, and is often mediated by differential regulation of positive and negative feedback loops that are a part of the signaling network. In some instances, these cellular responses are stimulated by small numbers of molecules, and so stochastic effects could be important. Therefore, we studied the influence of stochastic fluctuations on a simple signaling model with dueling positive and negative feedback loops. The class of models we have studied is characterized by single deterministic steady states for all parameter values, but the stochastic response is bimodal; a behavior that is distinctly different from models studied in the context of gene regulation. For example, when positive and negative regulation is roughly balanced, a unique deterministic steady state with an intermediate value for the amount of a downstream signaling product is found. However, for small numbers of signaling molecules, stochastic effects result in a bimodal distribution for this quantity, with neither mode corresponding to the deterministic solution; i.e., cells are in "on" or "off" states, not in some intermediate state. For a large number of molecules, the stochastic solution converges to the mean-field result. When fluctuations are important, we find that signal output scales with control parameters "anomalously" compared with mean-field predictions. The necessary and sufficient conditions for the phenomenon we report are quite common. So, our findings are expected to be of broad relevance, and suggest that stochastic effects can enable binary cellular decisions. 10.1073/pnas.0706110104 IS - 48 JF - Proceedings of the National Academy of Sciences EP - 18963 TI - Purely stochastic binary decisions in cell signaling models without underlying deterministic bistabilities VL - 104 SP - 18958 KW - bistability KW - desicion_making KW - noise AU - Artyomov, Maxim AU - Das, Jayajit AU - Kardar, Mehran AU - Chakraborty, Arup PY - 2007/11/27/ UR - http://dx.doi.org/10.1073/pnas.0706110104 ER -