Regulatory mechanisms for input-output curves in cell signaling

LAILA KAZIMIERSKI; ALEJANDRO COLMAN-LERNER; ALEJANDRA C VENTURA

Villa Carlos Paz, Córdoba

Workshop; XIII Latin American Workshop on Nonlinear Phenomena; 2013

An important characteristic of every living cell is its ability to communicate with the surrounding environment. This exchange of information is called cellular signaling and is based on the capacity of the cell to give proper responses to environmental signals. Mathematical/computational modeling based on biological information can be used to improve our understanding of cellular signaling, thereby enhancing predictive accuracy. Intracellular signaling networks have sensing mechanisms such as membrane receptors, responsible for converting extracellular stimuli in receptor activation. This generates network activity, which is composed of dierent steps of transduction, and generates a response to the stimulus. It is commonly accepted that stimulation levels that cause maximum receptor occupancy cannot be distinguished from each other; all produce the same saturated response. However, it has been experimentally observed that, in many cases and, despite this apparent saturation of the sensors, the system can generate distinguishable responses for apparently undistinguishable signals. In previous work we have presented a mechanism, pre-equilibrium signaling (PES) that enables cells to discriminatelevels of signals that saturate receptors at equilibrium. The mechanism is based onthe coupling of a slow sensing process compared to the time scale of the downstream processes. The immediate consequence of this coupling is to cause an expansion of the system´s dynamic range (range of stimuli to which the system can respond in a dose-dependent manner). Despite the diversity of possible biochemical networks, it may be common to nd that only a nite set of core topologies can execute a particular function. In this work we study biochemical networks with minimum number of components and minimum number of connections, to identify which of them are capable of using the PES mechanism. Our main goal with this study is to extract general design principles, meaning the rules that underlie what networks can achieve PES. These design rules provide a framework for functionally classifying complex natural networks and a manual for engineering networks.