Regulatory mechanisms for input-output curves in cell signaling

LAILA KAZIMIERSKI; ALEJANDRO COLMAN-LERNER; ALEJANDRA C VENTURA

Buenos Aires

Workshop; XV Giambiagi Winter School, "Information processing in biological sytems, from cells to equations and back".; 2013

Departamento de Física, FCEyN, UBA

An important characteristic of every living cell is its ability to communicate with thesurrounding environment. This exchange of information is called cellular signaling and isbased on the capacity of the cell to give proper responses to environmental signals.Mathematical/computational modeling based on biological information can be used toimprove 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 different 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 casesand, despite this apparent saturation of the sensors, the system can generatedistinguishable responses for apparently undistinguishable signals.In previous work we have presented a mechanism, pre-equilibrium signaling (PES)that enables cells to discriminate levels of signals that saturate receptors at equilibrium.The mechanism is based on the coupling of a slow sensing process compared to the timescale of the downstream processes. The immediate consequence of this coupling is tocause an expansion of the system´s dynamic range (range of stimuli to which the systemcan respond in a dose-dependent manner). Despite the diversity of possible biochemical networks, it may be common to find that onlya finite set of core topologies can execute a particular function. In this work we studybiochemical networks with minimum number of components and minimum number ofconnections, to identify which of them are capable of using the PES mechanism. Ourmain goal with this study is to extract general design principles, meaning the rules thatunderlie what networks can achieve PES. These design rules provide a framework forfunctionally classifying complex natural networks and a manual for engineeringnetworks.