Title of article :
Antagonism and bistability in protein interaction networks
Author/Authors :
Sabouri-Ghomi، نويسنده , , Mohsen and Ciliberto، نويسنده , , Andrea and Kar، نويسنده , , Sandip and Novak، نويسنده , , Bela and Tyson، نويسنده , , John J.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2008
Abstract :
A protein interaction network (PIN) is a set of proteins that modulate one anotherʹs activities by regulated synthesis and degradation, by reversible binding to form complexes, and by catalytic reactions (e.g., phosphorylation and dephosphorylation). Most PINs are so complex that their dynamical characteristics cannot be deduced accurately by intuitive reasoning alone. To predict the properties of such networks, many research groups have turned to mathematical models (differential equations based on standard biochemical rate laws, e.g., mass-action, Michaelis–Menten, Hill). When using Michaelis–Menten rate expressions to model PINs, care must be exercised to avoid making inconsistent assumptions about enzyme–substrate complexes. We show that an appealingly simple model of a PIN that functions as a bistable switch is compromised by neglecting enzyme–substrate intermediates. When the neglected intermediates are put back into the model, bistability of the switch is lost. The theory of chemical reaction networks predicts that bistability can be recovered by adding specific reaction channels to the molecular mechanism. We explore two very different routes to recover bistability. In both cases, we show how to convert the original ‘phenomenological’ model into a consistent set of mass-action rate laws that retains the desired bistability properties. Once an equivalent model is formulated in terms of elementary chemical reactions, it can be simulated accurately either by deterministic differential equations or by Gillespieʹs stochastic simulation algorithm.
Keywords :
Michaelis–Menten rate law , stochastic simulation , cell cycle control
Journal title :
Journal of Theoretical Biology
Journal title :
Journal of Theoretical Biology