An equation-free probabilistic steady-state approximation: Dynamic application to the stochastic simulation of biochemical reaction networks

2005 ◽  
Vol 123 (21) ◽  
pp. 214106 ◽  
Author(s):  
Howard Salis ◽  
Yiannis N. Kaznessis
Keyword(s):  
Steady State ◽  
Stochastic Simulation ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Biochemical Reaction Networks ◽  
State Approximation ◽  
Steady State Approximation

Multi-objective steady state optimization of biochemical reaction networks using a constrained genetic algorithm

2008 ◽  
Vol 32 (8) ◽  
pp. 1707-1713 ◽  
Author(s):  
Hannes Link ◽  
Julio Vera ◽  
Dirk Weuster-Botz ◽  
Néstor Torres Darias ◽  
Ezequiel Franco-Lara
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Biochemical Reaction Networks ◽  
Multi Objective

scholarly journals Stochasticity in multi-phosphorylation and quasi steady state approximation in stochastic simulation

2018 ◽  
Author(s):  
S. Das ◽  
D. Barik
Keyword(s):  
Steady State ◽  
Stochastic Simulation ◽  
Chemical Reactions ◽  
Quasi Steady State ◽  
Chemical Noise ◽  
State Approximation ◽  
Steady State Approximation ◽  
Qualitative Nature ◽  
Multistep Reactions ◽  
Simulation Scheme

AbstractQuantitative and qualitative nature of chemical noise propagation in a network of chemical reactions depend crucially on the topology of reaction networks. Multisite reversible phosphorylation-dephosphorylation of target proteins is one such recurrently found topology in various cellular networks regulating key functions in living cells. Here we analytically calculated the stochasticity in multistep reversible chemical reactions by determining variance of phosphorylated species at the steady state using linear noise approximation. We investigated the dependence of variance on the rate parameters in the reaction chain and the number of phosphorylation sites on the species. Assuming a quasi steady state approximation on the multistep reactions, originating from the disparity in time scales in the network, we propose a simulation scheme for coupled chemical reactions to improve the computational efficiency of stochastic simulation of the network. We performed case studies on signal transduction cascade and positive feedback loop with bistability to show the accuracy and efficiency of the method.

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