A Short Note on the Exact Stochastic Simulation Scheme of the Hull-White Model and Its Implementation

2016 ◽  
Author(s):  
Christian P. Fries
Keyword(s):  
Stochastic Simulation ◽  
Short Note ◽  
Simulation Scheme ◽  
Exact Stochastic Simulation

scholarly journals Accurate Stochastic Simulation Methods for Homogeneous Biochemical Networks

2021 ◽  
Author(s):  
Farida Ansari
Keyword(s):  
Master Equation ◽  
Stochastic Simulation ◽  
Growth Factor Receptor ◽  
Stochastic Simulation Algorithm ◽  
Chemical Master Equation ◽  
Biochemical Networks ◽  
Random Time Change ◽  
Simulation Strategy ◽  
Epidermal Growth ◽  
Exact Stochastic Simulation

Stochastic models of intracellular processes are subject of intense research today. For homogeneous systems, these models are based on the Chemical Master Equation, which is a discrete stochastic model. The Chemical Master Equation is often solved numerically using Gillespie’s exact stochastic simulation algorithm. This thesis studies the performance of another exact stochastic simulation strategy, which is based on the Random Time Change representation, and is more efficient for sensitivity analysis, compared to Gillespie’s algorithm. This method is tested on several models of biological interest, including an epidermal growth factor receptor model.

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.

scholarly journals Fast Exact Stochastic Simulation Algorithms Using Partial Propensities

2010 ◽  
Author(s):  
Rajesh Ramaswamy ◽  
Ivo F. Sbalzarini ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
Ch. Tsitouras
Keyword(s):  
Stochastic Simulation ◽  
Stochastic Simulation Algorithms ◽  
Simulation Algorithms ◽  
Exact Stochastic Simulation

scholarly journals Accurate Stochastic Simulation Methods for Homogeneous Biochemical Networks

2021 ◽  
Author(s):  
Farida Ansari
Keyword(s):  
Master Equation ◽  
Stochastic Simulation ◽  
Growth Factor Receptor ◽  
Stochastic Simulation Algorithm ◽  
Chemical Master Equation ◽  
Biochemical Networks ◽  
Random Time Change ◽  
Simulation Strategy ◽  
Epidermal Growth ◽  
Exact Stochastic Simulation

Stochastic models of intracellular processes are subject of intense research today. For homogeneous systems, these models are based on the Chemical Master Equation, which is a discrete stochastic model. The Chemical Master Equation is often solved numerically using Gillespie’s exact stochastic simulation algorithm. This thesis studies the performance of another exact stochastic simulation strategy, which is based on the Random Time Change representation, and is more efficient for sensitivity analysis, compared to Gillespie’s algorithm. This method is tested on several models of biological interest, including an epidermal growth factor receptor model.

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