Stochastic modeling of cytoplasmic reactions in complex biological systems

Authors:
Preetam Ghosh;Samik Ghosh;Kalyan Basu;Sajal Das;Simon Daefler
Affiliations:
Dept. of Computer Science and Engineering, The University of Texas at Arlington;Dept. of Computer Science and Engineering, The University of Texas at Arlington;Dept. of Computer Science and Engineering, The University of Texas at Arlington;Dept. of Computer Science and Engineering, The University of Texas at Arlington;Division of Infectious Diseases, Mount Sinai School of Medicine
Venue:
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Year:
2006

Citing 3
Cited 3

Mathematical physiology

Mathematical physiology
Parallel Discrete Event Simulation in Star Networks with Application to Telecommunications

MASCOTS '95 Proceedings of the 3rd International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
iSimBioSys: A Discrete Event Simulation Platform for 'in silico' study of biological systems

ANSS '06 Proceedings of the 39th annual Symposium on Simulation

Parametric modeling of protein-DNA binding kinetics: A discrete event based simulation approach

Discrete Applied Mathematics
DNA sites buried in nucleosome become accessible at room temperature: a discrete-event-simulation based modeling approach

ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
A computationally fast and parametric model to estimate protein-ligand docking time for stochastic event based simulation

Transactions on computational systems biology VIII

Quantified Score

Hi-index	0.00

Visualization

Abstract

The use of “in silico” stochastic event based modeling can identify the dynamic interactions of different processes in a complex biological system. This requires the computation of the time taken by different events in the system based on their biological functions. One such important event is the reactions between the molecules inside the cytoplasm of a cell. We present a mathematical formulation for the estimation of the reaction time between two molecules within a cell based on the system state. We derive expressions for the average and second moment of the time for reaction to be used by our stochastic event-based simulation. Unlike rate equations, the proposed model does not require the assumption of concentration stability for multiple molecule reactions.