title: Methods for simulating the dynamics of complex biological processes.
abstract: In this chapter, we provide the basic information required to understand the central concepts in the modeling and simulation of complex biochemical processes. We underline the fact that most biochemical processes involve sequences of interactions between distinct entities (molecules, molecular assemblies), and also stress that models must adhere to the laws of thermodynamics. Therefore, we discuss the principles of mass-action reaction kinetics, the dynamics of equilibrium and steady state, and enzyme kinetics, and explain how to assess transition probabilities and reactant lifetime distributions for first-order reactions. Stochastic simulation of reaction systems in well-stirred containers is introduced using a relatively simple, phenomenological model of microtubule dynamic instability in vitro. We demonstrate that deterministic simulation [by numerical integration of coupled ordinary differential equations (ODE)] produces trajectories that would be observed if the results of many rounds of stochastic simulation of the same system were averaged. In Section V, we highlight several practical issues with regard to the assessment of parameter values. We draw some attention to the development of a standard format for model storage and exchange, and provide a list of selected software tools that may facilitate the model building process, and can be used to simulate the modeled systems.
authors.affiliation: Biological and Neural Computation Group, Science and Technology Research Institute, University of Hertfordshire, College Lane, Hatfield AL10 9AB, United Kingdom.
authors.forename: Maria J
authors.initials: MJ
authors.lastname: Schilstra
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Martin
authors.forename: Sarah M
authors.initials: SM
authors.lastname: Keating
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007700:Kinetics; D008870:Microtubules / Q000737:chemistry; D008954:Models, Biological; D013269:Stochastic Processes; D013997:Time Factors