Darren J. Wilkinson

TL;DR: SBML Models Auto-regulatory network Lotka-Volterra reaction system Dimerisation-kinetics model Bayesian inference for latent variable models Alternatives to MCMC Inference for Stochastic Kinetic Models Conclusion.

TL;DR: Stochastic models are being used increasingly in preference to deterministic models to describe biochemical network dynamics at the single-cell level to adequately describe observed noise, variability and heterogeneity of biological systems over a range of scales of biological organization.

TL;DR: Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but it is found here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem.

TL;DR: Three ontologies created specifically to address the needs of the systems biology community are described, including the Systems Biology Ontology, which provides semantic information about the model components, and the Kinetic Simulation Algorithm Ontology and the Terminology for the Description of Dynamics, which categorizes dynamical features of the simulation results and general systems behavior.

TL;DR: This paper explores how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model.