Bayesian Inference for Simple Genetic Regulatory Networks

• Activity Number: 98
• Type: Topic Contributed
• Date/Time: Monday, August 12, 2002 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Stat. Sciences*
• Author(s): Richard Boys*+ and Darren Wilkinson and Wan Ng and Thomas Kirkwood
• Affiliation(s): Newcastle University and Newcastle University and Newcastle University and Newcastle University
• Keywords: continuous-time processes ; genetic circuits ; MCMC methods ; stochastic kinetics

Abstract

It is generally acknowledged that the molecular mechanisms regulating key cellular processes, such as gene expression, are intrinsically stochastic. The random diffusion of cell-signalling molecules and the combinatorial assembly of transcription factor complexes provide extensive opportunities for the action of chance. In recent years stochastic regulatory network models have been developed, based on discrete-event simulation techniques for generating realizations from the complex continuous-time, countable-state Markov processes governing the reaction systems. These models contain many parameters with uncertain values. In addition, the latent process can only be observed partially, and at discrete time intervals. Inference for such Markov process models is an extremely challenging problem.

This talk will describe the techniques used to model regulatory networks, and the computational tools needed for simulation and analysis. An overview will also be given of the MCMC algorithms, which can in principle be used for carrying out Bayesian inference for the parameters underlying the network models and the problems associated with applying such techniques in practice.