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This video was recorded at Workshop on Probabilistic Modelling of Networks and Pathways, Sheffield 2007. It is common when modelling biochemical networks to use qualitative information such as the general ODE model structure so as to proceed in parameter estimation while at the same time retaining the basic model structure the best represents the biochemical process governing the cell. This is not the case however when the population of the available molecules from each of the participating species is very small (small copy number) deeming necessary the introduction of complex stochastic modelling techniques that make use of chemical master equations to simulate the trajectories of the states (species concentration) of the system [7]. Gene expression is stochastic by nature [7][5] and as a consequence gene regulatory and signal transduction networks follow a similar behaviour. Most importantly, a large number of gene expression data sets examined in yeast, mouse and human cells follow a Pareto-like distribution model skewed by many low-abundance transcripts, covering a large variety of eukaryotic cells [2]. It is therefore apparent that a stochastic modelling strategy should be structure so as to accommodate the specific needs of the system....

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