Motivation: Many biochemical networks involve reactions localized on the cell membrane. This can give rise to spatial gradients of the concentration of cytosolic species. Moreover, the number of membrane molecules can be small and stochastic effects can become relevant. Pathways usually consist of a complex interaction network and are characterized by a large set of parameters. The inclusion of spatial and stochastic effects is a major challenge in developing quantitative and dynamic models of pathways. Results: We have developed a particle-based spatial stochastic method (GMP) to simulate biochemical networks in space, including fluctuations from the diffusion of particles and reactions. Gradients emerging from membrane reactions can be resolved. As case studies for theGMPmethod we used a simple gene expression system and the phosphoenolpyruvate:glucose phosphotransferase system pathway.

Oxford U.P.
Mathematics and Computation for the System Biology of Cells
Multiscale Dynamics

Vidal Rodríguez, J, Dobrzynski, M, Kaandorp, J.A, & Blom, J.G. (2006). Spatial stochastic modelling of the phosphoenolpyruvatedependentphosphotransferase (PTS) pathway in Escherichia coli. Bioinformatics, 22(15), 1895–1901.