Genetically identical cells are experimentally proven to exhibit considerable celltocell variations in mRNA and protein levels. Such variability of phenotypes is driven by both the intrinsic noise, characterized by gene network structure, and the extrinsic noise, which acts globally on a single cell but varies from one to another. For intrinsic noise, mathematical and computational biologists have been developing and improving simulation techniques to investigate the stochastic network under strict parameters, and discovered interesting behaviours, like noiseinduced multistability and oscillations.
However, global effect of extrinsic noise raises a few more questions: how does the internal stochastic behavior change with respect to some perturbations of biophysical parameters? How may we define, quantify and predict these changes under stochastic context? Do these changes in a single cell apply similarly to a collection of cells? If not, how to identify the role of intrinsic and extrinsic noise in generating the variations in the experimental observations?


Under deterministic context, all these questions falls into the subject of bifurcation analysis, a theory describing the dependence of the steady state on continuous changes in parameters. But stochastic modeling, by contrast, is still lagging behind concerning appropriate parametric methods. The commonly used Monte Carlo formulation becomes inefficient in parametric analysis, as it requires separate simulations for different parameter combinations. On the other hand, equationbased models, like chemical master equation and other continuous approximation, suffers from the socalled ‘curse of dimensionality’.
In the StoBifAn project, we want to develop novel, computational tools, with userfriendly interfaces, for analysing complex biochemical regulatory systems in general, and their stochastic bifurcation structure in particular. Our ultimate results will be twofold:
 We create and distribute methods and software that supports modellers of biochemical reaction pathways. Our tools will include efficient (multiscale) stochastic simulator, automatic parameter estimator, sensitivity and bifurcation analyser.
 And, we build up a unique synergy between biologists and computational mathematicians. By inventing advanced computing tools, mathematicians can facilitate knowledge discovery for biologist from simulationbased modelling. With appropriate guidance from biologist, mathematicians can develop methodologies that are more worthwhile.