The systems biology is a recent scientific discipline that arose from the need to combine biology with mathematics, physics, chemistry and computer science. Partly driven by the availability of a morass of data and partly driven by the availability of computational resources, the field of systems biology was reborn few years ago. Recently, a number of computational methods have been developed to model cellular pathways and networks. One of the major issues in building mathematical models of cellular processes is the difficulty of estimating parameters. Due to a wide numerical range within which parameters operate, it takes a large number of iterations to find the biologically relevant values. In This work the author describes how grid technology can be used to salvage this situation and help in building robust in-silico models.