ENISI SDE: A novel web-based stochastic modeling tool for computational biology

Abstract

Stochasticity is part of the nature of many biological processes and an important aspect in modeling and simulations of computational biology. Gillespie's algorithm was developed for modeling stochasticity of chemical reactions and has been broadly applied to stochastic modeling in computational biology. The Gillespie's algorithm accounts for the particle effect in chemical reactions. However, many biological processes including cellular and molecular immunological mechanisms have many other sources of stochasticity such as cell movement, ligand binding, or unaccounted variation in experimental settings. In this paper, we propose stochastic differential equations (SDE) being used as a generic stochastic modeling technique for systems immunology. SDE has been widely used in statistics and economics areas; but only a few isolated studies in computational biology are found using SDE to model stochastic behaviors of cells and molecules. In addition, to the best of our knowledge, there is no user-friendly SDE-based modeling tool available for computational biologists. This paper presents ENISI SDE, a web-based user-friendly stochastic modeling tool for computational biologists. This work provides three major contributions: (1) we discuss SDE and propose it as a generic approach for stochastic modeling in computational biology; (2) we develop ENISI SDE, a web-based user-friendly SDE modeling tool that only requires little extra effort beyond regular ODE-based modeling; (3) we use the model SDE modeling tool to study stochastic sources of cell heterogeneity in the context of a CD4+ T Cell differentiation process. The case study clearly shows the effectiveness of SDE as a stochastic modeling approach in biology in general and immunology in particular and the power of the SDE modeling tool we developed.