q-bio 2015

Posted by admin on July 16, 2015

Using the Finite Buffer method (fb-dCME) to model stochastic viral dynamics

The Finite Buffer method (fb-dCME) provides an efficient and optimal algorithm for enumerating state spaces and directly solving the fundamental discrete Chemical Master Equations for modeling stochastic gene regulatory networks in systems biology. Instead of running millions of stochastic simulation trajectories using Gillespie's algorithm (Gillespie, 1977), the Finite Buffer method can accurately and efficiently capture the stochastic dynamics including important rare events in biological networks by directly solving the steady state and time-evolving probability landscapes for the underlying dCME. The fb-dCME method has been successfully applied to study important biological processes and identify key interactions in complex regulatory network, such as the cell fate determination and switching efficiency and stability issues in the epigenetic circuits of phage lambda, a virus to E. coli cell (Cao et al. 2010). The fb-dCME can be applied to study broad issues in systems biology, such as the regulation of stem cell development and differentiation, and cell cancerogenesis. In this session, we will apply the fb-dCME method to model the stochastic viral dynamics during initial HIV infection. We will build stochastic viral dynamics models to study the continuous and burst virion production processes, respectively. All models will be encoded in SBML format.

SBML file for the bistable Schlogl model
SBML file of the stochastic viral dynamics model continuous viral production
SBML file of the stochastic viral dynamics model with burst viral production
Initial state file of the stochastic viral dynamics model