q-bio 2016

Posted by admin on July 13, 2016

Using the accurate chemical master equation (ACME) modeling method to study stochastic viral dynamics

The ACME provides an efficient and optimal algorithm for enumerating state spaces and directly solving the discrete Chemical Master Equations (dCME) for modeling stochastic biological networks in systems biology. Instead of running millions of stochastic simulation trajectories using Gillespie's algorithm (Gillespie, 1977 JCP), the ACME can accurately 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 ACME 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 PNAS). The ACME can be used 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 use the ACME method to study the stochastic controls in HIV intracellular circuits during initial HIV infection.

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