P.S. Thiagarajan 

NUS

Title: Probabilistic Approximations of ODEs Based Bio-pathways Dynamics

Abstract: A system of Ordinary Differential Equations (ODEs) is often
used to model the dynamics of a bio-chemical network. Such systems are
difficult to analyze. To get around this, we construct a discrete
probabilistic approximation of the ODE dynamics as a dynamic Bayesian
network. Consequently, pathway properties can be analyzed using standard
Bayesian inference techniques. We have tested our method on a number of
pathways models. We have also carried out a combined computational and
experimental study of the complement system under inflammation
conditions.  The results we obtain are very promising in terms of both
accuracy and efficiency. Further, our method appears to be amenable to a
fast GPU based implementation.