Probabilistic Approximations of Signaling Pathway Dynamics

Systems of ordinary differential equations (ODEs) are often used to model the dynamics of complex biological pathways. We construct a discrete state model as a probabilistic approximation of the ODE dynamics by discretizing the value space and the time domain. We then sample a representative set of trajectories and exploit the discretization and the structure of the signaling pathway to encode these trajectories compactly as a dynamic Bayesian network. As a result, many interesting pathway properties can be analyzed efficiently through standard Bayesian inference techniques. We have tested our method on a model of EGF-NGF signaling pathway [1] and the results are very promising in terms of both accuracy and efficiency.

• Type: Conference
• Year: 2009
• Where: CMSB
• Authors: Bing Liu, P. S. Thiagarajan, David Hsu
• Keywords: Applied Computing, CMSB 2009, EGF-NGF Signaling Pathway, Ordinary Differential Equations, Signaling Pathway