Component-based construction of bio-pathway models: the parameter estimation problem

Abstract

Constructing and analyzing large biological pathway models is a significant challenge. We propose a general approach that exploits the structure of a pathway to identify pathway components, constructs the component models, and finally assembles the component models into a global pathway model. Specifically, we apply this approach to pathway parameter estimation, a main step in pathway model construction. A large biological pathway often involves many unknown parameters and the resulting high-dimensional search space poses a major computational difficulty. By exploiting the structure of a pathway and the distribution of available experimental data over the pathway, we decompose a pathway into components and perform parameter estimation for each component. However, some parameters may belong to multiple components. Independent parameter estimates from different components may be in conflict for such parameters. To reconcile these conflicts, we represent each component as a factor graph, a standard probabilistic graphical model. We then combine the resulting factor graphs and use a probabilistic inference technique called belief propagation to obtain the maximally likely parameter values that are globally consistent. We validate our approach on a synthetic pathway model based on the Akt-MAPK signaling pathways. The results indicate that the approach can potentially scale up to large pathway models.