Incremental Signaling Pathway Modeling by Data Integration

Abstract

Constructing quantitative dynamic models of signaling pathways is an important task for computational systems biology. Pathway model construction is often an inherently incremental process, with new pathway players and interactions continuously being discovered and additional experimental data being generated. Here we focus on the problem of performing model parameter estimation incrementally by integrating new experimental data into an existing model. A probabilistic graphical model known as the factor graph is used to represent pathway parameter estimates. By exploiting the network structure of a pathway, a factor graph compactly encodes many parameter estimates of varying quality as a probability distribution. When new data arrives, the parameter estimates are refined efficiently by applying a probabilistic inference algorithm known as belief propagation to the factor graph. A key advantage of our approach is that the factor graph model contains enough information about the old data, and uses only new data to refine the parameter estimates without requiring explicit access to the old data. To test this approach, we applied it to the Akt-MAPK pathways, which regulate the apoptotic process and are among the most actively studied signaling pathways. The results show that our new approach can obtain parameter estimates that fit the data well and refine them incrementally when new data arrives.