Constructing explanatory process models from biological data and knowledge.

Abstract

We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input. We demonstrate IPM's use both on photosynthesis and on a second domain, biochemical kinetics, reporting the models induced and their fit to observations. We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.