Constructing explanatory process models from biological data and knowledge

Objective We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. Methods 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. Results We demonstrate IPM’s use both on photosynthesis and on a second domain, biochemical kinetics, reporting the models induced and their fit to observations. Conclusion We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.

Authors

• Pat Langley Georgia Tech Research Institute Institute for the Study of Learning and Expertise
• Oren Shiran Pat Langley
• Jeff Shrager Tim Finin
• Ljupčo Todorovski
• Andrew Pohorille

Keywords

• Computational scientific discovery
• Inductive process modeling
• Photosynthesis regulation
• Biochemical kinetic reactions