Constructing explanatory process models from biological data and knowledge

doi:10.1016/j.artmed.2006.04.003

Constructing explanatory process models from biological data and knowledge

2006-07-01

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AI

DOI：10.1016/j.artmed.2006.04.003

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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. (C) 2006 Published by Elsevier B.V.

Keywords:

computational scientific discovery

inductive process modeling

photosynthesis regulation

biochemical kinetic reactions

Constructing explanatory process models from biological data and knowledge

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