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.


CONCLUSIONS
We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.
Langley
Pat
P
Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA 94305, USA. langley@csli.stanford.edu
Shiran
Oren
O
Shrager
Jeff
J
Todorovski
Ljupco
L
Pohorille
Andrew
A
D000465:Algorithms; D001671:Biochemistry / Q000379:methods; D007700:Kinetics; D019359:Knowledge; D008954:Models, Biological; D016571:Neural Networks, Computer; D010788:Photosynthesis