title: Constructing explanatory process models from biological data and knowledge.
abstract: 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.
authors.affiliation: Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA 94305, USA. langley@csli.stanford.edu
authors.forename: Pat
authors.initials: P
authors.lastname: Langley
authors.forename: Oren
authors.initials: O
authors.lastname: Shiran
authors.forename: Jeff
authors.initials: J
authors.lastname: Shrager
authors.forename: Ljupco
authors.initials: L
authors.lastname: Todorovski
authors.forename: Andrew
authors.initials: A
authors.lastname: Pohorille
mesh_terms: D000465:Algorithms; D001671:Biochemistry / Q000379:methods; D007700:Kinetics; D019359:Knowledge; D008954:Models, Biological; D016571:Neural Networks, Computer; D010788:Photosynthesis