Constructing explanatory process models from biological data and knowledge

  • Authors:

  • Pat Langley;Oren Shiran;Jeff Shrager;Ljupčo Todorovski;Andrew Pohorille

  • Affiliations:

  • Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA 94305, USA;Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA 94305, USA;Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA 94305, USA;Department of Intelligent Systems, Jozef Stefan Institute, Jamova 39 SI-1000, Ljubljana, Slovenia;Center for Computational Astrobiology and Fundamental Biology, NASA Ames Research Center, M/S 239-4, Moffett Field, CA 94035, USA

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

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. Conclusion: We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.