Experimental Mechanics

, Volume 43, Issue 3, pp 361–370

Process pathway inference via time series analysis

  • C. H. Wiggins
  • I. Nemenman

DOI: 10.1007/BF02410536

Cite this article as:
Wiggins, C.H. & Nemenman, I. Experimental Mechanics (2003) 43: 361. doi:10.1007/BF02410536


Motivated by recent experimental developments in functional genomics, we construct and test a numerical technique for inferring process pathways, in which one process calls another process, from time series data. We validate using a case in which data are readily available and we formulate an extension, appropriate for genetic regulatory networks, which exploits Bayesian inference and in which the present-day undersampling is compensated for by prior understanding of genetic regulation.

Key Words

Genomicspathwaysgene expression regulationBayesian statisticsauto-regressive models

Copyright information

© Society for Experimental Mechanics 2002

Authors and Affiliations

  • C. H. Wiggins
    • 1
    • 2
    • 3
  • I. Nemenman
    • 3
  1. 1.Department of Applied Physics and Applied MathematicsColumbia University
  2. 2.Center for Computational Biology and Bioinformatics (C2B2)Columbia University
  3. 3.Kavli Institute for Theoretical PhysicsUniversity of CaliforniaSanta Barbara