Experimental Mechanics

, Volume 43, Issue 3, pp 361-370

First online:

Process pathway inference via time series analysis

  • C. H. WigginsAffiliated withDepartment of Applied Physics and Applied Mathematics, Columbia UniversityCenter for Computational Biology and Bioinformatics (C2B2), Columbia UniversityKavli Institute for Theoretical Physics, University of California
  • , I. NemenmanAffiliated withKavli Institute for Theoretical Physics, University of California

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


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

Genomics pathways gene expression regulation Bayesian statistics auto-regressive models