Inferring quantitative models of regulatory networks from expression data

I. Nachman, A. Regev, N. Friedman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

149 Scopus citations


Motivation: Genetic networks regulate key processes in living cells. Various methods have been suggested to reconstruct network architecture from gene expression data. However, most approaches are based on qualitative models that provide only rough approximations of the underlying events, and lack the quantitative aspects that are critical for understanding the proper function of biomolecular systems. Results: We present fine-grained dynamical models of gene transcription and develop methods for reconstructing them from gene expression data within the framework of a generative probabilistic model. Unlike previous works, we employ quantitative transcription rates, and simultaneously estimate both the kinetic parameters that govern these rates, and the activity levels of unobserved regulators that control them. We apply our approach to expression datasets from yeast and show that we can learn the unknown regulator activity profiles, as well as the binding affinity parameters. We also introduce a novel structure learning algorithm, and demonstrate its power to accurately reconstruct the regulatory network from those datasets.

Original languageAmerican English
Pages (from-to)i248-i256
Issue numberSUPPL. 1
StatePublished - 2004

Bibliographical note

Funding Information:
We thank Uri Alon for many stimulating discussions, and Rani Nelken for comments on the manuscript. A.R. and N.F. were supported by an NIGMS Center of Excellence grant. I.N. is supported by a Horowitz fellowship.


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