Inferring quantitative models of regulatory networks from expression data.
BACKGROUND
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.
Nachman
I
I
School of Computer Science & Engineering, Hebrew University, Jerusalem, Israel.
Regev
A
A
Friedman
N
N
D001665:Binding Sites; D002874:Chromosome Mapping / Q000379:methods; D003198:Computer Simulation; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008957:Models, Genetic; D011485:Protein Binding; D020543:Proteome / Q000378:metabolism*; D050436:Regulatory Elements, Transcriptional / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods; D015398:Signal Transduction / Q000235:genetics*; D014157:Transcription Factors / Q000235:genetics*; D015533:Transcriptional Activation / Q000502:physiology
D020543:Proteome; D014157:Transcription Factors