Connecting quantitative regulatory-network models to the genome

Bioinformatics
Yue PanMark Craven

Abstract

An important task in computational biology is to infer, using background knowledge and high-throughput data sources, models of cellular processes such as gene regulation. Nachman et al. have developed an approach to inferring gene-regulatory networks that represents quantitative transcription rates, and simultaneously estimates both the kinetic parameters that govern these rates and the activity levels of unobserved regulators that control them. This approach is appealing in that it provides a more detailed and realistic description of how a gene's regulators influence its level of expression than alternative methods. We have developed an extension to this approach that involves representing and learning the key kinetic parameters as functions of features in the genomic sequence. The primary motivation for our approach is that it provides a more mechanistic representation of the regulatory relationships being modeled. We evaluate our approach using two Escherichia coli gene-expression data sets, with a particular focus on modeling the networks that are involved in controlling how E.coli regulates its response to the carbon source(s) available to it. Our results indicate that our sequence-based models provide predictive accuracy...Continue Reading

References

Mar 11, 1987·Nucleic Acids Research·C B Harley, R P Reynolds
Apr 25, 1983·Nucleic Acids Research·D K Hawley, W R McClure
Sep 5, 1997·Science·F R BlattnerY Shao
Dec 7, 2000·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·N FriedmanDana Pe'er
Jul 27, 2001·Bioinformatics·Dana Pe'erN Friedman
Jul 27, 2001·Bioinformatics·Amos Tanay, R Shamir
Oct 18, 2002·Nucleic Acids Research·Panayiotis V BenosGary D Stormo
Feb 4, 2003·Nature Medicine·Shahin Rafii, Mihaela Skobe
Jul 2, 2003·Bioinformatics·Joseph BockhorstJeremy Glasner
Jul 12, 2003·Bioinformatics·Joseph BockhorstMark Craven
Jul 21, 2004·Bioinformatics·I NachmanN Friedman
Dec 21, 2004·Nucleic Acids Research·Ingrid M KeselerPeter D Karp
Feb 12, 2005·The Journal of Biological Chemistry·Mingzhu LiuFrederick R Blattner
May 4, 2005·Trends in Microbiology·Lisa U MagnussonThomas Nyström
Nov 16, 2005·Molecular Cell·Rachel Anne MooneyRobert Landick
Dec 13, 2005·Biological Chemistry·Dagmar K Willkomm, Roland K Hartmann
Dec 24, 2005·Nature·Edward Witten

Citations

Sep 2, 2009·Microbiology and Molecular Biology Reviews : MMBR·Sacha A F T van HijumOscar P Kuipers
Jan 6, 2010·Statistical Methods in Medical Research·Ning Sun, Hongyu Zhao
May 22, 2009·BMC Bioinformatics·I Nachman, Aviv Regev
Sep 7, 2013·EURASIP Journal on Bioinformatics & Systems Biology·Guy Karlebach
Apr 18, 2013·Molecular Systems Biology·Luca GerosaUwe Sauer
Feb 2, 2011·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Rui ChangWei Wang
Sep 18, 2008·Nature Reviews. Molecular Cell Biology·Guy Karlebach, Ron Shamir

Related Concepts

Genome Mapping
In Silico
Computer Programs and Programming
Signal Transduction
Proteome

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