TY - JOUR
T1 - Learning biological networks
T2 - From modules to dynamics
AU - Bonneau, Richard
N1 - Funding Information:
We thank E. Vanden-Eijnden, D. Reiss, A. Madar, N. Baliga, B. Church and P. Waltman. We thank D. Shasha and the anonymous reviewers for detailed and insightful comments. R.B. is supported by the US National Science Foundation (DBI-0820757), the US Department of Energy GTL program and the US Department of Defense Computing and Society program.
PY - 2008/11
Y1 - 2008/11
N2 - Learning regulatory networks from genomics data is an important problem with applications spanning all of biology and biomedicine. Functional genomics projects offer a cost-effective means of greatly expanding the completeness of our regulatory models, and for some prokaryotic organisms they offer a means of learning accurate models that incorporate the majority of the genome. There are, however, several reasons to believe that regulatory network inference is beyond our current reach, such as (i) the combinatorics of the problem, (ii) factors we can't (or don't often) collect genome-wide measurements for and (iii) dynamics that elude cost-effective experimental designs. Recent works have demonstrated the ability to reconstruct large fractions of prokaryotic regulatory networks from compendiums of genomics data; they have also demonstrated that these global regulatory models can be used to predict the dynamics of the transcriptome. We review an overall strategy for the reconstruction of global networks based on these results in microbial systems.
AB - Learning regulatory networks from genomics data is an important problem with applications spanning all of biology and biomedicine. Functional genomics projects offer a cost-effective means of greatly expanding the completeness of our regulatory models, and for some prokaryotic organisms they offer a means of learning accurate models that incorporate the majority of the genome. There are, however, several reasons to believe that regulatory network inference is beyond our current reach, such as (i) the combinatorics of the problem, (ii) factors we can't (or don't often) collect genome-wide measurements for and (iii) dynamics that elude cost-effective experimental designs. Recent works have demonstrated the ability to reconstruct large fractions of prokaryotic regulatory networks from compendiums of genomics data; they have also demonstrated that these global regulatory models can be used to predict the dynamics of the transcriptome. We review an overall strategy for the reconstruction of global networks based on these results in microbial systems.
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U2 - 10.1038/nchembio.122
DO - 10.1038/nchembio.122
M3 - Review article
C2 - 18936750
AN - SCOPUS:54249124501
SN - 1552-4450
VL - 4
SP - 658
EP - 664
JO - Nature Chemical Biology
JF - Nature Chemical Biology
IS - 11
ER -