ArticlePLoS computational biology2006
Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.
Article in PLoS computational biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 53 citations in OpenAlex.
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- Designing Experiments to Discriminate Families of Logic Models.Frontiers in bioengineering and biotechnology · 2015Article
- Experimental design schemes for learning Boolean network models.Bioinformatics (Oxford, England) · 2014Article
- Genomic reconstruction of transcriptional regulatory networks in lactic acid bacteria.BMC genomics · 2013Article
- Network Medicine: New Paradigm in the -Omics Era.Anatomy & physiology : current research · 2011Article
- An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models.PLoS computational biology · 2010Article
- OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains.BMC systems biology · 2010Article
- Discriminating between rival biochemical network models: three approaches to optimal experiment design.BMC systems biology · 2010Article
- The comprehensive microbial resource.Nucleic acids research · 2010Article
- Reconstructing transcriptional regulatory networks through genomics data.Statistical methods in medical research · 2009Article
- How to identify essential genes from molecular networks?BMC systems biology · 2009Article
- Mechanisms and evolution of control logic in prokaryotic transcriptional regulation.Microbiology and molecular biology reviews : MMBR · 2009Review
- Gene expression profiling and the use of genome-scale in silico models of Escherichia coli for analysis: providing context for content.Journal of bacteriology · 2009Review
- The Analysis of Multiple Genome Comparisons in Genus Escherichia and Its Application to the Discovery of Uncharacterised Metabolic Genes in Uropathogenic Escherichia coli CFT073.Comparative and functional genomics · 2009Article
- Genome-scale models of bacterial metabolism: reconstruction and applications.FEMS microbiology reviews · 2009Review
- Elucidating regulatory mechanisms downstream of a signaling pathway using informative experiments.Molecular systems biology · 2009Article
- Geometric interpretation of gene coexpression network analysis.PLoS computational biology · 2008Article
- Underlying principles of natural selection in network evolution: systems biology approach.Evolutionary bioinformatics online · 2007Article
- Selective control of the apoptosis signaling network in heterogeneous cell populations.PloS one · 2007Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy) data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12)) that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.