Reverse engineering systems models of regulation: Discovery, prediction and mechanisms

Ashworth, J; Wurtmann, EJ; Baliga, NS

Reverse engineering systems models of regulation: Discovery, prediction and mechanisms

Ashworth, J

Wurtmann, EJ Baliga, NS

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Publication Type:: Journal Article
Citation:: Current Opinion in Biotechnology, 2012, 23 (4), pp. 598 - 603
Issue Date:: 2012-08-01

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Field	Value	Language
dc.contributor.author	Ashworth, J https://orcid.org/0000-0003-4835-6551	en_US
dc.contributor.author	Wurtmann, EJ	en_US
dc.contributor.author	Baliga, NS	en_US
dc.date.available	2011-12-08	en_US
dc.date.issued	2012-08-01	en_US
dc.identifier.citation	Current Opinion in Biotechnology, 2012, 23 (4), pp. 598 - 603	en_US
dc.identifier.issn	0958-1669	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115130
dc.description.abstract	Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties. © 2011 Elsevier Ltd.	en_US
dc.relation.ispartof	Current Opinion in Biotechnology	en_US
dc.relation.isbasedon	10.1016/j.copbio.2011.12.005	en_US
dc.subject.classification	Biotechnology	en_US
dc.subject.mesh	Animals	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Systems Biology	en_US
dc.subject.mesh	Models, Biological	en_US
dc.subject.mesh	Gene Regulatory Networks	en_US
dc.subject.mesh	Bioengineering	en_US
dc.title	Reverse engineering systems models of regulation: Discovery, prediction and mechanisms	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	23	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - C3 - Climate Change Cluster
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties. © 2011 Elsevier Ltd.

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http://hdl.handle.net/10453/115130