Linked Life Data

16986624

Source:http://linkedlifedata.com/resource/pubmed/id/16986624

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2006-9-21
pubmed:abstractText
Understanding the large reaction networks found in biological systems is a daunting task. One approach is to divide a network into more manageable smaller modules, thus simplifying the problem. This is a common strategy used in engineering. However, the process of identifying biological modules is still in its infancy and very little is understood about the range and capabilities of motif structures found in biological modules. In order to delineate these modules, a library of functional motifs has been generated via in silico evolution techniques. On the basis of their functional forms, networks were evolved from four broad areas: oscillators, bistable switches, homeostatic systems and frequency filters. Some of these motifs were constructed from simple mass action kinetics, others were based on Michaelis-Menten kinetics as found in protein/protein networks and the remainder were based on Hill equations as found in gene/protein interaction networks. The purpose of the study is to explore the capabilities of different network architectures and the rich variety of functional forms that can be generated. Ultimately, the library may be used to delineate functional motifs in real biological networks.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1741-2471
pubmed:author
pubmed:issnType
Print
pubmed:volume
153
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
223-35
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
In silico evolution of functional modules in biochemical networks.
pubmed:affiliation
Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.