Linked Life Data

18366722

Source:http://linkedlifedata.com/resource/pubmed/id/18366722

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-4-8
pubmed:abstractText
Modeling and simulation of cellular signaling and metabolic pathways as networks of biochemical reactions yields sets of non-linear ordinary differential equations. These models usually depend on several parameters and initial conditions. If these parameters are unknown, results from simulation studies can be misleading. Such a scenario can be avoided by fitting the model to experimental data before analyzing the system. This involves parameter estimation which is usually performed by minimizing a cost function which quantifies the difference between model predictions and measurements. Mathematically, this is formulated as a non-linear optimization problem which often results to be multi-modal (non-convex), rendering local optimization methods detrimental.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1752-0509
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
2
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
26
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Hybrid optimization method with general switching strategy for parameter estimation.
pubmed:affiliation
Process Engineering Group, Spanish Council for Scientific Research, IIM-CSIC, Spain. ebalsa@iim.csic.es
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't