Linked Life Data

21198117

Source:http://linkedlifedata.com/resource/pubmed/id/21198117

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2011-1-4
pubmed:abstractText
Dynamical models of cellular processes promise to yield new insights into the underlying systems and their biological interpretation. The processes are usually nonlinear, high dimensional, and time-resolved experimental data of the processes are sparse. Therefore, parameter estimation faces the challenges of structural and practical nonidentifiability. Nonidentifiability of parameters induces nonobservability of trajectories, reducing the predictive power of the model. We will discuss a generic approach for nonlinear models that allows for identifiability and observability analysis by means of a realistic example from systems biology. The results will be utilized to design new experiments that enhance model predictiveness, illustrating the iterative cycle between modeling and experimentation in systems biology.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1089-7682
pubmed:author
pubmed:copyrightInfo
© 2010 American Institute of Physics.
pubmed:issnType
Electronic
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
045105
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Identifiability and observability analysis for experimental design in nonlinear dynamical models.
pubmed:affiliation
Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@fdm.uni-freiburg.de
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't