Model Selection in Systems Biology Depends on Experimental Design

PLoS Computational Biology

Volumn 10, Issue 6, 2014, Pages

  Silk, Daniel ^a   Kirk, Paul D W ^a   Barnes, Chris P ^a   Toni, Tina ^a   Stumpf, Michael P H ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ORDINARY DIFFERENTIAL EQUATIONS; STATE SPACE METHODS; STATISTICS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

DEGREE OF CONFIDENCE; DESIGN AND MODELING; DESIGN SELECTIONS; INTERPRETATION OF MODELS; MODEL SELECTION; MODELING TASK; OPTIMAL EXPERIMENTS; SELECTION FRAMEWORK; STOCHASTICS; SYSTEMS BIOLOGY;

DESIGN OF EXPERIMENTS;

JANUS KINASE; STAT PROTEIN;

ANALYTICAL PARAMETERS; ARTICLE; COMPUTER MODEL; EXPERIMENTAL DESIGN; GENE REGULATORY NETWORK; GENETIC MODEL; MOLECULAR INTERACTION; MONTE CARLO METHOD; PREDICTIVE VALUE; ROBUSTNESS; SENSITIVITY ANALYSIS; SIGNAL TRANSDUCTION; STOCHASTIC MODEL; SYSTEMS BIOLOGY; BIOLOGICAL MODEL; BIOLOGY; COMPUTER SIMULATION; MATHEMATICAL PHENOMENA;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; MATHEMATICAL CONCEPTS; MODELS, BIOLOGICAL; MONTE CARLO METHOD; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

EID: 84903388376     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003650     Document Type: Article

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