Model-based global analysis of heterogeneous experimental data using gfit

Mikhail K. Levin, Manju M. Hingorani, Raquell M. Holmes, Smita S. Patel, John H. Carson

Research output: Chapter in Book/Report/Conference proceedingChapter

10 Scopus citations

Abstract

Regression analysis is indispensible for quantitative understanding of biological systems and for developing accurate computational models. By applying regression analysis, one can validate models and quantify components of the system, including ones that cannot be observed directly. Global (simultaneous) analysis of all experimental data available for the system produces the most informative results. To quantify components of a complex system, the dataset needs to contain experiments of different types performed under a broad range of conditions. However, heterogeneity of such datasets complicates implementation of the global analysis. Computational models continuously evolve to include new knowledge and to account for novel experimental data, creating the demand for flexible and efficient analysis procedures. To address these problems, we have developed gfit software to globally analyze many types of experiments, to validate computational models, and to extract maximum information from the available experimental data.

Original languageEnglish (US)
Title of host publicationSystems Biology
EditorsIvan Maly
Pages335-359
Number of pages25
Edition1
DOIs
StatePublished - 2009

Publication series

NameMethods in Molecular Biology
Number1
Volume500
ISSN (Print)1064-3745

Keywords

  • Computational model
  • Computer simulation
  • Curve fitting
  • Least-squares
  • MATLAB
  • Regression analysis

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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