Systems Biology

Volume 500 of the series Methods in Molecular Biology pp 335-359


Model-Based Global Analysis of Heterogeneous Experimental Data Using gfit

  • Mikhail K. LevinAffiliated withRichard Berlin Center for Cell Analysis and Modeling, University of Connecticut Health Center Email author 
  • , Manju M. Hingorani
  • , Raquell M. Holmes
  • , Smita S. Patel
  • , John H. Carson

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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.


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