Model-Based Global Analysis of Heterogeneous Experimental Data Using gfit

Richard Berlin Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, 06030, USA.
Methods in Molecular Biology (Impact Factor: 1.29). 02/2009; 500(1):335-59. DOI: 10.1007/978-1-59745-525-1_12
Source: PubMed


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.

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