Summary
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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References
Mogilner, A., Wollman, R., and Marshall, W. F. (2006) Quantitative modeling in cell biology: what is it good for? Dev. Cell. 11, 279–287.
Albeck, J. G., MacBeath, G., White, F. M., Sorger, P. K., Lauffenburger, D. A., and Gaudet, S. (2006) Collecting and organizing systematic sets of protein data. Nat. Rev. Mol. Cell. Biol. 7, 803–812.
Jaqaman, K. and Danuser, G. (2006) Linking data to models: data regression. Nat. Rev. Mol. Cell. Biol. 7, 813–819.
Beechem, J. M. (1992) Global analysis of biochemical and biophysical data. Meth. Enzymol. 210, 37–54.
Draper, N. R. and Smith, H. (1998) Applied Regression Analysis. Wiley, New York.
Slepchenko, B. M., Schaff, J. C., Macara, I., and Loew, L. M. (2003) Quantitative cell biology with the Virtual Cell. Trends Cell. Biol. 13, 570–576.
Levin, M. K. and Patel, S. S. (2002) Helicase from hepatitis C virus, energetics of DNA binding. J. Biol. Chem. 277, 29377–29385.
Epstein, I. R. (1978) Cooperative and non-cooperative binding of large ligands to a finite one-dimensional lattice. A model for ligand-oligonucleotide interactions. Biophys. Chem. 8, 327–339.
Tsodikov, O. V., Holbrook, J. A., Shkel, I. A., and Record, M. T., Jr. (2001) Analytic binding isotherms describing competitive interactions of a protein ligand with specific and nonspecific sites on the same DNA oligomer. Biophys. J. 81, 1960–1969.
Moles, C. G., Mendes, P., and Banga, J. R. (2003) Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13, 2467–2474.
Banga, J. R. (2008) Optimization in computational systems biology. BMC Syst. Biol. 2, 47.
Johnson, A. and O Donnell, M. (2005) Cellular DNA replicases: components and dynamics at the replication fork. Annu. Rev. Biochem. 74, 283–315.
Acknowledgments
Authors thank members of the Center for Cell Analysis and Modeling: Pavel Kraykivski, Igor Novak, Jim Schaff, and Boris Slepchenko for their advice and critical discussions and Les Loew for his support. Experimental data for clamp loader protein was provided thanks to Siying Chen. This work was supported in part by grants NS15190 (NIH), RR13186 (NIH), RR022232 (NIH) and RR022624 (NIH) to J.H.C; GM55310 (NIH) to S.S.P; GM64514-01 (NIH) and MCB 0448379 (NSF) to M.M.H.
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© 2009 Humana Press
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Levin, M., Hingorani, M., Holmes, R., Patel, S., Carson, J. (2009). Model-Based Global Analysis of Heterogeneous Experimental Data Using gfit . In: Maly, I. (eds) Systems Biology. Methods in Molecular Biology, vol 500. Humana Press. https://doi.org/10.1007/978-1-59745-525-1_12
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DOI: https://doi.org/10.1007/978-1-59745-525-1_12
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