Parameter Estimation and Structure Identification
Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, time-dense profiles of metabolites and proteins that are measurable with mass spectrometry and nuclear magnetic resonance. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations. This long-term project thus explores different means of identifying parameters from biological time series data.
[1] Voit, E.O., and J.S. Almeida: Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 20(11), 1670-1681, 2004.
[2] Voit, E.O.: The Dawn of a New Era of Metabolic Systems Analysis, Drug Discovery Today BioSilico 2(5), 182-189, 2004.
[3] Chou, I-C., H. Martens, and E.O. Voit. Parameter Estimation in Biochemical Systems Models with Alternating Regression. BMC Theoretical Biology and Medical Modelling, 2006.
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[1] Voit, E.O., and J.S. Almeida: Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 20(11), 1670-1681, 2004.
[2] Voit, E.O.: The Dawn of a New Era of Metabolic Systems Analysis, Drug Discovery Today BioSilico 2(5), 182-189, 2004.
[3] Chou, I-C., H. Martens, and E.O. Voit. Parameter Estimation in Biochemical Systems Models with Alternating Regression. BMC Theoretical Biology and Medical Modelling, 2006.
Click image to view:
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| from [2] | from [2] |
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| from [3] | from [3] |