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Chapter 4 Applications of Monte Carlo Simulation in Modelling of Biochemical Processes

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The biochemical models describing complex and dynamic metabolic systems are typically multi-parametric and non-linear, thus the identification of their parameters requires nonlinear regression analysis of the experimental data. The stochastic nature of the experimental samples poses the necessity to...

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Bibliographic Details
Main Author: Tenekedjiev, Kiril Ivanov (auth)
Other Authors: Nikolova, Natalia Danailova (auth), Kolev, Krasimir (auth), Ivanov, Kiril (auth), Danailova, Natalia (auth)
Format: Electronic Book Chapter
Language:English
Published: InTechOpen 2012
Subjects:
Biochemistry
biochemistry
monte carlo simulation
Confidence interval
Confidence region
Enzyme
Enzyme kinetics
Fatty acid
Plasmin
Random variable
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Summary:The biochemical models describing complex and dynamic metabolic systems are typically multi-parametric and non-linear, thus the identification of their parameters requires nonlinear regression analysis of the experimental data. The stochastic nature of the experimental samples poses the necessity to estimate not only the values fitting best to the model, but also the distribution of the parameters, and to test statistical hypotheses about the values of these parameters. In such situations the application of analytical models for parameter distributions is totally inappropriate because their assumptions are not applicable for intrinsically non-linear regressions. That is why, Monte Carlo simulations are a powerful tool to model biochemical processes.
ISBN:14984
Access:Open Access

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