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

Tenekedjiev, Kiril Ivanov - Personal Name; Nikolova, Natalia Danailova - Personal Name; Kolev, Krasimir - Personal Name; Ivanov, Kiril - Personal Name; Danailova, Natalia - Personal Name;

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.


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Series Title
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Call Number
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Publisher
: InTechOpen., 2012
Collation
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Language
English
ISBN/ISSN
-
Classification
NONE
Content Type
text
Media Type
computer
Carrier Type
online resource
Edition
-
Subject(s)
Biochemistry
monte carlo simulation
Specific Detail Info
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Statement of Responsibility
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Other Information
Cataloger
Candra
Source
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Validator
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Digital Object Identifier (DOI)
https://doi.org/10.5772/14984
Journal Volume
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  • Chapter 4 Applications of Monte Carlo Simulation in Modelling of Biochemical Processes
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