627382.pdf
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...
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oapen-20.500.12657-315312021-11-12T16:34:35Z Chapter 4 Applications of Monte Carlo Simulation in Modelling of Biochemical Processes Tenekedjiev, Kiril Ivanov Nikolova, Natalia Danailova Kolev, Krasimir Ivanov, Kiril Danailova, Natalia Kolev, Krasimir biochemistry monte carlo simulation biochemistry monte carlo simulation Confidence interval Confidence region Enzyme Enzyme kinetics Fatty acid Plasmin Random variable bic Book Industry Communication::P Mathematics & science::PS Biology, life sciences::PSB Biochemistry 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. 2019-10-04 14:49:35 2020-04-01T13:38:50Z 2017-04-12 23:55 2019-10-04 14:49:35 2020-04-01T13:38:50Z 2017-03-01 23:55:55 2019-10-04 14:49:35 2020-04-01T13:38:50Z 2020-04-01T13:38:50Z 2012 chapter 627382 OCN: 1030816752 http://library.oapen.org/handle/20.500.12657/31531 eng application/pdf n/a 627382.pdf InTechOpen Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science 10.5772/14984 10.5772/14984 09f6769d-48ed-467d-b150-4cf2680656a1 f2e388d7-7fba-4011-886d-3edbbf66e522 d859fbd3-d884-4090-a0ec-baf821c9abfd Wellcome 1 083174 Wellcome Trust Wellcome open access |
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English |
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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 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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InTechOpen |
| publishDate |
2019 |
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