TY  - CHAP
AU  - Tenekedjiev, Kiril Ivanov
AU  - Nikolova, Natalia Danailova
AU  - Kolev, Krasimir
AU  - Ivanov, Kiril
AU  - Danailova, Natalia
AU  - Kolev, Krasimir
AB  - 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.
DO  - 10.5772/14984
ID  - OAPEN ID: 627382
ID  - OAPEN ID: OCN: 1030816752
KW  - biochemistry
KW  - monte carlo simulation
KW  - biochemistry
KW  - monte carlo simulation
KW  - Confidence interval
KW  - Confidence region
KW  - Enzyme
KW  - Enzyme kinetics
KW  - Fatty acid
KW  - Plasmin
KW  - Random variable
L1  - https://library.oapen.org/bitstream/id/737b1c0b-133a-47e4-b900-e8b1954a36cb/627382.pdf
LA  - English
LK  - http://library.oapen.org/handle/20.500.12657/31531
PB  - InTechOpen
PY  - 2012
TI  - Chapter 4 Applications of Monte Carlo Simulation in Modelling of Biochemical Processesnull
ER  - 
