Quantifying Parameter Interdependence in Stochastic Discrete Models of Biochemical Systems

Stochastic modeling of biochemical processes at the cellular level has been the subject of intense research in recent years. The Chemical Master Equation is a broadly utilized stochastic discrete model of such processes. Numerous important biochemical systems consist of many species subject to many...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 25; no. 8; p. 1168
Main Authors: Gholami, Samaneh, Ilie, Silvana
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2023
MDPI
Subjects:
Algorithms
Biochemistry
Chemotaxonomy
Collinearity
Discrete-time systems
estimability analysis
Expected values
Finite difference method
finite-difference methods
Markov analysis
Methods
Monte Carlo simulation
Ordinary differential equations
Parameter estimation
Parameter sensitivity
parameter subset selection
Sensitivity analysis
Singular value decomposition
stochastic biochemical systems
Stochastic models
Stochastic processes
stochastic simulation algorithm
Canada
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary:Stochastic modeling of biochemical processes at the cellular level has been the subject of intense research in recent years. The Chemical Master Equation is a broadly utilized stochastic discrete model of such processes. Numerous important biochemical systems consist of many species subject to many reactions. As a result, their mathematical models depend on many parameters. In applications, some of the model parameters may be unknown, so their values need to be estimated from the experimental data. However, the problem of parameter value inference can be quite challenging, especially in the stochastic setting. To estimate accurately the values of a subset of parameters, the system should be sensitive with respect to variations in each of these parameters and they should not be correlated. In this paper, we propose a technique for detecting collinearity among models’ parameters and we apply this method for selecting subsets of parameters that can be estimated from the available data. The analysis relies on finite-difference sensitivity estimations and the singular value decomposition of the sensitivity matrix. We illustrated the advantages of the proposed method by successfully testing it on several models of biochemical systems of practical interest.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e25081168