Systems biology: parameter estimation for biochemical models

Ashyraliyev, Maksat; Fomekong-Nanfack, Yves; Kaandorp, Jaap A.; Blom, Joke

doi:10.1111/j.1742-4658.2008.06844.x

Cited by 297 publications

(322 citation statements)

References 65 publications

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“…fminsearch, finds local function-minimizing parameters, but MCMC and Markov chain methods are more likely to find global function-minimizing parameters (Ashyraliyev et al, 2009). That is, while fminsearch will return the parameter values that produce the lowest least-squares fitting within a bounded neighborhood of the initial parameters, all Markov chain methods return the parameter values that produce the lowest leastsquares fitting over a finite number of arbitrary parameters from anywhere in the parameter space (Ashyraliyev et al, 2009). This difference in the domain of each algorithm leads to defining behaviors that either help or hinder the goodness of fit.…”

Section: Hybrid Minimization Algorithmmentioning

confidence: 99%

A Comparison and Catalog of Intrinsic Tumor Growth Models

2014

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Determining the dynamics and parameter values that drive tumor growth is of great interest to mathematical modelers, experimentalists and practitioners alike. We provide a basis on which to estimate the growth dynamics of ten different tumors by fitting growth parameters to at least five sets of published experimental data per type of tumor. These timescale tumor growth data are also used to determine which of the most common tumor growth models (exponential, power law, logistic, Gompertz, or von Bertalanffy) provides the best fit for each type of tumor. In order to compute the best-fit parameters, we implemented a hybrid local-global least squares minimization algorithm based on a combination of Nelder-Mead simplex direct search and Monte Carlo Markov Chain methods.

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Section: Hybrid Minimization Algorithmmentioning

confidence: 99%

A Comparison and Catalog of Intrinsic Tumor Growth Models

2014

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“…This makes parameter estimation from experimental data a crucial problem for quantitative systems biology (Ashyraliyev et al, 2009;Crampin, 2006;Jaqaman and Danuser, 2006;Chou and Voit, 2009). For all but the simplest systems, parameter estimation of biological systems is a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

On the identifiability of metabolic network models

et al. 2012

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A major problem for the identification of metabolic network models is parameter identifiability. The identifiability of a parameter consists in the possibility to unambiguously infer the parameter from the data. Identifiability problems may be due to the structure of the model, in particular implicit dependencies between the parameters, or to limitations in the quantity and quality of the available data. We address the detection and resolution of identifiability problems for a class of pseudo-linear models of metabolism, so-called linlog models.Linlog models have the advantage that parameter estimation reduces to linear or orthogonal regression, which facilitates the analysis of identifiability. We develop precise definitions of structural and practical identifiability, and clarify the fundamental relations between these concepts. In addition, we use Singular Value Decomposition (SVD) to detect identifiability problems and reduce the model to an identifiable approximation by a Principal Component Analysis (PCA) approach. The criterion is adapted to real data, which are frequently scarce, incomplete, and noisy. The test of the criterion on a model with simulated data shows that it is capable of correctly identifying the principal components of the data vector. The application to a state-of-the-art dataset on central carbon metabolism in Escherichia coli yields the surprising result that only 4 out of 31 reactions, and 37 out of 100 parameters, are identifiable. This underlines the practical importance of identifiability analysis and model reduction in the modeling of large-scale metabolic networks. Although our approach has been developed in the context of linlog models, it carries over to other pseudo-linear models and provides useful hints for the identifiability analysis of more general classes of nonlinear models of metabolism.

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“…This makes parameter estimation from experimental data a crucial problem for quantitative systems biology [Ashyraliyev et al, 2009, Crampin, 2006. For all but the simplest systems, parameter estimation is a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

Structural and practical identifiability of approximate metabolic network models

Berthoumieux

Kahn

Jong

et al. 2012

IFAC Proceedings Volumes

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Abstract:Parameter estimation from experimental data is a crucial problem in quantitative modeling of biochemical reaction networks. An especially important issue, raised by the complexity of the models and the challenging nature of the experimental data, is parameter identifiability. Despite several approaches proposed in the systems biology literature, no agreement exists on the analysis of structural and practical identifiability, and the relations among the two. In this paper we propose a mathematical framework for the analysis of identifiability of metabolic network models, establish basic results and methods for the structural and practical identifiability analysis of the class of so-called linlog models, and discuss the results on the basis of an artificial example.

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Systems biology: parameter estimation for biochemical models

Cited by 297 publications

References 65 publications

A Comparison and Catalog of Intrinsic Tumor Growth Models

A Comparison and Catalog of Intrinsic Tumor Growth Models

On the identifiability of metabolic network models

Structural and practical identifiability of approximate metabolic network models

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