Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems.

Complexity
Itai DattnerEberhard O Voit

Abstract

Nonlinear dynamic models are widely used for characterizing processes that govern complex biological pathway systems. Over the past decade, validation and further development of these models became possible due to data collected via high-throughput experiments using methods from molecular biology. While these data are very beneficial, they are typically incomplete and noisy, which renders the inference of parameter values for complex dynamic models challenging. Fortunately, many biological systems have embedded linear mathematical features, which may be exploited, thereby improving fits and leading to better convergence of optimization algorithms. In this paper, we explore options of inference for dynamic models using a novel method of separable nonlinear least-squares optimization and compare its performance to the traditional nonlinear least-squares method. The numerical results from extensive simulations suggest that the proposed approach is at least as accurate as the traditional nonlinear least-squares, but usually superior, while also enjoying a substantial reduction in computational time.

References

Feb 28, 2004·Bioinformatics·Eberhard O Voit, Jonas Almeida
Aug 23, 2007·BMC Bioinformatics·Marco VilelaJonas S Almeida
Sep 6, 2008·Bioinformatics·Gautam GoelEberhard O Voit
Feb 24, 2016·Frontiers in Genetics·Sepideh Dolatshahi, Eberhard O Voit
Mar 24, 2017·Bioinformatics·Mojdeh Faraji, Eberhard O Voit
Oct 6, 2017·IEEE Transactions on Cybernetics· Min Gan Long Chen
Dec 21, 2017·Statistical Methods in Medical Research·Rami YaariAmit Huppert
Jan 1, 2017·Journal of the American Statistical Association·Shizhe ChenDaniela M Witten
May 31, 2018·Statistical Methods in Medical Research·Itai Dattner, Amit Huppert
Jun 26, 2018·Statistica Neerlandica·Itai Dattner, Shota Gugushvili

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Software Mentioned

fminsearch
Matlab
ggplot2
R
BST
simode
TIMP

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