Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems
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. W...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6403641 |
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| author | Itai Dattner Harold Ship Eberhard O. Voit |
| author_facet | Itai Dattner Harold Ship Eberhard O. Voit |
| author_sort | Itai Dattner |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1210cee18ec2476aaeebc16f6609a2a0 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1210cee18ec2476aaeebc16f6609a2a02025-02-03T01:01:30ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/64036416403641Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic SystemsItai Dattner0Harold Ship1Eberhard O. Voit2Department of Statistics, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, IsraelDepartment of Statistics, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, IsraelThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 950 Atlantic Drive, Atslanta, GA 30332–2000, USANonlinear 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.http://dx.doi.org/10.1155/2020/6403641 |
| spellingShingle | Itai Dattner Harold Ship Eberhard O. Voit Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems Complexity |
| title | Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems |
| title_full | Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems |
| title_fullStr | Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems |
| title_full_unstemmed | Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems |
| title_short | Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems |
| title_sort | separable nonlinear least squares parameter estimation for complex dynamic systems |
| url | http://dx.doi.org/10.1155/2020/6403641 |
| work_keys_str_mv | AT itaidattner separablenonlinearleastsquaresparameterestimationforcomplexdynamicsystems AT haroldship separablenonlinearleastsquaresparameterestimationforcomplexdynamicsystems AT eberhardovoit separablenonlinearleastsquaresparameterestimationforcomplexdynamicsystems |