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Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational method...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5256869/ https://www.ncbi.nlm.nih.gov/pubmed/28114351 http://dx.doi.org/10.1371/journal.pcbi.1005331 |
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author | Fröhlich, Fabian Kaltenbacher, Barbara Theis, Fabian J. Hasenauer, Jan |
author_facet | Fröhlich, Fabian Kaltenbacher, Barbara Theis, Fabian J. Hasenauer, Jan |
author_sort | Fröhlich, Fabian |
collection | PubMed |
description | Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. |
format | Online Article Text |
id | pubmed-5256869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-52568692017-02-06 Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks Fröhlich, Fabian Kaltenbacher, Barbara Theis, Fabian J. Hasenauer, Jan PLoS Comput Biol Research Article Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. Public Library of Science 2017-01-23 /pmc/articles/PMC5256869/ /pubmed/28114351 http://dx.doi.org/10.1371/journal.pcbi.1005331 Text en © 2017 Fröhlich et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fröhlich, Fabian Kaltenbacher, Barbara Theis, Fabian J. Hasenauer, Jan Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks |
title | Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks |
title_full | Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks |
title_fullStr | Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks |
title_full_unstemmed | Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks |
title_short | Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks |
title_sort | scalable parameter estimation for genome-scale biochemical reaction networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5256869/ https://www.ncbi.nlm.nih.gov/pubmed/28114351 http://dx.doi.org/10.1371/journal.pcbi.1005331 |
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