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Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks

Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimatio...

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Autores principales: Lakrisenko, Polina, Stapor, Paul, Grein, Stephan, Paszkowski, Łukasz, Pathirana, Dilan, Fröhlich, Fabian, Lines, Glenn Terje, Weindl, Daniel, Hasenauer, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838866/
https://www.ncbi.nlm.nih.gov/pubmed/36595539
http://dx.doi.org/10.1371/journal.pcbi.1010783
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author Lakrisenko, Polina
Stapor, Paul
Grein, Stephan
Paszkowski, Łukasz
Pathirana, Dilan
Fröhlich, Fabian
Lines, Glenn Terje
Weindl, Daniel
Hasenauer, Jan
author_facet Lakrisenko, Polina
Stapor, Paul
Grein, Stephan
Paszkowski, Łukasz
Pathirana, Dilan
Fröhlich, Fabian
Lines, Glenn Terje
Weindl, Daniel
Hasenauer, Jan
author_sort Lakrisenko, Polina
collection PubMed
description Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical.
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spelling pubmed-98388662023-01-14 Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks Lakrisenko, Polina Stapor, Paul Grein, Stephan Paszkowski, Łukasz Pathirana, Dilan Fröhlich, Fabian Lines, Glenn Terje Weindl, Daniel Hasenauer, Jan PLoS Comput Biol Research Article Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical. Public Library of Science 2023-01-03 /pmc/articles/PMC9838866/ /pubmed/36595539 http://dx.doi.org/10.1371/journal.pcbi.1010783 Text en © 2023 Lakrisenko et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Lakrisenko, Polina
Stapor, Paul
Grein, Stephan
Paszkowski, Łukasz
Pathirana, Dilan
Fröhlich, Fabian
Lines, Glenn Terje
Weindl, Daniel
Hasenauer, Jan
Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
title Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
title_full Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
title_fullStr Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
title_full_unstemmed Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
title_short Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
title_sort efficient computation of adjoint sensitivities at steady-state in ode models of biochemical reaction networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838866/
https://www.ncbi.nlm.nih.gov/pubmed/36595539
http://dx.doi.org/10.1371/journal.pcbi.1010783
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