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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010783 |
| _version_ | 1797924624793600000 |
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| author | Polina Lakrisenko Paul Stapor Stephan Grein Łukasz Paszkowski Dilan Pathirana Fabian Fröhlich Glenn Terje Lines Daniel Weindl Jan Hasenauer |
| author_facet | Polina Lakrisenko Paul Stapor Stephan Grein Łukasz Paszkowski Dilan Pathirana Fabian Fröhlich Glenn Terje Lines Daniel Weindl Jan Hasenauer |
| author_sort | Polina Lakrisenko |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T15:04:58Z |
| format | Article |
| id | doaj.art-154d58c3a25b4b748e7ec79817755d57 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-10T15:04:58Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-154d58c3a25b4b748e7ec79817755d572023-02-15T05:30:46ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-01-01191e101078310.1371/journal.pcbi.1010783Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks.Polina LakrisenkoPaul StaporStephan GreinŁukasz PaszkowskiDilan PathiranaFabian FröhlichGlenn Terje LinesDaniel WeindlJan HasenauerDynamical 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.https://doi.org/10.1371/journal.pcbi.1010783 |
| spellingShingle | Polina Lakrisenko Paul Stapor Stephan Grein Łukasz Paszkowski Dilan Pathirana Fabian Fröhlich Glenn Terje Lines Daniel Weindl Jan Hasenauer Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks. PLoS Computational Biology |
| 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 |
| url | https://doi.org/10.1371/journal.pcbi.1010783 |
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