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Lakrisenko, P. ; Stapor, P. ; Grein, S.* ; Paszkowski, L.* ; Pathirana, D.* ; Fröhlich, F.* ; Lines, G.T.* ; Weindl, D. ; Hasenauer, J.

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

PLoS Comput. Biol. 19:e1010783 (2023)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Methylation; Algorithm; Patterns
ISSN (print) / ISBN 1553-734X
e-ISSN 1553-7358
Zeitschrift PLoS Computational Biology
Quellenangaben Band: 19, Heft: 1, Seiten: , Artikelnummer: e1010783 Supplement: ,
Verlag Public Library of Science (PLoS)
Verlagsort 1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen National Cancer Institute
Human Frontier Science Program
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
German Federal Ministry of Education and Research (BMBF) within the e:Med funding scheme (junior research alliance PeriNAA)
European Union's Horizon 2020 research and innovation program (CanPathPro)