PuSH - Publikationsserver des Helmholtz Zentrums München

Dorešić, D. ; Grein, S.* ; Hasenauer, J.

Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.

Bioinformatics 40, i558-i566 (2024)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
MOTIVATION: Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a nonlinear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared. RESULTS: We propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods. AVAILABILITY AND IMPLEMENTATION: Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO) available at https://github.com/ICB-DCM/pyPESTO.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 1367-4803
Zeitschrift Bioinformatics
Quellenangaben Band: 40, Heft: Supplement_1, Seiten: i558-i566 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen University of Bonn
German Federal Ministry of Education and Research
German Research Foundation
Deutsche Forschungsgemeinschaft