ArticleBioinformatics (Oxford, England)2018
Hierarchical optimization for the efficient parametrization of ODE models.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- Mathematical multi-compartment modeling of chronic lymphocytic leukemia cell kinetics under ibrutinib.iScience · 2024Article
- Mathematical basis and toolchain for hierarchical optimization of biochemical networks.PLoS computational biology · 2024Article
- Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences.Royal Society open science · 2024Article
- Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.Bioinformatics (Oxford, England) · 2024Article
- AI-Aristotle: A physics-informed framework for systems biology gray-box identification.PLoS computational biology · 2024Article
- Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes.iScience · 2023Article
- A protocol for dynamic model calibration.Briefings in bioinformatics · 2022Article
- Mini-batch optimization enables training of ODE models on large-scale datasets.Nature communications · 2022Article
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- Computational modeling of stem and progenitor cell kinetics identifies plausible hematopoietic lineage hierarchies.iScience · 2021Article
- Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach.Journal of mathematical biology · 2020Article
- Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines.PLoS computational biology · 2020Article
- Efficient parameterization of large-scale dynamic models based on relative measurements.Bioinformatics (Oxford, England) · 2020Article
- Benchmark problems for dynamic modeling of intracellular processes.Bioinformatics (Oxford, England) · 2019Article
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3 authors.
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Abstract
Motivation: Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems. Results: In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimization problem into an inner and outer subproblem. These subproblems possess lower dimensions than the original optimization problem, and the inner problem can be solved analytically. We evaluated accuracy, robustness and computational efficiency of the hierarchical approach by studying three signaling pathways. The proposed approach achieved better convergence than the standard approach and required a lower computation time. As the hierarchical optimization approach is widely applicable, it provides a powerful alternative to established approaches. Availability and implementation: The code is included in the MATLAB toolbox PESTO which is available at http://github.com/ICB-DCM/PESTO. Supplementary information: Supplementary data are available at Bioinformatics online.
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