ArticleScientific reports2021
Benchmarking of numerical integration methods for ODE models of biological systems.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Article
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- Review
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- PEtab.jl: advancing the efficiency and utility of dynamic modelling.Bioinformatics (Oxford, England) · 2025Article
- Current state and open problems in universal differential equations for systems biology.NPJ systems biology and applications · 2025Article
- Jaxkineticmodel: Neural ordinary differential equations inspired parameterization of kinetic models.PLoS computational biology · 2025Article
- Converging functional phenotyping with systems mapping to illuminate the genotype-phenotype associations.Horticulture research · 2024Article
- Towards a comprehensive assessment of QSP models: what would it take?Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Quantitative Online Monitoring of an Immobilized Enzymatic Network by Ion Mobility-Mass Spectrometry.Journal of the American Chemical Society · 2024Article
- The distribution of fitness effects during adaptive walks using a simple genetic network.PLoS genetics · 2024Article
- Iterative design of training data to control intricate enzymatic reaction networks.Nature communications · 2024Article
- Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states.PloS one · 2024Article
- Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance.Molecular systems biology · 2023Article
- Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks.PLoS computational biology · 2023Article
- Multi-scale models of whole cells: progress and challenges.Frontiers in cell and developmental biology · 2023Review
- SBbadger: biochemical reaction networks with definable degree distributions.Bioinformatics (Oxford, England) · 2022Article
- Merits and Demerits of ODE Modeling of Physicochemical Systems for Numerical Simulations.Molecules (Basel, Switzerland) · 2022Review
- GAMES: A Dynamic Model Development Workflow for Rigorous Characterization of Synthetic Genetic Systems.ACS synthetic biology · 2022Article
- Mini-batch optimization enables training of ODE models on large-scale datasets.Nature communications · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 142 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.
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