ArticleBioinformatics (Oxford, England)2023
pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Spatiotemporal modeling of GPCR signaling: The role of endosomal dynamics and receptor recycling.PLoS computational biology · 2026Article
- PEtab Select: Specification standard and supporting software for automated model selection.PLoS computational biology · 2026Article
- MEmilio: a high performance Modular EpideMIcs simuLatIOn software for multi-scale and comparative simulations of infectious disease dynamics.Scientific reports · 2026Article
- Kinetic parameter prediction using neural networks identifies limitations to CThe New phytologist · 2026Article
- Quantitative modelling of P-TEFb mediated CTD phosphorylation identifies local cooperativity.PLoS computational biology · 2026Article
- Data-driven model reveals increased stability of CAG-expanded huntingtin RNA due to MID1 binding.PLoS computational biology · 2026Article
- Suggested experimental design and computational modeling to infer single-cell lipid dynamics from a single destructive measurement.iScience · 2026Article
- A standardized workflow for kinetic metabolic model curation and dissemination.PLoS computational biology · 2026Article
- PEtab-GUI: a graphical user interface to create, edit, and inspect PEtab parameter estimation problems.Bioinformatics (Oxford, England) · 2026Article
- Article
- Using PyBioNetFit to leverage qualitative and quantitative data in biological model parameterization and uncertainty quantification.Frontiers in immunology · 2026Article
- CrossLabFit: A novel framework for integrating qualitative and quantitative data across multiple labs for model calibration.PLoS computational biology · 2025Article
- 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
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Jaxkineticmodel: Neural ordinary differential equations inspired parameterization of kinetic models.PLoS computational biology · 2025Article
- A stochastic modelling framework for cancer patient trajectories: combining tumour growth, metastasis, and survival.Journal of mathematical biology · 2025Article
- Integrated mathematical and experimental modeling uncovers enhanced EMT plasticity upon loss of the DLC1 tumor suppressor.PLoS computational biology · 2025Article
- Assessment of uncertainty quantification in universal differential equations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Quantifying cell cycle regulation by tissue crowding.Biophysical journal · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
summaryMechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. pyPESTO is a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods. AVAILABILITY AND IMPLEMENTATION: pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.