ArticleNature protocols2014
A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.
Article in Nature protocols, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers.
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Who cites it
97 citing papers in PubMed.
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- Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators.Journal of mathematical biology · 2025Article
- Conformal prediction for uncertainty quantification in dynamic biological systems.PLoS computational biology · 2025Article
- Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.Journal of theoretical biology · 2024Article
- Robust parameter estimation and identifiability analysis with hybrid neural ordinary differential equations in computational biology.NPJ systems biology and applications · 2024Article
- Integration of Sense and Control for Uncertain Systems Based on Delayed Feedback Active Inference.Entropy (Basel, Switzerland) · 2024Article
- Rationalised experiment design for parameter estimation with sensitivity clustering.Scientific reports · 2024Article
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.bioRxiv : the preprint server for biology · 2024Article
- Competition between physical search and a weak-to-strong transition rate-limits kinesin binding times.PLoS computational biology · 2024Article
- Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma.Journal of mathematical biology · 2023Article
- ADP release can explain spatially-dependent kinesin binding times.bioRxiv : the preprint server for biology · 2023Article
- Heterogeneity is a key factor describing the initial outbreak of COVID-19.Applied mathematical modelling · 2023Article
- Julia for biologists.Nature methods · 2023Review
37 more citing papers are in PubMed but not listed here.
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6 authors.
Funding
Abstract
As modeling becomes a more widespread practice in the life sciences and biomedical sciences, researchers need reliable tools to calibrate models against ever more complex and detailed data. Here we present an approximate Bayesian computation (ABC) framework and software environment, ABC-SysBio, which is a Python package that runs on Linux and Mac OS X systems and that enables parameter estimation and model selection in the Bayesian formalism by using sequential Monte Carlo (SMC) approaches. We outline the underlying rationale, discuss the computational and practical issues and provide detailed guidance as to how the important tasks of parameter inference and model selection can be performed in practice. Unlike other available packages, ABC-SysBio is highly suited for investigating, in particular, the challenging problem of fitting stochastic models to data. In order to demonstrate the use of ABC-SysBio, in this protocol we postulate the existence of an imaginary reaction network composed of seven interrelated biological reactions (involving a specific mRNA, the protein it encodes and a post-translationally modified version of the protein), a network that is defined by two files containing 'observed' data that we provide as supplementary information. In the first part of the PROCEDURE, ABC-SysBio is used to infer the parameters of this system, whereas in the second part we use ABC-SysBio's relevant functionality to discriminate between two different reaction network models, one of them being the 'true' one. Although computationally expensive, the additional insights gained in the Bayesian formalism more than make up for this cost, especially in complex problems.
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