ArticlePLoS computational biology2022
Bayesian parameter estimation for dynamical models in systems biology.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Drosophila embryo cellularization is tuned by the viscoelastic properties of membrane-cortex linkers.Biophysical journal · 2026Article
- A Nonparametric Approach to Practical Identifiability of Nonlinear Mixed Effects Models.Bulletin of mathematical biology · 2026Article
- Rules of life at the interface of calcium signaling and mechanobiology.APL bioengineering · 2025Review
- Multi-omics strategies for biomarker discovery and application in personalized oncology.Molecular biomedicine · 2025Review
- Calcium dynamics in small spaces: Lessons learned from modeling in dendritic spines.Biophysical journal · 2025Review
- Quantitative Assessment of Biological Dynamics with Aggregate Data.Bulletin of mathematical biology · 2025Article
- Systems modeling and uncertainty quantification of AMP-activated protein kinase signaling.NPJ systems biology and applications · 2025Article
- Multi-cellular network model predicts alterations in glomerular endothelial structure in diabetic kidney disease.PLoS computational biology · 2025Article
- Increasing certainty in systems biology models using Bayesian multimodel inference.Nature communications · 2025Article
- Improving parameter inference by resolving Bayesian prior ambiguity via multi-dataset analysis: Application to isothermal titration calorimetry.bioRxiv : the preprint server for biology · 2025Article
- Translating microbial kinetics into quantitative responses and testable hypotheses using Kinbiont.Nature communications · 2025Article
- Logic-based modeling of inflammatory macrophage cross talk with glomerular endothelial cells in diabetic kidney disease.American journal of physiology. Renal physiology · 2025Article
- Recent advances in deep learning for protein-protein interaction: a review.BioData mining · 2025Review
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Conformal prediction for uncertainty quantification in dynamic biological systems.PLoS computational biology · 2025Article
- Challenges and opportunities in uncertainty quantification for healthcare and biological systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- A bayesian approach for parameterizing and predicting plasmid conjugation dynamics.Scientific reports · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and '-omics' studies that have helped populate protein-protein interaction networks in great detail, modeling in systems biology lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.
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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.