ArticleBMC systems biology2010
An iterative identification procedure for dynamic modeling of biochemical networks.
Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
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- Using Approximate Bayesian Computation to Calibrate the Model Parameters Characterizing the Autoregulatory Behavior of Microvessels.International journal for numerical methods in biomedical engineering · 2025Article
- Employing Observability Rank Conditions for Taking into Account Experimental Information a priori.Bulletin of mathematical biology · 2025Article
- Article
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- Two new user-friendly methods to assess pharmacometric parameter identifiability on categorical and continuous scales.CPT: pharmacometrics & systems pharmacology · 2024Article
- Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Structural identifiability of biomolecular controller motifs with and without flow measurements as model output.PLoS computational biology · 2023Article
- Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway.bioRxiv : the preprint server for biology · 2023Article
- A novel strategy for dynamic modeling of genome-scale interaction networks.Bioinformatics (Oxford, England) · 2023Article
- Benchmarking tools for a priori identifiability analysis.Bioinformatics (Oxford, England) · 2023Article
- Structural identifiability of compartmental models for infectious disease transmission is influenced by data type.Epidemics · 2022Article
- Limits of epidemic prediction using SIR models.Journal of mathematical biology · 2022Article
- A protocol for dynamic model calibration.Briefings in bioinformatics · 2022Article
- Robust dynamic experiments for the precise estimation of respiration and fermentation parameters of fruit and vegetables.PLoS computational biology · 2022Article
- A Multiphase Multiobjective Dynamic Genome-Scale Model Shows Different Redox Balancing among Yeast Species of themSystems · 2021Article
- Quantitative Systems Pharmacology Modeling of Avadomide-Induced Neutropenia Enables Virtual Clinical Dose and Schedule Finding Studies.The AAPS journal · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMathematical models provide abstract representations of the information gained from experimental observations on the structure and function of a particular biological system. Conferring a predictive character on a given mathematical formulation often relies on determining a number of non-measurable parameters that largely condition the model's response. These parameters can be identified by fitting the model to experimental data. However, this fit can only be accomplished when identifiability can be guaranteed.
resultsWe propose a novel iterative identification procedure for detecting and dealing with the lack of identifiability. The procedure involves the following steps: 1) performing a structural identifiability analysis to detect identifiable parameters; 2) globally ranking the parameters to assist in the selection of the most relevant parameters; 3) calibrating the model using global optimization methods; 4) conducting a practical identifiability analysis consisting of two (a priori and a posteriori) phases aimed at evaluating the quality of given experimental designs and of the parameter estimates, respectively and 5) optimal experimental design so as to compute the scheme of experiments that maximizes the quality and quantity of information for fitting the model.
conclusionsThe presented procedure was used to iteratively identify a mathematical model that describes the NF-kappaB regulatory module involving several unknown parameters. We demonstrated the lack of identifiability of the model under typical experimental conditions and computed optimal dynamic experiments that largely improved identifiability properties.
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