A protocol for dynamic model calibration

Autor: Jan Hasenauer, Julio R. Banga, Fabian Fröhlich, Dilan Pathirana, Alejandro F. Villaverde
Přispěvatelé: European Commission, Ministerio de Economía y Competitividad (España), Ministerio de Ciencia, Innovación y Universidades (España)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Optimization
0209 industrial biotechnology
Identification
Computational complexity theory
AcademicSubjects/SCI01060
Computer science
Calibration (statistics)
02 engineering and technology
computer.software_genre
Models
Biological
Quantitative Biology - Quantitative Methods
03 medical and health sciences
020901 industrial engineering & automation
Parameter estimation
Identifiability
Molecular Biology
Quantitative Methods (q-bio.QM)
030304 developmental biology
Protocol (science)
0303 health sciences
Estimation theory
12 Matemáticas
Experimental data
2404 Biomatemáticas
Maxima and minima
Identification (information)
FOS: Biological sciences
Calibration
Dynamic Modelling
Parameter Estimation
Systems Biology
Problem Solving Protocol
Data mining
Systems biology
computer
Information Systems
Dynamic modelling
Zdroj: Briefings in Bioinformatics
Investigo. Repositorio Institucional de la Universidade de Vigo
Universidade de Vigo (UVigo)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Brief. Bioinform. 23:bbab387 (2022)
Popis: 19 pages, 6 figures, 2 tables.-- This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License
Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem
European Union’s Horizon 2020 Research and Innovation Programme (grant no. 686282) (‘CANPATHPRO’); Spanish MINECO/FEDER Project SYNBIOCONTROL (DPI2017-82896-C2-2-R to J.R.B.); Ramón y Cajal Fellowship (RYC-2019-027537-I to A.F.V.) from the Ministerio de Ciencia e innovación, Spain; Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia (ED431F 2021/003 to A.F.V.); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (EXC 2151 - 390873048 to J.H.), (EXC-2047/1 - 390685813 to D.P.); German Federal Ministry of Economic Affairs and Energy (grant no. 16KN074236 to D.P.). Ministerio de Ciencia e Innovación, Spain (grant PID2020-117271RB-C22, ‘BIODYNAMICS’, to J.R.B.; Funding for open access charge: Universidade de Vigo/CISUG)
Databáze: OpenAIRE
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