2.50
Hdl Handle:
http://hdl.handle.net/10033/620692
Title:
Analysis of Practical Identifiability of a Viral Infection Model.
Authors:
Nguyen, Van Kinh; Klawonn, Frank; Mikolajczyk, Rafael ( 0000-0003-1271-7204 ) ; Hernandez-Vargas, Esteban A. ( 0000-0002-3645-435X )
Abstract:
Mathematical modelling approaches have granted a significant contribution to life sciences and beyond to understand experimental results. However, incomplete and inadequate assessments in parameter estimation practices hamper the parameter reliability, and consequently the insights that ultimately could arise from a mathematical model. To keep the diligent works in modelling biological systems from being mistrusted, potential sources of error must be acknowledged. Employing a popular mathematical model in viral infection research, existing means and practices in parameter estimation are exemplified. Numerical results show that poor experimental data is a main source that can lead to erroneous parameter estimates despite the use of innovative parameter estimation algorithms. Arbitrary choices of initial conditions as well as data asynchrony distort the parameter estimates but are often overlooked in modelling studies. This work stresses the existence of several sources of error buried in reports of modelling biological systems, voicing the need for assessing the sources of error, consolidating efforts in solving the immediate difficulties, and possibly reconsidering the use of mathematical modelling to quantify experimental data.
Affiliation:
Helmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany.
Citation:
Analysis of Practical Identifiability of a Viral Infection Model. 2016, 11 (12):e0167568 PLoS ONE
Journal:
PloS one
Issue Date:
2016
URI:
http://hdl.handle.net/10033/620692
DOI:
10.1371/journal.pone.0167568
PubMed ID:
28036339
Type:
Article
Language:
en
ISSN:
1932-6203
Appears in Collections:
Publications of the AG (ESME); publications of the research group systems medicine of infections([BRICS]SMID); publications of the research group cellular proteom research (CPRO)

Full metadata record

DC FieldValue Language
dc.contributor.authorNguyen, Van Kinhen
dc.contributor.authorKlawonn, Franken
dc.contributor.authorMikolajczyk, Rafaelen
dc.contributor.authorHernandez-Vargas, Esteban A.en
dc.date.accessioned2017-01-11T09:39:52Z-
dc.date.available2017-01-11T09:39:52Z-
dc.date.issued2016-
dc.identifier.citationAnalysis of Practical Identifiability of a Viral Infection Model. 2016, 11 (12):e0167568 PLoS ONEen
dc.identifier.issn1932-6203-
dc.identifier.pmid28036339-
dc.identifier.doi10.1371/journal.pone.0167568-
dc.identifier.urihttp://hdl.handle.net/10033/620692-
dc.description.abstractMathematical modelling approaches have granted a significant contribution to life sciences and beyond to understand experimental results. However, incomplete and inadequate assessments in parameter estimation practices hamper the parameter reliability, and consequently the insights that ultimately could arise from a mathematical model. To keep the diligent works in modelling biological systems from being mistrusted, potential sources of error must be acknowledged. Employing a popular mathematical model in viral infection research, existing means and practices in parameter estimation are exemplified. Numerical results show that poor experimental data is a main source that can lead to erroneous parameter estimates despite the use of innovative parameter estimation algorithms. Arbitrary choices of initial conditions as well as data asynchrony distort the parameter estimates but are often overlooked in modelling studies. This work stresses the existence of several sources of error buried in reports of modelling biological systems, voicing the need for assessing the sources of error, consolidating efforts in solving the immediate difficulties, and possibly reconsidering the use of mathematical modelling to quantify experimental data.en
dc.language.isoenen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.titleAnalysis of Practical Identifiability of a Viral Infection Model.en
dc.typeArticleen
dc.contributor.departmentHelmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany.en
dc.identifier.journalPloS oneen

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