Analysis of Practical Identifiability of a Viral Infection Model.

Nguyen, Van Kinh; Klawonn, Frank; Mikolajczyk, Rafael; Hernandez-Vargas, Esteban Abelardo

dc.contributor.author	Nguyen, Van Kinh
dc.contributor.author	Klawonn, Frank
dc.contributor.author	Mikolajczyk, Rafael
dc.contributor.author	Hernandez-Vargas, Esteban Abelardo
dc.date.accessioned	2017-01-11T09:39:52Z
dc.date.available	2017-01-11T09:39:52Z
dc.date.issued	2016
dc.identifier.citation	Analysis of Practical Identifiability of a Viral Infection Model. 2016, 11 (12):e0167568 PLoS ONE	en
dc.identifier.issn	1932-6203
dc.identifier.pmid	28036339
dc.identifier.doi	10.1371/journal.pone.0167568
dc.identifier.uri	http://hdl.handle.net/10033/620692
dc.description.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.
dc.language.iso	en	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/4.0/	*
dc.title	Analysis of Practical Identifiability of a Viral Infection Model.	en
dc.type	Article	en
dc.contributor.department	Helmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany.	en
dc.identifier.journal	PloS one	en
refterms.dateFOA	2018-06-13T16:02:11Z
html.description.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.