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Analysis of Practical Identifiability of a Viral Infection Model.

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.

SUBMITTER: Nguyen VK

PROVIDER: S-EPMC5201286 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Analysis of Practical Identifiability of a Viral Infection Model.

Nguyen Van Kinh VK Klawonn Frank F Mikolajczyk Rafael R Hernandez-Vargas Esteban A EA

PloS one 20161230 12

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 mathematica ...[more]

PMID: 28036339

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Project description:The origin and evolutionary relationship of viruses is poorly understood. This makes archaeal virus-host systems of particular interest because the hosts generally root near the base of phylogenetic trees, while some of the viruses have clear structural similarities to those that infect prokaryotic and eukaryotic cells. Despite the advantageous position for use in evolutionary studies, little is known about archaeal viruses or how they interact with their hosts, compared to viruses of bacteria and eukaryotes. In addition, many archaeal viruses have been isolated from extreme environments and present a unique opportunity for elucidating factors that are important for existence at the extremes. In this article we focus on virus-host interactions using a proteomics approach to study Sulfolobus Turreted Icosahedral Virus (STIV) infection of Sulfolobus solfataricus P2. Using cultures grown from the ATCC cell stock, a single cycle of STIV infection was sampled six times over a 72?h period. More than 700 proteins were identified throughout the course of the experiments. Seventy one host proteins were found to change their concentration by nearly twofold (p?<?0.05) with 40 becoming more abundant and 31 less abundant. The modulated proteins represent 30 different cell pathways and 14 clusters of orthologous groups. 2D gel analysis showed that changes in post-translational modifications were a common feature of the affected proteins. The results from these studies showed that the prokaryotic antiviral adaptive immune system CRISPR-associated proteins (CAS proteins) were regulated in response to the virus infection. It was found that regulated proteins come from mRNAs with a shorter than average half-life. In addition, activity-based protein profiling (ABPP) profiling on 2D-gels showed caspase, hydrolase, and tyrosine phosphatase enzyme activity labeling at the protein isoform level. Together, this data provides a more detailed global view of archaeal cellular responses to viral infection, demonstrates the power of quantitative two-dimensional differential gel electrophoresis and ABPP using 2D gel compatible fluorescent dyes.

Dataset Information

Analysis of Practical Identifiability of a Viral Infection Model.

Publications

Analysis of Practical Identifiability of a Viral Infection Model.

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