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Journal Article

How Computational Models Enable Mechanistic Insights into Virus Infection.


Sbalzarini,  Ivo F.
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Sbalzarini, I. F., & Greber, U. F. (2018). How Computational Models Enable Mechanistic Insights into Virus Infection. Methods in molecular biology (Clifton, N.J.), 1836, 609-631. doi:10.1007/978-1-4939-8678-1_30.

Cite as: https://hdl.handle.net/21.11116/0000-0003-F67C-D
An implicit aim in cellular infection biology is to understand the mechanisms how viruses, microbes, eukaryotic parasites, and fungi usurp the functions of host cells and cause disease. Mechanistic insight is a deep understanding of the biophysical and biochemical processes that give rise to an observable phenomenon. It is typically subject to falsification, that is, it is accessible to experimentation and empirical data acquisition. This is different from logic and mathematics, which are not empirical, but built on systems of inherently consistent axioms. Here, we argue that modeling and computer simulation, combined with mechanistic insights, yields unprecedented deep understanding of phenomena in biology and especially in virus infections by providing a way of showing sufficiency of a hypothetical mechanism. This ideally complements the necessity statements accessible to empirical falsification by additional positive evidence. We discuss how computational implementations of mathematical models can assist and enhance the quantitative measurements of infection dynamics of enveloped and non-enveloped viruses and thereby help generating causal insights into virus infection biology.