Quantitative modelling in stem cell biology and beyond: how to make best use of it
Quantitative modelling in stem cell biology and beyond: how to make best use of it
Purpose of review: this article gives a broad overview of quantitative modelling approaches in biology and provides guidance on how to employ them to boost stem cell research, by helping to answer biological questions and to predict the outcome of biological processes.
Recent findings: the twenty-first century has seen a steady increase in the proportion of cell biology publications employing mathematical modelling to aid experimental research. However, quantitative modelling is often used as a rather decorative element to confirm experimental findings, an approach which often yields only marginal added value, and is in many cases scientifically questionable.
Summary: quantitative modelling can boost biological research in manifold ways, but one has to take some careful considerations before embarking on a modelling campaign, in order to maximise its added value, to avoid pitfalls that may lead to wrong results, and to be aware of its fundamental limitations, imposed by the risks of over-fitting and “universality”.
Advanced hypothesis testing, Over-fitting, Quantitative modelling, Universality
67-76
Greulich, Philip
65da32ad-a73a-435a-86e0-e171437430a9
11 December 2023
Greulich, Philip
65da32ad-a73a-435a-86e0-e171437430a9
Greulich, Philip
(2023)
Quantitative modelling in stem cell biology and beyond: how to make best use of it.
Current Stem Cell Reports, 9 (4), .
(doi:10.1007/s40778-023-00230-7).
Abstract
Purpose of review: this article gives a broad overview of quantitative modelling approaches in biology and provides guidance on how to employ them to boost stem cell research, by helping to answer biological questions and to predict the outcome of biological processes.
Recent findings: the twenty-first century has seen a steady increase in the proportion of cell biology publications employing mathematical modelling to aid experimental research. However, quantitative modelling is often used as a rather decorative element to confirm experimental findings, an approach which often yields only marginal added value, and is in many cases scientifically questionable.
Summary: quantitative modelling can boost biological research in manifold ways, but one has to take some careful considerations before embarking on a modelling campaign, in order to maximise its added value, to avoid pitfalls that may lead to wrong results, and to be aware of its fundamental limitations, imposed by the risks of over-fitting and “universality”.
Text
Quant_Modelling_in_SCbio_Greulich revised--submitted-17-08-23.
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Restricted to Repository staff only until 8 November 2024.
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s40778-023-00230-7
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Accepted/In Press date: 17 October 2023
Published date: 11 December 2023
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Publisher Copyright:
© 2023, The Author(s).
Keywords:
Advanced hypothesis testing, Over-fitting, Quantitative modelling, Universality
Identifiers
Local EPrints ID: 484652
URI: http://eprints.soton.ac.uk/id/eprint/484652
ISSN: 2198-7866
PURE UUID: e06abd50-46bd-4f9d-935d-4d783eb2d843
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Date deposited: 20 Nov 2023 17:33
Last modified: 18 Mar 2024 03:27
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