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Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic

1
Laboratory of Systems Tumor Immunology, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, 91054 Erlangen, Germany
2
Institute of Mechanics, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 4, 1113 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2021, 22(2), 547; https://doi.org/10.3390/ijms22020547
Received: 25 November 2020 / Revised: 21 December 2020 / Accepted: 28 December 2020 / Published: 7 January 2021
In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today’s level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment. View Full-Text
Keywords: scientific method; computational modelling; systems biology; biomathematics; data-driven mathematical modelling; computational drug discovery; mathematical oncology scientific method; computational modelling; systems biology; biomathematics; data-driven mathematical modelling; computational drug discovery; mathematical oncology
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MDPI and ACS Style

Vera, J.; Lischer, C.; Nenov, M.; Nikolov, S.; Lai, X.; Eberhardt, M. Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic. Int. J. Mol. Sci. 2021, 22, 547. https://doi.org/10.3390/ijms22020547

AMA Style

Vera J, Lischer C, Nenov M, Nikolov S, Lai X, Eberhardt M. Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic. International Journal of Molecular Sciences. 2021; 22(2):547. https://doi.org/10.3390/ijms22020547

Chicago/Turabian Style

Vera, Julio, Christopher Lischer, Momchil Nenov, Svetoslav Nikolov, Xin Lai, and Martin Eberhardt. 2021. "Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic" International Journal of Molecular Sciences 22, no. 2: 547. https://doi.org/10.3390/ijms22020547

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