TY - JOUR
T1 - Mathematical modelling in biomedicine
T2 - A primer for the curious and the skeptic
AU - Vera, Julio
AU - Lischer, Christopher
AU - Nenov, Momchil
AU - Nikolov, Svetoslav
AU - Lai, Xin
AU - Eberhardt, Martin
N1 - Funding Information:
Funding: J.V.’s work in mathematical modelling applied to biomedicine has been generously funded over the years by the German Ministry of Education and Research (BMBF) through the initiatives FORSYS [CALSYS-FORSYS 0315264], e:Bio [e:Bio-miRSys 0316175A, e:Bio-MelEVIR 031L0073A], and e:Med [e:Med-CAPSyS 01ZX1304F, e:Med-MelAutim 01ZX1905A] and targeted initiatives in artificial intelligence [KI-VesD, 031L0244A] as well as by the German Research Foundation (DFG) [Ve642/1-1 in SPP1757] and the Bavarian Government [Gaminfection-UKER]. We also acknowledge support by Deutsche Forschungsgemeinschaft and Friedrich-Alexander-Universität Erlangen-Nürnberg within the funding program Open Access Publishing.
Funding Information:
J.V.’s work in mathematical modelling applied to biomedicine has been generously funded over the years by the German Ministry of Education and Research (BMBF) through the initiatives FORSYS [CALSYS-FORSYS 0315264], e:Bio [e:Bio-miRSys 0316175A, e:Bio-MelEVIR 031L0073A], and e:Med [e:Med-CAPSyS 01ZX1304F, e:Med-MelAutim 01ZX1905A] and targeted initiatives in artificial intelligence [KI-VesD, 031L0244A] as well as by the German Research Foundation (DFG) [Ve642/1-1 in SPP1757] and the Bavarian Government [Gaminfection-UKER]. We also acknowledge support by Deutsche Forschungsgemeinschaft and Friedrich-Alexander-Universität Erlangen-Nürnberg within the funding program Open Access Publishing.Some of the misconceptions about modelling come from actual discussions with experimental and clinical researchers at the Department of Dermatology of the FAU Erlangen-Nürnberg, to whom we are grateful for their honest though at times scathing disagreement. Some parts of the text are based on a presentation given by J.V. at the Heidelberger Institut für Theoretische Studien (HITS), and others are from exchanges of ideas within the laboratory of Systems Tumor Immunology. J.V. also thanks Olaf Wolkenhauer (University of Rostock) for 15 years of continuous discussion on the foundations of systems biology.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/1/2
Y1 - 2021/1/2
N2 - 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.
AB - 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.
KW - Biomathematics
KW - Computational drug discovery
KW - Computational modelling
KW - Data-driven mathematical modelling
KW - Mathematical oncology
KW - Scientific method
KW - Systems biology
U2 - 10.3390/ijms22020547
DO - 10.3390/ijms22020547
M3 - Review Article
C2 - 33430432
AN - SCOPUS:85099133162
SN - 1661-6596
VL - 22
SP - 1
EP - 16
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
IS - 2
M1 - 547
ER -