Images are one of the best means to celebrate the beauty of science and to explore any biological topic. Computational science and advanced tools highlight basic cell functions, the darkness of disease, environmental questions and – as the editors explain – how complexity generates richness in unexpected ways.
The Art of Theoretical Biology is a collection of pieces of art; they are the result of a deep scientific research, of data analysis and mathematical models; and they may help us make science more accessible to the public as well.
More than 120 authors contributed to this book, creating a synergic union between art and science: a promising new way to show how the potential of mathematical models may support discoveries in any field.
We had the honour and the pleasure to interview the editors of this book: Franziska Matthäus, Giersch Professor of Bioinformatics at the Goethe University, Frankfurt; Sebastian Matthäus, founder and head at the Grenfarben Agentur für Gestaltung, Berlin; Sarah Anne Harris, Associate Professor of Biological Physics at the University of Leeds; Thomas Hillen, Professor and Associate Chair Research at the Department of Mathematical and Statistical Sciences at the University of Alberta.
1) In the preface, it is explained that you conceive “the book as a superposition of art and science; each separate image is an act of scientific research, but the whole collection is a work of art”. From an educational point of view, do you think it would be possible to profit from the conjoined usage of art and science to get closer to the audience?
Yes, our intention was to use the power of images to reach out to non-scientists and increase awareness of and interest in the increasingly important field of theoretical biology. The stories behind each image are formulated in a very compact style free of scientific jargon, and targeted to a wider audience using the scientists’ own words. While most people outside the scientific community would not, out of plain intrinsic interest, read scientific articles, they are nonetheless attracted to the images and become curious, especially because for most of the images it is not immediately apparent what is displayed. Connecting the images to the research story and the creation process allows the reader a short journey into a variety of exciting research topics. We hope that our book will show readers that science is exciting and full of beauty.
2. A significant part of the book concerns oncological research, for the purpose of improving patient treatment and decreasing cancer mortality rates. What are the future perspectives of predictive studies on carcinogenesis by means of mathematical models?
Oncological research profits from a tight collaboration between medics, biologists and mathematical modelers. Validating our predictive models against experimental data can be far more challenging than for inanimate matter. Cancer occurs in living organisms, which are extremely complex and need to be treated with particular care. However, tissue engineers can now grow “tumoroids”, which are synthetic cell cultures that grow into tumor-like tissue, and which can then be studied in a more controlled manner. These synthetic systems are likely to accelerate our understanding of how best to model carcinogenesis.
Moreover, the advent of new technologies such as automated image processing (e.g. of biopsies and scans) and artificial intelligence have generated large interest from the clinical sciences, so increasingly both data science and mathematical modelling are informing medical research and treatment of disease. The future of cancer therapy is likely to be “personalized” or “precision medicine”, where each patient receives a treatment bespoke to their particular disease, which may be based on the genetic profile of their tumour. An exciting example where mathematical modelling is at the forefront of development in precision medicine is adaptive evolutionary therapies. These can drastically reduce side effects, reduce chemotherapeutic resistance and improve patients’ quality of life.
3. Mathematical models may be used in order to develop drugs and to simulate their pharmacokinetics. Thanks to this approach, what progress has been made as regards pharmacological drug development?
For pharmacokinetics, there has been a great deal of progress in the use of pre-clinical models to predict the behavior of drugs in patients in relation to their duration of action, absorption, metabolism, excretion and toxicity. These models use experiments that expose cultured cells and or microsomes (which are highly simplified mimics of organs) to the drug in the laboratory to predict how it will behave in animals.
However, de novo drug design from the molecular level upwards is a very complicated and expensive process. Big pharma companies have recognized the value in computer aided design in optimizing new molecules for further development. However, the enormous complexity of drug interactions, which includes practical details such as solubility and bioavailability, cell penetration, off-target interactions that cause side effects, potential degradation by metabolic enzymes and time to excretion mean that multiple different types of models must be used, depending on aspect of drug development that is being optimized.
4. At present, how determining has been the contribution of theoretical biology to the scientific research? How important is its predictive purpose, in order to improve quality?
The ultimate aim of theoretical biology is to understand living systems, to be able to make predictions about their behavior and to give us the insight we need to engineer them. Models have been used successfully to describe biology at every length-scale, from the movement of electrons during photosynthesis at the sub-atomic level, through to the atomic interactions that drive protein-protein interactions (Knitting Proteins by Santiago Schnell) and molecular signaling cascades (Crop Circles of Cancer by Katharina Baum, Jagath C. Rajapakse & Francisco Azuaje), up to cell dynamics (Cellular Swarms in Cellular Automata by Andreas Deutsch), the biomechanics of organs, and eventually ecological interactions of populations and their response to their environments (Tower of Life by Sylvain Gretchko).
However, what our models are not yet able to do, is to cross these different theoretical regimes. While we can successfully build models at individual length-scales, e.g. for single proteins, or for collections of cells, or for the heart, or the population of a species, we cannot predict how a change at any one of these length-scales will impact the others. For example, when clinical geneticists sequence the genomes of their patients to understand the origin of their disease, they find many “variants of unknown significance”. Genetic variation is so prevalent that it is often impossible to identify the mutations responsible for a genetic disease. While we can say precisely how a mutation will affect the atomic structure of DNA, we cannot predict how this atomic level change will affect the next length-scales up, such as cells or tissues. A major breakthrough in theoretical biology will be when we can trace the information flow between the multi-length scales, so from atomic level changes through to cells and organs, that are all important in biology.
To understand, predict and control a biological system is the ultimate goal of theoretical biology. Scientific merit is measured directly through its implication on science, biology, ecology, or medicine. Theoretical biology is developing fast, many aspects of the experimental data lack reproducibility, because we don’t understand the variables. Models can help with this.
5. The field of theoretical biology also contributes to the study of nature and of environmental impact assessments. Habitat alterations of several species are one of the main anthropic threats to biodiversity. What potential have mathematical tools in the field of ecological issues?
Theoretical biology plays a major role in ecology. Modelling is used to manage fishing quotas, to design protective marine areas, and to control invasive species, such as mountain pine beetles in Western Canada. Modelling is the only way to address vital questions about the future of our planet, such as looming species extinctions and the effect on predator-prey dynamics, biodiversity, evolution. Models enable us to make predictions about the impact of climate change, and to provide scientific strategies to mitigate against the consequences.
Reference: The Art of Theoretical Biology; Matthäus, F., Matthäus, S., Harris, S., Hillen, Th. (Eds.) © 2020 Springer LINK TO THE BOOK.