Can you put a living being inside a computer program? And can it then travel through time? This isn’t the plot of a science fiction B-film from the 1980s; it’s part of the research conducted by Kirsten ten Tusscher, Professor of Computational Developmental Biology at Utrecht University. Professor Ten Tusscher works to unravel the complex algorithms that nature uses to turn a single cell into a multicellular living organism. On 30 January 2019, she will give her inaugural lecture titled ‘Coding (and Decoding) Biological Algorithms’.
From body axis to Arabidopsis
By coding organisms such as plants, animals, and humans, in computer models, Ten Tusscher tries to decode how nature works and how evolution has shaped developmental biological processes over time. “The great thing about this field is that you not only study how something works in living nature, but also why it works that way”.
Some of the intriguing questions she faces include how humans and animals grow equally long arms and legs in precisely symmetrical locations, while plants can grow in highly asymmetrical ways. Although the final result may be different - symmetry or asymmetry - in both cases the question is how the cells and tissues in the body of a plant or animal communicate in order to achieve it, and why they do it in a specific way.
To study plant roots, Ten Tusscher uses a computer model that includes the genes, hormones, and environmental factors that play a role in root growth. “To do that, I collaborate with researchers from a variety of disciplines, who provide data from experimental research on issues such as the influence of a shortage of phosphates, soil salinization, flooding, or shade on plant growth. Using these models, my research group works to unravel which factors may be essential for the process of lateral root formation, and how environmental conditions can influence that process. We then join forces with experimental biologists to test whether these factors actually do what our model predicts.”
The interplay between theory and experiment
“Theoretical and experimental biology complement one another well in this method”, Ten Tusscher explains. “On the one hand, I work together with experimentalists to answer questions that can’t be answered using experiments alone. For example, because some of the important processes are extremely fast whereas others are extremely slow, which makes it impossible to study the processes experimentally at the same time in order to differentiate cause and effect. So we use models to study these processes simultaneously instead. On the other hand, I need the assistance of my experimental colleagues in order to test the hypotheses that our models put forward.”
“The great thing about a computer model is that you by systematically varying factors, you can see which ones are important in replicating phenomena such as the growth of a lateral root, and which ones are not. And once you know how nature builds that lateral root, you can come up with a crazy alternative, build it into an alternative model, and see if it has any disadvantages that can explain why nature builds lateral roots in a certain way.” Ten Tusscher can also use computer models to travel through time, or even to multiple parallel universes: she simulates the process of evolution over and over to observe when, and under what circumstances, a certain type of organism evolves. “That way, you can determine whether nature ‘chooses’ a specific solution more often because the solution offers advantages that the alternatives don’t, for example because they can deal with mutations more effectively.”
The interdisciplinary nature of Ten Tusscher’s field is reflected in the backgrounds of the students who intern at her research group. They include students from Biology, Mathematics, and Computer Science, but also students with a background in Physics. Prof. Ten Tusscher’s teaching activities are not limited to Bachelor’s students; she also illustrates the importance of computational biology during Master’s courses for various programmes, or at summer schools offered by programmes such as Complex Systems Studies. But she hopes that even more students will come to recognise the opportunities presented by computational biology. “It’s an interdisciplinary field that’s undergoing rapid development, and experimental research and theoretical models need one another more than ever. So it’s a field of study with a promising future.”