Projects

BioDynaMo: A high-performance agent-based simulation software. This project involves interdisciplinary research, including Computer Science, Biology, Mathematics, Physics and Engineering. We are a consortium that employs agent-based modelling to study a variety of problems, including for instance neural development, cancer growth and therapy, as well as spreading of diseases. More information on the collaboration can be found here.

Some relevant sub-projects include: 

1) Usage of agent-based modelling to simulate the development of the brain. The human brain is a highly complex system, comprising approximately 86 billion neurons and 10^15 synapses. Computer models have become an indispensable tool to better understand the workings of the brain. However, one main challenge is how to specify the complex neural networks of the human brain. Some very ambitious people tried (and failed) to model and simulate such complexity. One main reason is that every brain is different. Hence, as a computational modeller, a significant challenge is regarding how to choose the initial configuration of the brain.

My proposal is to use a strikingly different approach: rather than modelling the brain from a complex initial configuration, I suggest to start the model with a single precursor cell. This single precursor cell incorporates, like the zygote does, the information necessary to build a brain. This is a much simpler solution to the problem of tackling brain complexity. 

2) Simulation of cancer growth and therapy. Cancer is one of the most deadly diseases, taking up the second position behind cardiovascular diseases in terms of mortality. While significant progress in diagnosing and curing cancer has been achieved, cancer is in most cases still not curable. 

Cancer is a very complex disease, involving numerous cell types, gene regulatory networks, the immune system and even neural systems. The capability to computationally model and capture the origins, progression and therapy of cancer would constitute a big advance for curing it. In the past, agent-based modelling has been shown to be very well-suited to do this. However, move work is required to better put in accordance with experimental data and demonstrate predictive performance. 

Cryopreservation: Cryoreservation denotes the process of preserving biological materials at very low temperatures. It is a highly complex process that involves the interaction of numerous biophysical mechanisms. In my lab, we have done some first steps towards using advanced computational methods to model and optimise the cryopreservation process. 

Here, the goal is to employ numerical methods to model causes of injury to cells and tissues during cryopreservation. Both slow-cooling as well as vitrification can be computationally modelled. In particular, the impact of cryoprotective agents, the cooling and warming rates, as well as different experimental protocols will be taken into account.