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Date
  • 21/05/2025 11:00 am
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Roman Bauer

University of Surrey, UK

Dr Bauer is senior lecturer at the University of Surrey, UK. He received his Bachelor’s and Master’s Degree in Computational Science and Engineering from ETH Zuerich, Switzerland. Afterwards, he did his doctoral studies at the Institute for Neuroinformatics (ETH Zürich/Uni Zürich). After postdoctoral fellowships at Newcastle University (UK) he moved to the University of Surrey in 2020, where he leads the COMBYNE research lab (www.combynelab.com).
Dr Bauer conducts research in computational biology and biomedical engineering. His research focuses on developing computational models to better understand complex biological systems, particularly in the areas of neuroscience and oncology. He integrates multi-scale biological data with computational simulations to identify key factors that influence disease progression or treatment efficacy. His interdisciplinary approach bridges gaps between experimental biology and computational science, fostering new avenues for collaboration across disciplines. Notably, he is co-founder and spokesperson of the international BioDynaMo collaboration, which has created the open-source and high-performance agent-based modelling software BioDynaMo (www.biodynamo.org). BioDynaMo has been utilised by many interdisciplinary labs to conduct research as well as to train the next generation of computational biologists

Multi-scale approaches for the computational modelling of biological system self-organisation

Biological systems are usually very complex. This complexity arises from very simple initial settings via developmental processes of self-organization and local information exchange. Computational modelling allows to capture how biological complexity develops, and hereby provides the means to compare and test different hypotheses. However, such computational approaches are often challenging from a software as well as hardware perspective. Here, I will talk about some of our work studying the brain and cancer, where we employed innovative computational modelling to obtain insights into their time evolution on different scales. I will elaborate on the implications with regards to the underlying mechanisms as well as experimentally verifiable predictions. Finally, I will comment on future directions and opportunities.