
Dr Roman Bauer
About
Biography
Roman 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 (INI) at ETH Zürich and University of Zürich, working on simulations of cortical development. He then joined Newcastle University in September 2013 as a postdoctoral research associate and afterwards started his MRC fellowship project in September 2016 as an independent principal investigator. In June 2018 he took up an EPSRC UKRI Innovation Fellowship at the School of Computing and a joint affiliation with the Institute of Genetic Medicine, both at Newcastle University. In August 2020 he then became a lecturer at the Department of Computer Science, University of Surrey.
News
ResearchResearch interests
Roman Bauer's research focus on the computational modelling and analysis of biological dynamics, in particular those of the brain. He devises and analyses computational and statistical models of how tissue evolves (e.d. during development, degeneration or other types of processing, e.g. cryopreservation), in order to better understand the system dynamics. These models incorporate the interaction between cellular processes and physical laws of the extracellular environment. Since such a detailed approach can be very demanding from a computational point of view, his research also involves modern computing approaches and IT-related collaboration.
Research projects
Computational modelling of cryopreservation of biological tissueWe employ innovative computational modelling to optimise cryopreservation protocols.
Computational modelling of retinal developmentWe use agent-based computational modelling and use various biological data to model the development of the retina.
Neural network self-organizationWe make use of advanced computational modelling and analysis techniques to better understand and leverage how neural circuits self-organise and produce function.
AI for healthWe use AI methods to model how the brain changes during development and ageing, in health as well as in disease. To inform such modelling, we make use of various data, including retinal images, neuroimaging data and EEG data.
Research collaborations
BioDynaMo collaboration. This is an international collaboration in Computational Biology. More information can be found at www.biodynamo.org as well as www.romanbauer.net.
Research interests
Roman Bauer's research focus on the computational modelling and analysis of biological dynamics, in particular those of the brain. He devises and analyses computational and statistical models of how tissue evolves (e.d. during development, degeneration or other types of processing, e.g. cryopreservation), in order to better understand the system dynamics. These models incorporate the interaction between cellular processes and physical laws of the extracellular environment. Since such a detailed approach can be very demanding from a computational point of view, his research also involves modern computing approaches and IT-related collaboration.
Research projects
We employ innovative computational modelling to optimise cryopreservation protocols.
We use agent-based computational modelling and use various biological data to model the development of the retina.
We make use of advanced computational modelling and analysis techniques to better understand and leverage how neural circuits self-organise and produce function.
We use AI methods to model how the brain changes during development and ageing, in health as well as in disease. To inform such modelling, we make use of various data, including retinal images, neuroimaging data and EEG data.
Research collaborations
BioDynaMo collaboration. This is an international collaboration in Computational Biology. More information can be found at www.biodynamo.org as well as www.romanbauer.net.
Publications
Highlights
Breitwieser, L., Hesam, A., Montigny, J., Vavourakis, V., Iosif, A., Jennings, J., Kaiser, M., Manca, M., Di Meglio, A. and Al-Ars, Z., Rademakers, F., Bauer, R. 2021. BioDynaMo: an agent-based simulation platform for scalable computational biology research. Bioinformatics.
Bauer, R., Clowry, G.J. and Kaiser, M., 2021. Creative destruction: a basic computational model of cortical layer formation. Cerebral Cortex, 31(7), pp.3237-3253.
Pegah Kassraian-Fard, Michael Pfeiffer, Roman Bauer. A generative growth model for thalamocortical axonal branching in primary visual cortex. PLoS Computational Biology, 2020
Peraza, L.R., Díaz-Parra, A., Kennion, O., Moratal, D., Taylor, J.P., Kaiser, M., Bauer, R. and Alzheimer's Disease Neuroimaging Initiative, 2019. Structural connectivity centrality changes mark the path toward Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 11, pp.98-107.
Gene regulatory networks represent collections of regulators that interact with each other and with other molecules to govern gene expression. Biological signalling networks model how signals are transmitted and how activities are coordinated in the cell. The study of the structure of such networks in complex diseases such as cancer can provide insights into how they function, and consequently, suggest suitable treatment approaches. Here, we explored such topological characteristics in the example of a mitogen-activated protein kinase (MAPK) signalling network derived from published studies in cancer. We employed well-established techniques to conduct network analyses, and collected information on gene function as obtained from large-scale public databases. This allowed us to map topological and functional relationships, and build hypotheses on this network's functional consequences. In particular, we find that the topology of this MAPK network is highly non-random, modular and robust. Moreover, analysis of the network's structure indicates the presence of organisational features of cancer hallmarks, expressed in an asymmetrical manner across communities of the network. Finally, our results indicate that the organisation of this network renders it problematic to use treatment approaches that focus on a single target. Our analysis suggests that multi-target attacks in a well-orchestrated manner are required to alter how the network functions. Overall, we propose that complex network analyses combined with pharmacological insights will help inform on future treatment strategies, exploiting structural vulnerabilities of signalling and regulatory networks in cancer.
Additional publications
Varghese, D., Bauer, R., Baxter-Beard, D., Muggleton, S. and Tamaddoni-Nezhad, A., Human-like rule learning from images using one-shot hypothesis derivation.
Cogno, N., Bauer, R. and Durante, M., 2022. A 3D Agent-Based Model of Lung Fibrosis. Symmetry, 14(1), p.90.
Axenie, C., Bauer, R. and Martínez, M.R., 2021. The Multiple Dimensions of Networks in Cancer: A Perspective. Symmetry, 13(9), p.1559.
de Montigny, J., Iosif, A., Breitwieser, L., Manca, M., Bauer, R. and Vavourakis, V., 2021. An in silico hybrid continuum-/agent-based procedure to modelling cancer development: Interrogating the interplay amongst glioma invasion, vascularity and necrosis. Methods, 185, pp.94-104.
Bauer, R. and Kaiser, M., 2017. Nonlinear growth: an origin of hub organization in complex networks. Royal Society open science, 4(3), p.160691.
Zubler, F., Hauri, A., Pfister, S., Bauer, R., Anderson, J.C., Whatley, A.M. and Douglas, R.J., 2013. Simulating cortical development as a self constructing process: a novel multi-scale approach combining molecular and physical aspects. PLoS Comput Biol, 9(8), p.e1003173.