Cologne, North Rhine-Westphalia, Germany
I have developed an exceptionally broad interdisciplinary skillset, with a strong basis in computational analysis (BSc in Bioinformatics and a PhD in Computational Biology), with an in-depth working knowledge of theoretical and experimental biology. My research interests use integrative post-genomic screening approaches to understand organism responses to physico-chemical stress and I am involved in the development of the Adverse Outcome Pathway (AOP) concept and its utilization for risk assessment and environmental monitoring. This integrates multiple levels of knowledge into a conceptual network linking the molecular initiating event (potential chemical interaction) via key events (molecular changes) to adverse outcome (undesired phenotypic results). It has been included in a draft OECD guideline for use of AOPs in risk assessment and thus will help shape future chemical evaluation and monitoring efforts for regulators. The unifying strategy behind this science, and a central part of AOP development, is to use massively parallel methods of screening responses in living systems in combination with advanced computational model-building approaches for integrating multiple layers of data into an interpretive framework. My goal is to engage in the establishment of a more comprehensive framework for chemical exposures, incorporating ecological and physiological elements and develop solutions to challenges in environmental science.
Leading the Computational Biology of Ageing Group at the Center for Molecular Medicine Cologne. Specialising in data integration, statistical and predictive modelling, as well as network biology to better understand the impact of stress and disease on the processes of Ageing.
CTO at our startup medlink. Medlink looks to connect Healthcare professionals and Healthcare organisations through digital innovations. Find out more at www.medlink.de
Lead scientific projects in the Computational Biology Facility, part of the Technology Directorate, at the Institute for Integrative Biology at the University of Liverpool. Direct and manage a number of PostDocs relating to projects with Bioinformatic new and establish collaborations across science.
Development of the Ecotoxicological Predictive Information Connectivity map (EPIC-map). This NERC funded fellowship is looking into developing a set of computational approaches to develop quantitative Adverse Outcome Pathways (AOP) from molecular data including facilities for read-across and prediction of chemical exposures. The EPIC-map will allow the wider scientific community to discover AOPs, predict effect of new chemicals and provide a basis for chemical classification via molecular responses in a regulatory context established species.
Manage the newly established Computational Biology Facility, part of the Technology Directorate, at the Institute for Integrative Biology at the University of Liverpool. With a number of PostDocs establish bespoke analysis pipelines for Projects with Bioinformatic need and establish new collaborations driving the use of computational biology in novel grant applications.
Representing the computational biology aspect in the multi-colaborative SOLUTIONS project.
Post-Doctoral position as a computational biologist in Cancer Research, Ecotoxiocology and General Medical Research.
Human health and the environment are at risk of being exposed to a large number of hazardous chemicals each day. Unfortunately, many of these chemicals have no or little recorded toxicity information. Predictive toxicology aims to provide tools and methodologies to address this issue. In combination with systems biology approaches these can provide a powerful toolbox for understanding the impact of chemicals on biological species. The work presented within this thesis demonstrates the effectiveness of such approaches in the context of industrial and environmentally relevant species. More specifically we focus on characterization of a general toxicity mechanism in Rattus norvegius and Daphnia magna as well as for the first time demonstrate that the transcriptional response of D. magna is predictive not only of chemical class but also of measured toxicity. We also show that inclusion of pathway-level information can increase biological interpretability in non-model species. Lastly, we provide evidence supporting the application of reverse engineering methodologies in the context of identifying adverse outcome pathways in Pimephales promelas, an environmentally relevant species. Ultimately, our results have shown that these approaches can provide highly relevant information in model and non-model species. Further development building on these results could potentially lead to improvements in risk assessment and environmental monitoring.