Photo of Dr Philipp Antczak

Dr Philipp Antczak

NERC Fellow, Associate Director Computational Biology Facility Functional and Comparative Genomics


    Effects of Stressors on Organisms in the Environment
    Zebrafish Larvae during exposure to a compound.
    Zebrafish Larvae during exposure to a compound.

    At the centre of my research interests stands the application of advanced computational tools to understand the effect stresses have on organisms in the environment. These include effects of environmental change as well as chemical effects. Currently I am developing a dataset that is characterising the effect of 200 compounds of environmental concern in zebrafish (D. rerio). The idea being that by having such a large understanding of the effects, it should possible to develop predictive models of effect and hence improve environmental health and regulation. This dataset will also provide a suitable piece of information that might help us with clustering of compounds based on effect, the underlying molecular basis of narcosis and provide knowledge on the suitability of the zebrafish larvae test (OECD TG 236) for regulatory applications.

    Integrating OMICs data and DEBTox Modelling

    In ecotoxicology research, two approaches have been gathering support because of how each tackles problems related to risk assessment. One is the adverse outcome pathway (AOP) framework, which links molecular initiating events to adverse outcomes at higher levels of biological organization. The second approach is dynamic energy budget (DEB) modeling, which employs a mechanistic approach to determining allocation of energy to growth and reproduction and that can be applied to multiple species. Furthermore, DEBs have been embedded into individual-based models (IBMs), allowing for extrapolation to population impacts. This working group will bring together a multi-disciplinary group of molecular biologists, systems biologists, DEB and AOP modelers, ecotoxicologists and mathematicians with interest and expertise in developing dynamic, mechanistic models to predict impacts on individuals from high throughput assays used to screen chemicals for potential risk. The working group will use a case study approach to demonstrate proof of concept and will aim to develop, not only example models, but a general framework for model development, evaluation, and communication that can be applied across different levels of biological organization and ecotoxicological endpoints relevant to the individual. The working group will closely coordinate with the working group on Modeling Organisms-to-Ecosystems chaired by Valery Forbes and Christopher Salice, using the same case study species (i.e. daphnids and trout), with the intent to develop a modeling framework that ultimately can link from molecular responses (AOPs) through whole organism responses to ecosystem service delivery. (NIMBIOS Working Group)

    Computational Workflows for Data Interpretation

    In parallel to my efforts in the environmental field, I am also active in the Biomedical field where data depth is generally less of a problem as compared to the environmental counterpart. Here I develop methodology underpinning multi-omics data integration, predictive/statistical/network modelling of the molecular response, and linkage of phenotypic measurements to molecular responses. These workflows are then applied to a variety of datasets resulting from almost all OMICs technologies to understand the underlying biology whether it is Rheumatoid Arthritis, Osteoarthritis, Cancer or other diseases/syndromes.

    Research Group Membership
    Research Grants
    • Developing the Ecotoxicological – Predictive – Information – Connectivity Map (EPIC-map)
    • Developing the Ecotoxicological – Predictive – Information – Connectivity Map (EPIC-map)
    • Developing an ECODB web-service to store and analyse any type of OMICS data
    • To evaluate the necessity of using adult fish over fish embryos for toxicity testing
    • Developing the Ecotoxicological - Predictive - Information - Connectivity Map (EPIC-map)

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