(Faculty of Life Sciences) Krzysztof Poterlowicz, Senior Lect in Bioinformatics & Biostat at the University of Bradford Skip to content
researcher

Krzysztof Poterlowicz

Senior Lect in Bioinformatics & Biostat

Faculty/Dept/School School of Chemistry and Bioscience
Emailk.poterlowicz1@bradford.ac.uk
Telephone +441274 234737

Biography

I graduated in mathematics and applied statistics at the University of Wroclaw in Poland and continued my education at the University of Bradford studying computational modelling of the yeast cell cycle and obtained a MPhil degree in Bioinformatics. In 2009 I visited the Biotechnology Research Institute of the National Research Council Canada where my research involved computer simulation of molecular signalling cascades governing the development and differentiation of skin appendages. In 2010 I was awarded a ESPRC PhD fellowship in the Centre of Skin Sciences, University of Bradford. My PhD research project on the bioinformatics analyses of multi-level transcriptional and epigenetic regulation of epidermis  resulted in a number of publications (Journal of Cell Biology, FASEB, Development and Journal of Investigative Dermatology) and Best Paper Prize at the World Congress of Hair Research in Edinburgh in 2013. The same year I joined the Faculty of Life Sciences at the University of Bradford as a Lecturer in Bioinformatics where I teach computational biology, medical genetics and statistics. My lab is involved in the national and international focus groups (Elixir, The Carpentries, Galaxy Training Network, Northern BUG) with aim to develop and provide bioinformatics and medical informatics training for research students and staff.

Research

I have a principal interest in application of machine learning for identification of biomarkers that influence tissue regeneration and disease and development of sustainable software for biomedical data analysis.
Some of my lab recent projects includes:
  • EWAS-Galaxy: a tools suite for population epigenetics integrated into Galaxy
  • The use of machine learning in the assessment of burn injuries