Education Profile

  • Research Associate, Wellcome Sanger Institute, Hinxton 2018
  • PhD in Theoretical and Computational Biology, ETH Zurich 2014
  • MSc in Molecular Biology, International Max Planck Research School Goettingen 2011

Research Interests

​Professor Moradigaravand's research is on the evolution and epidemiology of infectious diseases, especially in the context of antimicrobial resistance. He utilises a broad range of genomic, phenomic and machine learning approaches to understand the dissemination of pathogens within and between environmental and clinical settings and to pinpoint genetic factors driving the evolution of pathogens. Moreover, he employs machine learning approaches for predicting bacterial complex phenotypic features, e.g. bacterial growth, antimicrobial resistance, and horizontal gene transfer, from genomic variants

Selected Publications

  • Danesh Moradigaravand, Sandra Reuter, Veronique Martin, Sharon J Peacock, Julian Parkhill, The dissemination of multidrug-resistant Enterobacter cloacae throughout the UK and Ireland. Nat Microbiol, 2016. 1: p. 16173.
  • Danesh Moradigaravand, Martin Palm, Anne Farewell, Ville Mustonen, Jonas Warringer, Leopold Parts, Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data. PLoS Comput Biol, 2018. 14(12): p. e1006258
  • Danesh Moradigaravand, Veronique Martin, Sharon J Peacock, Julian Parkhill, Evolution and Epidemiology of Multidrug-Resistant Klebsiella pneumoniae in the United Kingdom and Ireland. mBio, 2017. 8(1)
  • Danesh Moradigaravand, Christine J Boinett, Veronique Martin, Sharon J Peacock, Julian Parkhill, Recent independent emergence of multiple multidrug-resistant Serratia marcescens clones within the United Kingdom and Ireland. Genome Res, 2016. 26(8): p. 1101-9.
  • Sam Benkwitz-Bedford, Martin Palm, Talip Yasir Demirtas, Ville Mustonen, Anne Farewell, Jonas Warringer, Leopold Parts, Danesh Moradigaravand, Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes. mSystems, 2021. 6(4): p. e0034621.