Computational Biology for Infection Research

The Department of “Computational Biology for Infection Research” studies the human microbiome, viral and bacterial pathogens, and human cell lineages within individual patients by analysis of large-scale biological and epidemiological data sets with computational techniques. Focusing on high throughput meta’omics, population genomic and single cell sequencing data, we produce testable hypotheses, such as sets of key sites or relevant genes associated with the presence of a disease, of antibiotic resistance or pathogenic evasion of immune defense. We interact with experimental collaborators to verify our findings and to promote their translation into medical treatment or diagnosis procedures. To achieve its research goals, the department also develops novel algorithms and software.


Computational biology of viral pathogens

The Research Department “Computational Biology for Infection Research” at the HZI studies rapidly evolving viral pathogens, such as influenza, hepatitis and human cytomegaloviruses and their coevolution with the adaptive immune response of the human host using computational techniques. A particular focus are influenza A viruses, where we combine epidemiological, genetic, antigenic and structural information on circulating viral strains to determine the antigenicity-altering areas on the protein structure, key sites and amino acid changes and analyze how these affect the viral fitness with regards to escaping human immune response [1,3,5]. In collaboration with infection biologists and immunologists from the HZI, the Hannover Medical School and the German Centre for Infection Research (DZIF), we also study the adaptation of influenza viruses to novel hosts and for maintaining fitness under immune selection in animal models. Viral pathogens such as hepatitis and human cytomegaloviruses differ from influenza viruses in that they cause chronic, not short-term infections. Together with collaborators we track the evolution of these pathogens and the corresponding adaptive immune ‘evolution’ of the host within individual patients over time. We thus aim to generate novel insights into pathogen-host co-evolution and identify leads of translational relevance, such as for development of a universal vaccine against hepatitis C virus infections.

We focus on:

  • Computational prediction of suitable vaccine strains for human influenza A
  • Determining epitopes for broadly neutralizing antibodies for development of a “universal vaccine” against hepatitis C virus infections
  • Reconstructing viral haplotypes from deep sequencing data
  • Inference of viral spread trajectories with viral phylogeographies
  • Development of a universal vaccine against influenza A viruses

SARS-COV-2 resources

High resolution global spread reconstruction of COVID-19 via air travel from genome and geographic location data available up to mid February 2020 using the method in (Reimering et. al,
PLOS Computational Biology, 2020
). Interactive visualisations using are available at

Selected Publications

  1. A.C. McHardy, B. Adams
    The role of genomics in tracking the evolution of influenza A virus.
    PLoS Pathogens 2009, 5(10): e1000566 
  2. L. Steinbrück, A.C. McHardy
    Inference of Genotype-Phenotype Relationships in the Antigenic Evolution of Human Influenza A (H3N2) Viruses.
    PLoS Comput Biol 2012, 8(4): e1002492

  3. C. Tusche, L. Steinbrück, A.C. McHardy
    Detecting patches of protein sites of influenza A viruses under positive selection.
    Mol Biol Evol 2012, 29(8): 2063-2071

  4. C. Kratsch, T.R. Klingen, L. Muemken, L. Steinbrueck, A.C. McHardy
    Determination of antigenicity-altering patches on the major surface protein of human influenza A/H3N2 viruses.
    Virus Evolution 2016, 2(1)

  5. L. Steinbrück, T.R. Klingen, A.C. McHardy
    Computational prediction of vaccine strains for human influenza A(H3N2) viruses.
    J Virol 2014, 88(20): 12123-32

  6. T.R. Klingen, S. Reimering, C.A. Guzman, A.C. McHardy
    In Silico Vaccine Strain Prediction for Human Influenza Viruses.
    Trends Microbiol 2018, 26(2):119-131

  7. T.R. Klingen, S. Reimering, J. Loers, K. Mooren, F. Klawonn, T. Krey, G. Gabriel, A.C. McHardy
    Sweep Dynamics (SD) plots: Computational identification of selective sweeps to monitor the adaptation of influenza A viruses
    Sci Rep 2018, 8(1):373

  8. T.R. Klingen, J. Loers, S. Stanelle-Bertram, G. Gabriel, A.C. McHardy
    Structures and functions linked to genome-wide adaptation of human influenza A viruses
    Sci Rep 2019, 9(1): 6267

  9. S. Reimering, S. Muñoz, A.C. McHardy
    Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic
    bioRxiv 2019, doi:


  • Akash Bahai
  • Adrian Fritz
  • Dr. Zhi-Luo Deng
  • Thorsten Klingen
  • Susanne Reimering


  • Gülsah Gabriel, Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
  • Carlos Guzmán, Department of Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
  • Thomas Pietschmann, Institute for Experimental Virology, Twincore Centre for Experimental and Clinical Infection Research, Hannover, Germany (DZIF collaboration)
  • Thomas Schulz, Institute of Virology, Hannover Medical School  (MHH), Hannover, Germany
  • Klaus Schughart, Infection Genetics, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
  • Thomas Krey, Institute of Virology, Hannover Medical School (MHH), Hannover, Germany
  • Wulf Blankenfeldt, Department of Structure and Functions of Proteins, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
  • Mohammad Mofrad, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, USA
PrintSend per emailShare