Statistical Physics and Quantitative Biology
University of Cologne
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Viral evolution

Human influenza A is one of the fastest-mutating viruses — and a laboratory for evolutionary innovation. But how many of these mutations are adaptive and drive influenza's antigenic evolution? We analyze viral genome sequences by a new method and find this number is surprisingly large. In a typical year, several driver mutations coexist in different strains and compete for fixation. This is the first evidence of clonal interference in a wild system.

Given that multiple clades compete for evolutionary success, which clade will win? We have developed a fitness model for influenza that predicts the evolution of the viral population from one year to the next. Our analysis provides a principled method to select influenza vaccine strains.

Adaptation map of influenza: Strains between 2003 and 2008 are colored by year and linked by their lineages. The vertical coordinate shows the production of fitness in the adaptive process.
Population immunity predicts evolutionary trajectories of SARS-CoV-2
Meijers M., Ruchnewitz D., Eberhardt J., Łuksza M., Lässig M., Cell 186, 5151–5164 (2023)
Steering and controlling evolution — from bioengineering to fighting pathogens
Lässig M., Mustonen V., Nourmohammad A., Nat Rev Genet, (2023)
Stochasticity of infectious outbreaks and consequences for optimal interventions
Morán-Tovar R., Gruell H., Klein F., Lässig M., J. Phys. A: Math. Theor. 55 384008 (2022)
Effective high-throughput RT-qPCR screening for SARS-CoV-2 infections in children
Dewald F., Suárez I., Johnen R., Grossbach J., Moran-Tovar R., Steger G., Joachim A., Rubio G.H., Fries M. , Behr F., Kley J., Lingnau A., Kretschmer A., Gude C., Baeza-Flores G., Laveaga del Valle D., Roblero-Hernandez A., Magana-Cerino J., Hernandez A.T., Ruiz-Quinones J., Schega K., Linne V., Junker L., Wunsch M., Heger E., Knops E., Di Cristanziano V., Meyer M., Hünseler C., Weber L.T., Lüers L.C., Quade G., Wisplinghoff H., Tiemann C., Zotz R., Jomaa H., Pranada A., Herzum I., Cullen P., Schmitz F.J., Philipsen P., Kirchner G., Knabbe C., Hellmich M., Buess M., Wolff A., Kossow A., Niessen J., Jeworutzki S., Schräpler J.-P., Lässig M., Dötsch J., Fätkenheuer D., Kaiser R., Beyer A., Rybniker J., Klein F., Nature Communications, 13 (3640) , (2022)
Clinical and Genomic Characterization of SARS CoV-2 infections in mRNA Vaccinated Health Care Personnel in New York City
Robilotti E.V., Whiting K., Lucca A., Poon C., Guest R., McMillen T., Jani K., Solovyov A., Kelson S., Browne K., Freeswick S., Hohl T.M., Korenstein D., Ruchnewitz D., Lässig M., Łuksza M., Greenbaum B., Seshan V.E., Babady N.E., Kamboj M., Clinical Infectious Diseases, ciab886, (2021)
Predicting in vivo escape dynamics of HIV-1 from a broadly neutralizing antibody
Meijers M., Vanshylla K., Gruell H., Klein F., Lässig M., PNAS 118 (30) e2104651118, (2021)
Antigenic waves of virus–immune coevolution
Marchi J., Lässig M., Walczak A.M., Mora T., PNAS 118 (27) e2103398118, (2021)
Predicting evolution
Michael Lässig, Ville Mustonen, Aleksandra M. Walczak, Nature Ecology & Evolution, (2017), doi:10.1038/s41559-017-0077
Epidemiological and evolutionary analysis of the 2014 Ebola virus outbreak
M. Łuksza, T.Bedford, and M. Lässig, arxiv:1411.1722 (2014)
A predictive fitness model for influenza
M. Łuksza and M. Lässig, Nature, 507, 57-61 (2014)
Clonal interference in the evolution of influenza
N. Strelkowa and M. Lässig, Genetics 192, 671 - 682 (2012)

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