Michael Landis

Michael Landis

Assistant Professor of Biology
PhD, University of California, Berkeley
research interests:
  • computational biology
  • statistical inference
  • phylogenetics
  • evolution
  • ecology
  • biogeography
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    contact info:

    mailing address:

    • Washington University
      CB 1137
      One Brookings Drive
      St. Louis, MO 63130-4899
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    The Landis Lab integrates insights from evolution, ecology, statistics, and computer science to study how life radiated throughout and adapted to an ever-changing world.

    Michael Landis is interested in learning how evolutionary processes behave and how Earth's biodiversity has changed over time. His lab at Washington University develops statistical models and scientific software to search for evolutionary patterns in biological and simulated datasets. In particular, he is interested in inferring phylogenetic relationships among species, estimating historical patterns of biogeography, and learning how phenotypes evolve over millions of years. His lab focuses research towards macroevolutionary questions in phylogenomics, biogeography, trait evolution, and statistical inference. To this end, the lab develops probabilistic models of evolution, writes open source and community-minded software to analyze those models, and tests evolutionary hypotheses by fitting those models to biological and simulated datasets. Landis earned his doctorate from the University of California, Berkeley, and most recently was an NSF Postdoctoral Fellow at Yale University.

    Recent Courses

    Practical Bioinformatics

    How does biological research benefit from computational thinking? In this course, student learn how to design research workflows, decompose complex problems into simpler solvable units, and apply scientific computing principles to research. Students also practice foundational computing skills, such as how to use the UNIX operating system on research clusters, write custom analysis programs with shell scripts and with Python, and summarize and visualize analysis output. The laboratory exercises build on one another, culminating in the construction of a bioinformatics pipeline that can process and analyze molecular data.

      Selected Publications

      Complete publication list on Google Scholar

      Braga, M. P., Janz, N., Nylin, S., Ronquist, F., & Landis, M. J. (2021). Phylogenetic reconstruction of ancestral ecological networks through time for pierid butterflies and their host plants. bioRxiv.

      Landis, M. J., Eaton, D. A. R., Clement, W. L., Park, B., Spriggs, E. L., Sweeney, P. W., Edwards, E. J., & Donoghue, M. J. (2021). Joint phylogenetic estimation of geographic movements and biome shifts during the global diversification of Viburnum. Systematic Biology 70: 67-85.

      Landis, M., Edwards, E. J., & Donoghue, M. J. (2021). Modeling phylogenetic biome shifts on a planet with a past. Systematic Biology, 70: 86-107.

      Landis, M. J. (2020). Biogeographic Dating of Phylogenetic Divergence Times Using Priors and Processes. In The Molecular Evolutionary Clock (pp. 135-155). Springer, Cham.

      Braga, M. P., Landis, M. J., Nylin, S., Janz, N., & Ronquist, F. (2020). Bayesian Inference of Ancestral Host–Parasite Interactions under a Phylogenetic Model of Host Repertoire Evolution. Systematic Biology 69: 1149-1162.

      Kim, A. S., Zimmerman, O., Fox, J. M., Nelson, C. A., Basore, K., Zhang, R., Durnell, L., Desari, C., Deem, S. L.,  Oppenheimer, J., Shapiro, B., Wang, T., Cherry, S., Coyne, C. B., Handley, S. A., Landis, M. J., Fremont, D. H., & Diamond, M. S. (2020). An evolutionary insertion in the Mxra8 receptor-binding site confers resistance to alphavirus infection and pathogenesis. Cell Host & Microbe 27: 428-440.

      Quintero, I., & Landis, M. J. (2020). Interdependent phenotypic and biogeographic evolution driven by biotic interactions. Systematic Biology 69: 739–755.

      Landis, M. J., Freyman, W. A., & Baldwin, B. G. (2018). Retracing the Hawaiian silversword radiation despite phylogenetic, biogeographic, and paleogeographic uncertainty. Evolution, 72: 2343-2359.

      Landis, M. J., & Schraiber, J. G. (2017). Pulsed evolution shaped modern vertebrate body sizes. Proceedings of the National Academy of Sciences 114: 13224-13229.

      Höhna, S., Landis, M. J., Heath, T. A., Boussau, B., Lartillot, N., Moore, B. R., Huelsenbeck J. P., & Ronquist, F. (2016). RevBayes: Bayesian phylogenetic inference using graphical models and an interactive model-specification language. Systematic Biology 65: 726-736.

      Schraiber, J. G., & Landis, M. J. (2015). Sensitivity of quantitative traits to mutational effects and number of loci. Theoretical Population Biology: 102, 85-93.

      Landis, M. J., & Bedford, T. (2014). Phylowood: interactive web-based animations of biogeographic and phylogeographic histories. Bioinformatics 30: 123-124.

      Landis, M. J., Matzke, N. J., Moore, B. R., & Huelsenbeck, J. P. (2013). Bayesian analysis of biogeography when the number of areas is large. Systematic Biology, 62: 789-804.

      Landis, M. J., Schraiber, J. G., & Liang, M. (2013). Phylogenetic analysis using Lévy processes: finding jumps in the evolution of continuous traits. Systematic Biology 62: 193-204.

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