Mikhail Tikhonov

Mikhail Tikhonov

Assistant Professor of Physics
research interests:
  • Theoretical biophysics
  • Evolution
  • Microbiome

contact info:

mailing address:

  • Washington University
    MSC 1105-110-02
    One Brookings Drive
    St. Louis, MO 63130-4899

Theoretical physics of ecology and evolution

The health of our planet, and our own, is shaped by microbial communities that harbor hundreds of coexisting "species." Their study is a very active and exciting field: an enormous amount of data is now available, with more being collected every day, and yet we still know very little about these microbial ecosystems. 

The challenge is not just experimental and technological: it is also theoretical. Microbiology and microbiome research are revolutionizing biology, making us question some of the most basic concepts, such as "species," "fitness," and even "organism." What if our macroscopic intuition about ecology and evolution is simply wrong at the scale of microbial life? 

To make progress, data is essential, but not sufficient. The mission of our group is to combine data-intensive inquiry with a theoretical effort, bringing tools and ideas from physics to conceptual problems in biology. Drawing on the rigorous tradition of statistical physics and close experimental collaborations, our aim is to develop high-diversity ecology as a field at the intersection of statistical physics, classical ecology, experimental microbiology and bioinformatics.

Other interests include genetic regulatory networks, developmental biology, and information theory.

Professional History

  • 2018-present: Assistant professor, Washington University
  • 2017-2018: Postdoctoral fellow, Stanford University
  • 2014-2017: Postdoctoral fellow, Harvard University

recent courses

Critical Analysis of Scientific Data (Physics 481/581)

"Data science" is most commonly associated with topics in computer science. But efficient algorithms, specific software packages, neural nets, etc., are only tools, and are easily misused. In a research setting, working with data is primarily an exercise in critical thinking. The purpose of this interactive, hands-on course is to learn from mistakes by making them in a safe environment. After covering/reviewing probability theory; Bayesian inference; elements of information theory and random matrix theory, the course will focus on case studies of real-world biological data, such as quantitative imaging data, next-generation sequencing (metagenomics), and neural recordings. These modules will involve critical reading of research papers and working through puzzle-based assignments. The primary modules will be supplemented by shorter presentations on topics chosen by students. Fair warning: this is explicitly NOT a course on "big data" or machine learning, although students may choose to explore some of these topics in their presentations (required for credit).

    Mechanics (Physics 411)

    Motion of a point particle, rotational motion, oscillation, gravitation and central forces, Lagrangian and Hamiltonian formulation.

      Introduction to Computational Physics (Physics 427)

      What does it mean to solve a research problem using a computer? What is the difference between "someone ran a simulation" and an interesting research result? And what skills does it take? Familiarity with a programming language is, of course, essential, but that is only the beginning. This course will focus on the methodology of computational research, touching also on topics in numerical analysis, statistics and visualization. The format will combine lectures and hands-on experience, with emphasis on research-style small-group projects. Prerequisites: Physics 1 (191/192 or 197/198), Calculus, and familiarity with a programming language.

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