Mikhail Tikhonov

Mikhail Tikhonov

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

contact info:

mailing address:

  • Washington University
  • CB 1105
  • One Brookings Drive
  • St. Louis, MO 63130-4899
image of book cover

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 serious 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

Selected Topics in Physics I (Physics 589)

CRITICAL ANALYSIS OF SCIENTIFIC DATA: "Data science" is most commonly associated with topics in computer science; however, efficient algorithms, specific software packages, or "deep learning" 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.