Carson C. Chow, Ph.D.

Senior Investigator

Mathematical Biology Section, Laboratory of Biological Modeling


Building 12A, Room 4007
12 South Dr
Bethesda, MD 20814

+1 301 467 6154

Research Topics

The ultimate goal of my research is to understand the biological and genetic mechanisms of common diseases.

Current Research

A major difficulty for understanding biological systems is that they span a broad range of scales. My lab aims to connect the microscopic (e.g., genetic or molecular) level to the macroscopic (e.g., phenotypic) level using mathematical and computational tools. One problem in bridging this gap is that direct modeling at the microscopic level is extremely difficult. My approach is to develop and analyze models at an intermediate mesoscopic level that are biophysically informed by the microscopic level but are simple enough to make quantitative predictions at the macroscopic level. This can also be applied in reverse, where the macroscopic level provides constraints on the mesoscopic level that can then isolate the key components at the microscopic level. When the appropriate mathematical and computational tools do not exist, I develop new ones. My research is thus divided between purely theoretical research and applied work in collaboration with experiments on topics in metabolism, gene transcription, human genetics, and neuroscience.

Applying our Research

One of the reasons that common diseases such as obesity and autism are notoriously difficult to treat is because they are highly complex and span many scales. I hope that my research will lead to new and better treatments by providing improved quantitative understanding of the underlying biology.

Need for Further Study

We are in the midst of an explosion of new sequencing and single cell imaging data. Understanding and integrating this data will require new mathematical and computational tools.


  • Ph.D., Massachusetts Institute of Technology, 1992

Selected Publications

  1. Rodriguez J, Ren G, Day CR, Zhao K, Chow CC, Larson DR. Intrinsic Dynamics of a Human Gene Reveal the Basis of Expression Heterogeneity. Cell. 2019;176(1-2):213-226.e18.

  2. Kim CM, Chow CC. Learning recurrent dynamics in spiking networks. Elife. 2018;7.

  3. Chow CC, Ong KM, Kagan B, Simons SS Jr. Theory of partial agonist activity of steroid hormones. AIMS Mol Sci. 2015;2(2):101-123.

  4. Vattikuti S, Lee JJ, Chang CC, Hsu SD, Chow CC. Applying compressed sensing to genome-wide association studies. Gigascience. 2014;3:10.

This page was last updated on June 3rd, 2021