The Faculty


Modeling complex dynamics


Born Chicago, Illinois, 1972
Harvard University, A.B., 1994, Ph.D., 1999
University of Oxford, 1999-2001
University of California, Berkeley, 2001-2003
University of Chicago, Professor, 2003- 
Director, James Franck Institute, 2012-
Deputy Dean, Physical Sciences Division, 2019-


2016 American Physical Society Fellowship  2016
2009 American Chemical Society Hewlett-Packard Outstanding Junior Faculty Award
2008 Alfred P. Sloan Fellow.
2006 NSF CAREER Award.
2005 Searle Scholar.
2003 Dreyfus New Faculty Award.
2000-2001 Linacre College EPA Cephalosporin Junior Research Fellow.
1999 Burroughs Wellcome Fund Hitchings-Elion Postdoctoral Fellow.
1994-1999 Howard Hughes Medical Institute Predoctoral Fellow

Research Interests

My group and I develop and apply theoretical and computational approaches for relating biological behavior to underlying molecular interactions.  Our effort is in three broad areas:

Physical models of biological systems:  Because the defining properties of life (growth, movement, and directed response to environmental stimuli) rely on irreversible energy consumption and dissipation, much of our research centers on the statistics of stochastic systems far from equilibrium.  This is an exciting time in this area as basic principles have emerged over the last two decades, and there is now the opportunity to explore how they apply in real systems in order to realize their potential for designing and interpreting experiments. To this end, my group and I are working to develop phenomenological models in close collaboration with researchers acquiring data amenable to quantitative analysis (e.g., cellular images, expression levels, forces).  We have worked on cytoskeletal, circadian, and developmental problems in reconstituted systems with purified components, cell lines, bacteria, flies, and mice.

Enhanced sampling algorithms:  One feature that makes studies of biological behavior challenging is that the relevant dynamics span a hierarchy of time and length scales ranging from Ångstroms and femtoseconds to millimeters and days. Experiments are now beginning to bridge the gaps in spatial and temporal resolution, and models are vital for design and interpretation of such measurements. Corresponding simulations require methods that can increase exploration of states that are rarely visited in comparison with the time scale of molecular fluctuations (e.g., transition states) while still enabling recovery of unbiased statistical averages. We developed some of the most general and efficient methods available for accelerating the convergence of properties of microscopically irreversible models (nonequilibrium umbrella sampling and steered transition path sampling); we are now working with applied mathematicians to analyze these methods rigorously to improve and extend them.  Additionally, we are exploring dimensional reduction techniques (i.e., automated methods for identifying physically informative reaction coordinates in complex systems) to aid in interpreting simulations and connecting them with experimental observables.

Reverse engineering regulatory networks:  Extensive data from high throughput (e.g., genome-wide) experiments are now available for many biological systems, but it remains unclear how best to use this information to formulate mechanistic and predictive models.  We introduced, validated, and deployed the first approach for combining binding and expression measurements to identify long-range (in DNA sequence) gene regulatory targets of transcription factor binding events (EMBER:  Expectation Maximization of Binding and Expression pRofiles).  We continue to investigate methods for integrating diverse types of data for better elucidation of molecular interactions.

Selected References

Thiede, Erik H., Giannakis, Dimitrios, Dinner, Aaron R., and Weare, Jonathan. "Galerkin approximation of dynamical quantities using trajectory data," The Journal of Chemical Physics 150, no. 24 (2019): 244111.

Dinner, Aaron R., Jonathan C. Mattingly, Jeremy O. B. Tempkin, Brian Van Koten, and Jonathan Weare. "Trajectory stratification of stochastic dynamics." SIAM Review, 60 no. 4 (2018): 909-38.  

Freedman, Simon L., Shiladitya Banerjee, Glen M. Hocky, and Aaron R. Dinner. "A versatile framework for simulating the dynamic mechanical structure of cytoskeletal networks." Biophysical Journal 113, no. 2 (2017): 448-460.

Stam, Samantha, Simon L. Freedman, Shiladitya Banerjee, Kimberly L. Weirich, Aaron R. Dinner, and Margaret L. Gardel. "Filament rigidity and connectivity tune the deformation modes of active biopolymer networks." Proceedings of the National Academy of Sciences (2017): E10037-E10045.

Burov, Stanislav, Patrick Figliozzi, Binhua Lin, Stuart A. Rice, Norbert F. Scherer, and Aaron R. Dinner. "Single-pixel interior filling function approach for detecting and correcting errors in particle tracking." Proceedings of the National Academy of Sciences 114, no. 2 (2017): 221-226.

Banerjee, Shiladitya, Klevin Lo, Matthew K. Daddysman, Alan Selewa, Thomas Kuntz, Aaron R. Dinner, and Norbert F. Scherer. "Biphasic growth dynamics control cell division in Caulobacter crescentus." Nature Microbiology 2, no. 9 (2017): 17116.

Leypunskiy, Eugene, Jenny Lin, Haneul Yoo, UnJin Lee, Aaron R. Dinner, and Michael J. Rust. "The cyanobacterial circadian clock follows midday in vivo and in vitro." eLife 6 (2017): e23539.

Scholz, Monika, Stanislav Burov, Kimberly L. Weirich, Björn J. Scholz, SM Ali Tabei, Margaret L. Gardel, and Aaron R. Dinner. "Cycling state that can lead to glassy dynamics in intracellular transport." Physical Review X 6, no. 1 (2016): 011037.

Thiede, Erik H., Brian Van Koten, Jonathan Weare, and Aaron R. Dinner. "Eigenvector method for umbrella sampling enables error analysis." The Journal of Chemical Physics 145, no. 8 (2016): 084115. 

Tabei, SM Ali, Stanislav Burov, Hee Y. Kim, Andrey Kuznetsov, Toan Huynh, Justin Jureller, Louis H. Philipson, Aaron R. Dinner, and Norbert F. Scherer. "Intracellular transport of insulin granules is a subordinated random walk." Proceedings of the National Academy of Sciences 110, no. 13 (2013): 4911-4916.