The Dinner group seeks to develop a theoretical understanding of how complex biological behavior arises from molecular interactions. Because the defining properties of living systems (growth, movement, and directed response to environmental stimuli) rely on irreversible energy consumption and dissipation, much of our research centers on stochastic processes far from equilibrium. We quantitatively analyze experimental data on living systems, construct physical models to interpret the observed statistics, and implement algorithms for efficiently simulating the dynamics of such models.

Research interests

Accelerating molecular simulations


We are developing, mathematically characterizing, and employing algorithms for enhancing the sampling of rare events and recovering their statistics.

Modeling living systems


We are building models to interpret dynamics observed in cells and exploring the behavior of these models both analytically and numerically.

Analyzing biological data

image analysis

We are developing and applying quantitative approaches for interpreting data in close collaboration with experimentalists.

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Present members of the group are listed below. Information about former members can be found here.
Aaron Dinner

Aaron Dinner

I am a Professor of Chemistry and Deputy Dean of the Physical Sciences Division at the University of Chicago. I also hold appointments in the James Franck Institute and the Institute for Biophysical Dynamics. My group’s most well known contributions are to machine learning methods for interpreting complex biomolecular simulations, sampling methods for systems far from equilibrium, and models of hematopoietic cell fate choice. Much of my current work is collaborative, and many of my students and postdoctoral scholars are jointly mentored by experimentalists and/or applied mathematicians. My honors include a Searle Scholarship, NSF CAREER Award, Sloan Fellowship, APS Fellowship, and participation in the 2010 and 2014 Latke-Hamantash Debates.

Prior to joining the faculty at Chicago, I obtained my undergraduate (AB in Biochemical Sciences, 1994) and graduate (PhD in Biophysics, 1999) degrees at Harvard University, where I worked with Martin Karplus on Monte Carlo methods and their application to protein folding. Subsequently, I pursued postdoctoral studies at the University of Oxford (1999-2001), where I used hybrid quantum-mechanical/molecular-mechanical (QM/MM) methods to elucidate mechanisms of DNA repair, and the University of California, Berkeley (2001-2003), where I worked with David Chandler on transition path sampling and Arup Chakraborty on models of T lymphocyte signaling.


professor | since 2003
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photo: Sammy Allaw

Sammy Allaw

I am applying machine learning methods to predicting committor functions from molecular dynamics data.

undergraduate student | since 2020
photo: Adam Antoszewski

Adam Antoszewski

My research combines molecular dynamics, enhanced sampling algorithms, and dynamical analysis techniques to study insulin: dimer dissociation, phenol release from the hexamer, and receptor binding. The simulations probe how proteins couple folding with binding and can help identify novel approaches for treating diabetes mellitus.

graduate student | since 2017
photo:  Chris Chi

Chris Chi

I am studying the properties of the KaiABC oscillator system by fitting reaction network models to experimental data.

graduate student | since 2019
photo:  Carlos Floyd

Carlos Floyd

My research focuses on the hallmark features of active biological systems, including dissipation, contractility, and mechanical memory.

postdoctoral scholar | since 2021
photo:  Noah Gamble

Noah Gamble

I employ genomics, phenomenological modeling, and biochemical approaches to investigate gene expression and its consequences for cellular plasticity in the immune system.

graduate student | since 2020
photo:  Spencer Guo

Spencer Guo

I am studying voltage-dependent activation of a voltage-sensitive phosphatase and dissociation of the insulin dimer with methods for estimating kinetics and elucidating mechanisms from short-trajectory data.

graduate student | since 2021
photo:  Chatipat Lorpaiboon

Chatipat Lorpaiboon

I use machine learning and the operator theory of dynamical systems to develop data-driven methods for analyzing nonequilibrium chemical systems.

graduate student | since 2018
photo: Yuqing Qiu

Yuqing Qiu

I am combining tools of stochastic thermodynamics and simulations to identify efficient strategies that actomyosin networks use to access emergent states away from equilibrium.

postdoctoral scholar | since 2019
photo:  Steven Redford

Steven Redford

I am combining experiments, simulations, and image analysis to study materials comprised of cytoskeletal polymers. I am particularly interested in cases in which these materials are driven out of equilibrium by molecular motors.

graduate student | since 2018
photo:  John Strahan

John Strahan

My research focuses on development and validation of new methods for understanding long-time phenomena from trajectory data. To this end, I am exploring machine learning approaches including kernel methods and adversarial neural networks.

graduate student | since 2018
photo:  Bodhi Vani

Bodhi Vani

I am developing algorithms for enhanced sampling of nonequilibrium processes.

graduate student | since 2015
photo:  Elizabeth White

Elizabeth White

I am combining experiments, simulations, and image analysis to study cytoskeletal dynamics in the C. elegans embryo in order to elucidate the mechanisms of contractility that are operative at different stages of development.

graduate student | since 2018
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Recent publications

Selected recent publications are highlighted below. For a full list, see PubMed or Google Scholar.

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Long-time-scale predictions from short-trajectory data: A benchmark analysis of the trp-cage miniprotein
John Strahan, Adam Antoszewski, Chatipat Lorpaiboon, Bodhi P. Vani, Jonathan Weare, and Aaron R Dinner
Journal of Chemical Theory and Computation 17, 2948-2963 (2021) Link

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A strong nonequilibrium bound for sorting of cross-linkers on growing biopolymers
Yuqing Qiu, Michael Nguyen, Glen M Hocky, Aaron R Dinner, and Suriyanarayanan Vaikuntanathan
PNAS 118, e2102881118 (2021) Link

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Bayesian modeling reveals metabolite-dependent ultrasensitivity in the cyanobacterial circadian clock
Lu Hong, Danylo O Lavrentovich, Archana Chavan, Eugene Leypunskiy, Eileen Li, Charles Matthews, Andy LiWang, Michael J Rust, and Aaron R Dinner
Molecular Systems Biology 16, e9355 (2020) Link

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Insulin dissociates by diverse mechanisms of coupled unfolding and unbinding
Adam Antoszewski, Chi-Jui Feng, Bodhi P Vani, Erik H Thiede, Lu Hong, Jonathan Weare, Andrei Tokmakoff, and Aaron R Dinner
Journal of Physical Chemistry B 24, 5571−5587 (2020) Link

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