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
We are developing and applying quantitative approaches for interpreting data in close collaboration with experimentalists.
People
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
Ian Bongalonta
I am developing computational methods to interpret 2D IR spectra and applying them to studying intrinsically disordered proteins.
graduate student | since 2023
Chris Chi
I am studying the properties of the KaiABC oscillator system by fitting reaction network models to experimental data.
graduate student | since 2019
Carlos Floyd
I am using theory and simulation to understand hallmark features of active biological systems, including contractility and mechanical memory.
postdoctoral scholar | since 2021
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
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
Kwanghoon Jeong
I am studying insulin dimer dissociation by combining the maximum caliber approach with transition path theory and Markov state modeling to analyze molecular dynamics simulation data.
graduate student | since 2022
Darren Liu
I am combining experiments and simulations to study the physics of directed evolution.
graduate student | since 2023
Chatipat Lorpaiboon
I use machine learning and transition path theory to develop data-driven methods for analyzing stochastic dynamics. In particular, I am focusing on how to account for history in such approaches.
graduate student | since 2018
Zihan Pengmei
I am exploring neural-network architectures for learning protein dynamics.
graduate student | since 2023
Yuqing Qiu
I am combining tools of stochastic thermodynamics, simulations, and machine learning to understand the factors that control how actomyosin networks access emergent states far from equilibrium.
postdoctoral scholar | since 2019
Sarah Root
I am studying the biology of mechanosensing proteins in health and disease with microscopy and machine learning.
graduate student | since 2023
Jordan Shivers
I use theoretical and computational tools from soft matter physics, nonequilibrium thermodynamics, and machine learning to study emergent behavior in active assemblies of cellular components.
postdoctoral scholar | since 2022
John Strahan
My research focuses on development and validation of machine-learning methods for estimating forecast statistics from trajectory data.
graduate student | since 2018
Nils Strand
I am developing tensor-network methods for density estimation to address biological problems.
postdoctoral scholar | since 2023
Yihang Wang
My research focuses on parameter estimation for reaction network models using machine learning and Markov Chain Monte Carlo methods.
postdoctoral scholar | since 2022
Recent publications
Selected recent publications are highlighted below. For a full list, see PubMed or Google Scholar.
Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction
John Strahan, Spencer C Guo, Chatipat Lorpaiboon, Aaron R Dinner, and Jonathan Weare
J. Chem. Phys. 159, 014110 (2023)
Journal of Chemical Physics 159, 014110 (2023) Link
Dynamics of activation in the voltage-sensing domain of Ci-VSP
Spencer C Guo, Rong Shen, Benoit Roux, and Aaron R Dinner
biorxiv.org (2023) Link
A unified model for the dynamics of ATP-independent ultrafast contraction
Carlos Floyd, Arthur T Molines, Xiangting Lei, Jerry E Honts, Fred Chang, Mary Willard Elting, Suriyanarayanan Vaikuntanathan, Aaron R Dinner, and Saad Bhamla
PNAS 120, e2217737120 (2023) Link
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