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.
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.
I am a Professor of Chemistry 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 statisticians. 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
I am combining machine learning methods with molecular dynamics to understand insulin.graduate student | since 2017
I am studying the molecular mechanisms that govern the function of the KaiABC circadian oscillator through molecular dynamics simulations, biochemical experiments, and Markov Chain Monte Carlo sampling in a Bayesian framework for fitting mathematical models to time series data.graduate student | since 2016
I am studying insulin dimer dissociation by sampling free energy surfaces for a coarse-grained model.undergraduate student | since 2017
I have been exploring a nonequilibrium statistical mechanical theory to understand the constraints on self-replicating assemblies. I am also developing a model to understand chemical waves in a biofilm.undergraduate student | since 2016
How can nonequilibrium thermodynamics be applied to complex biological processes such as self-replication, information processing, energy transduction, and gene expression? I am investigating this question with the aim of revealing general principles that can inform our understanding of living systems and guide the design of artificial active materials.postdoctoral scholar | since 2016
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
My research is at the intersection between machine learning, applied mathematics, and chemical kinetics. By taking an operator-theoretic view of dynamics, I am developing new ways to extrapolate the long-time behavior of molecular systems from short trajectories. In addition, I study dimensional reduction techniques to help gain insight into the complex motions of proteins.graduate student | since 2014
I study self-assembly processes in cells, specifically, those that occur during times of stress. Using both modeling and experimental approaches I hope to explain and predict the regulatory consequences of aggregation.graduate student | since 2015
I am developing statistical mechanical descriptions of nonequilibrium biomolecular processes. Specifically, I am using steered transition path sampling to study the mechanism of the dimer-to-monomer transition of insulin and simulating temperature-jump experiments for interpretation of infrared spectroscopy experiments on intrinsically disordered proteins.graduate student | since 2015
I am developing machine learning methods for particle tracking and cytoskeletal image analysis.graduate student | since 2018
Eigenvector method for umbrella sampling enables error analysis
Erik Thiede, Brian Van Koten, Jonathan Weare, and Aaron R Dinner
Journal of Chemical Physics 145, 084115 (2016) Link
A Versatile Framework for Simulating the Dynamic Mechanical Structure of Cytoskeletal Networks
Simon L Freedman, Shiladitya Banerjee, Glen M Hocky, Aaron R Dinner
Biophysical Journal 113, 448-460 (2017) Link
The cyanobacterial circadian clock follows midday in vivo and in vitro
Eugene Leypunskiy, Jenny Lin, Haneul Yoo, UnJin Lee, Aaron R Dinner, Michael J Rust
eLife 6, e23539 (2017) Link
Biphasic growth dynamics control cell division in Caulobacter crescentus
Shiladitya Banerjee, Klevin Lo, Matthew K. Daddysman, Alan Selewa, Thomas Kuntz, Aaron R Dinner, Norbert F Scherer
Nature Microbiology 2, 17116 (2017) Link