Aaron Dinner

About

Aaron Dinner is a Professor of Chemistry at the University of Chicago, affiliated with the Committee on Immunology. His research focuses on tailoring interactions between active nematic defects using reinforcement learning, as well as exploring the mechanics and thermodynamics of growing vesicles. His work involves advanced computational methods, including pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics. Additionally, he investigates complex biochemical reaction dynamics, such as insulin dimer dissociation, and studies mechanisms related to circadian clock proteins like KaiB. His research also extends to biophysical processes, including calcium-activated contraction in giant cells and viral evolution in chronic infections, contributing to the understanding of biological systems through computational and experimental approaches.

Research topics

• Chemistry
• Artificial Intelligence
• Biology
• Genetics
• Cell biology
• Computer Science
• Machine Learning
• Biochemistry
• Materials science
• Algorithm

Selected publications

• Machine learning force fields and coarse-grained variables in molecular\n dynamics: application to materials and biological systems

  Journal of Chemical Theory and Computation · 2020 · 224 citations

  Machine learning encompasses a set of tools and algorithms which are now\nbecoming popular in almost all scientific and technological fields. This is\ntrue for molecular dynamics as well, where machine learning offers promises of\nextracting valuable information from the enormous amounts of data generated by\nsimulation of complex systems. We provide here a review of our current\nunderstanding of goals, benefits, and limitations of machine learning\ntechniques for computational studies on atomis…

  DOI

• Spatiotemporal control of liquid crystal structure and dynamics through activity patterning

  Nature Materials · 2021 · 133 citations

  DOI

• Transient intracellular acidification regulates the core transcriptional heat shock response

  eLife · 2020 · 94 citations

  , we report the discovery that Hsf1 can be robustly activated when protein synthesis is inhibited, so long as cells undergo cytosolic acidification. Heat shock has long been known to cause transient intracellular acidification which, for reasons which have remained unclear, is associated with increased stress resistance in eukaryotes. We demonstrate that acidification is required for heat shock response induction in translationally inhibited cells, and specifically affects Hsf1 activation. Physi…

  DOI

• Kinetic modeling reveals additional regulation at co-transcriptional level by post-transcriptional sRNA regulators

  Cell Reports · 2021 · 25 citations

  Small RNAs (sRNAs) are important gene regulators in bacteria. Many sRNAs act post-transcriptionally by affecting translation and degradation of the target mRNAs upon base-pairing interactions. Here we present a general approach combining imaging and mathematical modeling to determine kinetic parameters at different levels of sRNA-mediated gene regulation that contribute to overall regulation efficacy. Our data reveal that certain sRNAs previously characterized as post-transcriptional regulators…

  PublisherOA PDFDOI

• Limits on the computational expressivity of non-equilibrium biophysical processes

  Nature Communications · 2025-08-05 · 13 citations

  articleOpen access

  Many biological decision-making processes can be viewed as performing a classification task over a set of inputs, using various chemical and physical processes as "biological hardware." In this context, it is important to understand the inherent limitations on the computational expressivity of classification functions instantiated in biophysical media. Here, we model biochemical networks as Markov jump processes and train them to perform classification tasks, allowing us to investigate their com…

  PublisherOA PDFDOI

Recent grants

• Using systematic computational approaches to elucidate the molecular and network dynamics underlying a circadian clock

  NSF · $506k · 2020–2024

• Elucidating mechanisms of rare events underlying protein function with short molecular dynamics simulations and physics-informed machine learning

  NIH · $2.2M · 2020–2031

• NIH Grant R01GM109455

  NIH · $1.4M · 2020

Frequent coauthors

• Jonathan Weare

  105 shared

• Erik H. Thiede

  Cornell University

  51 shared

• Martin Karplus

  Harvard University

  47 shared

• Chatipat Lorpaiboon

  University of Chicago

  43 shared

• Norbert F. Scherer

  University of Chicago

  33 shared

• Robert J. Webber

  California Institute of Technology

  33 shared

• Aryeh Warmflash

  Rice University

  32 shared

• Alan L. Hutchison

  27 shared

Labs

• Dinner LabPI

Similar researchers at University of Chicago

• Jeffrey Rathmellh-128
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• Bana Jabrih-75