News: People

4/01/2025

Congratulations Arvind Murugan!

Arvind was awarded the 2025 Tel Aviv University International Prize in Biophysics together with Ariel Amir (Weizmann) and Julien Tailleur (MIT). 

4/01/2025

Congratulations Ondrej Maxian!

Ondrej Maxian (Munro/Dinner)  recently accepted a faculty position as an assistant professor in the Department of Applied and Computational Math and Statistics (ACMS) at University of Notre Dame. He will be starting in January 2026. 

4/01/2025

Congratulations Seongsoo Kim!

Seongsoo Kim (Gardel lab) was awarded  a BSD Chicago Fellowship and a Schmidt AI in Science Postdoctoral Fellowship.

4/01/2025

CLS Trainee Highlight – Jesse Lin

Jesse Lin is a physics PhD student in the Vitelli group at UChicago. He studies the quantitative principles guiding biological evolution from microbial ecology to immunological systems, combining techniques from statistical physics and optimization theory/machine learning. His work in collaboration with the Kuehn group explores whether the growth behavior of E. coli can be explained by a dynamic optimization principle, similar to how artificial agents learn to optimally play games such as chess. Unlike game-playing agents, Darwinian evolution does not directly alter the phenotypes of organisms but instead changes their genotypes which determine organismal behavior downstream. They have shown that in silico models based on dynamic optimization of neural networks exhibit identical behavior to known biochemical signaling networks responsible for metabolic regulation. Moreover, this dynamic optimization in time-varying environments induces a generic preference for complex genotype-phenotype maps. In this project and others, Jesse is interested in how robust, “guiding principles” of evolution may emerge naturally in systems that optimize over time.

4/01/2025

CLS Trainee Highlight – Rio Ondo

Rio Ondo is a graduate student in the Murugan and Szostak group at UChicago. She studies the evolution of error correction in RNA replication, combining biochemical selection techniques with molecular evolution approaches to investigate how early replicators may have improved fidelity. Her work focuses on developing and selecting ribozymes capable of proofreading by cleaving mismatched bases from RNA strands, inspired by kinetic proofreading. Through this, she aims to uncover fundamental principles of error correction in non-enzymatic and ribozyme-catalyzed replication. By exploring how early RNA-based systems could have enhanced fidelity, her research seeks to understand how primitive replicators may have mitigated error catastrophe, a major challenge for early life, and what this suggests about the emergence of error correction in molecular evolution.

3/01/2025

Trainee Highlight – Erin Brandt

Erin Brandt is a postdoc in the Nirody group in the department of Organismal Biology and Anatomy. Currently she is studying the unusual semi-hydraulic locomotion system in spiders. To do this, she combines detailed studies of morphology, kinematics, and force production in jumping spider jumping to better understand this unusual locomotion system. She is looking for other approaches, both old and new, to address these questions. In her project, she combines studies of (1) jumping kinematics, (2) ground reaction forces generated by the legs during jump takeoff, and (3) measurements of anatomical structures within spiders to shed light on how an unusual system of locomotion leads to dextrous motion in the jumping spider Phidippus audax.

3/01/2025

Trainee Highlight – Amanda Johnson

Amanda Johnson is a graduate student in the Gardel group within the Development, Regeneration, & Stem Cell Biology program. She investigates how coordinated cell behaviors drive tissue patterning using human stem cell organoid models. She is particularly interested in the principles of self-organization and multicellular dynamics and is eager to collaborate with theorists and computational modelers to uncover fundamental rules governing developmental systems. Using an in vitro gastruloid model, she demonstrates how cell movement and signaling feedback loops coordinate mesoderm migration and self-organization. By dissecting the mechanics of migration and its role in shaping tissue architecture, her project reframes development as a fluid, responsive process, highlighting how spatiotemporal dynamics drive fate decisions beyond predefined genetic programs.

1/01/2025

Trainee Highlight – Aleksander Radakovic

Aleksandar Radakovic is a postdoc in Joe Thornton and Arvind Murugan’s groups. He obtained his Ph.D. working with Jack Szostak at Harvard University where he studied prebiotic RNA aminoacylation mechanisms. At Chicago, he is combining phylogenetics, biophysics, and biochemistry techniques to study the evolution of the universal genetic code through the specification of aminoacyl-tRNA synthetase enzymes, an essential part of the machinery that underlies ribosomal translation in all life. Synthetases prepare amino acids for protein synthesis by coupling each amino acid to its corresponding tRNA, but how this functionality evolved is not known. Aleksander’s work aims to understand how a synthetase enzyme acquires the specificities which enable it to couple new pairs of amino acids and tRNAs.  He is integrating a new RNA nanopore sequencing method with ancestral sequence reconstruction to resolve how, in early eukaryotes, the duplication of an ancestral glutamyl-tRNA enzyme led to the partitioning of enzyme specificities, resulting in highly specific glutamyl-tRNA and glutaminyl-tRNA synthetases. He is looking for collaborators knowledgeable in computer science, molecular simulations, and theoretical biology.

12/01/2024

Trainee Highlight – Samar Alqatari

Samar is a grad student in the Nagel group. She received her bachelor’s from Stanford, then completed a master’s in computational science at MIT, where she performed experiments and simulations on the Rayleigh-Taylor instability under confinement. At UChicago, she has expanded her work on fluid instabilities into the Saffman-Taylor viscous fingering that is observed under applied rotational shear. More recently, she has been studying function and adaptation using the framework of evolvable mechanical networks. Samar’s current work is centered on the evolution of strain in elastic mechanical networks that are tuned via selective pruning to exhibit an “allosteric” function: applying a strain to a pair of “source” nodes induces a strain at a pair of “target” nodes, analogous to allostery in proteins. The networks are tuned for either an in-phase or an out-of-phase strain response between the source and target. The network can be evolved from one function to a another with a single-step mutation by adding or removing a link. The statistics of this evolving strain behavior show similarities to biological evolution, such as epistatic effects, i.e. the result of each mutation is dependent on the nature of previous mutations. This analysis has led to the identification of several systematic features of evolution. To make the most of this system, she hopes to collaborate with biologists, biophysicists, and anyone interested in evolution and the emergence of function in matter.

12/01/2024

Trainee Highlight – Nico Romeo

Nico is a CLS fellow working with Vincenzo Vitelli, Elizabeth Jerison, Aaron Dinner and Noah Mitchell. He obtained his PhD at MIT, where he researched the mechanics of biological development and methods for its representation . His work at Chicago focuses on developing methods to study the complex dynamics of innate immune responses and biological development, drawing on tools from statistical physics, geometry, and machine learning. In his current project, he and his collaborators have developed a contrastive learning technique to identify dynamical behaviors from trajectory data and characterize systems beyond topological features, using transition path theory. These techniques allow them to draw a ‘phase diagram’ of dynamical systems and reveal structure in excitable systems. As he continues this work, Nico would love to learn about simple systems that exhibit complex spatial dynamics.

12/01/2024

Congratulations Jasmine Nirody!

Please join us in congratulating Jasmine Nirody on being elected as a Member-at-Large in the Division of Biological Physics in the American Physical Society! She will take office at this upcoming APS Global Physics Summit in March 2025. 

11/01/2024

Welcome CLS Postdoctoral Fellow Sang Hyun Choi

Sang Hyun is currently working on exploring phase transitions in a XY model where two species of spins interact non-reciprocally, exploring how dynamics are affected by non-reciprocity, space, and noise.

10/01/2024

Welcome Noah Mitchell!

Please welcome Noah Mitchell as a CLS Investigator! Previously an affiliate, he will be working with the MA2 team on encoding information in morphogenetic systems. Noah’s lab is a part of the Department of Molecular Genetics and their current research focus is collective cell behaviors and mechanical interactions between mesodermal and epithelial tissue layers that link genes to geometry.

10/01/2024

Congratulations Stephanie Palmer!

Please join us in congratulating Stephanie Palmer as one of the new Schmidt Science Polymaths! This program offers support to researchers looking to explore different focuses with their work. Stephanie has several projects she is interested in pursuing including, examine the evolution of neural computation in color vision for butterflies and the circadian rhythms of bacteria, specifically how their environment affects computation and migration. Read the full article here.

10/01/2024

Trainee Highlight – Cal Floyd 

Cal is a postdoc in the Vaikuntanathan and Dinner groups who obtained his Ph.D. working with Garyk Papoian and Chris Jarzynski at the University of Maryland, where he researched the non-equilibrium thermodynamics of the cytoskeleton. At Chicago, he is studying cellular computation and control, using simulation and theory to analyze the computational functions of biochemical processes. He recently collaborated with Suri Vaikuntanathan, Arvind Murugan, and Aaron Dinner to investigate non-equilibrium biophysical systems that perform a classification function, with the goal of examining the physical limitations on the systems’ computational expressivity. The target systems were modeled as discrete-state Markov processes, and the systems’ steady state distributions effectively partitioned their input space into distinct computational outputs. Through analytical derivations and supporting numerical work, he showed that these effective input-output functions have specific inherent limitations that prevent them from classifying arbitrarily complicated input distributions.  However, he also demonstrated that these limitations can be systematically overcome.  More complex computations can be achieved by feeding inputs into the system through multiple paths.  Further analysis suggests that the mathematical structure underlying these biophysical computation mechanisms resembles the transformer architectures used in machine learning, such as those found in systems like ChatGPT.  As these findings are fairly general, they have the potential to provide insight into a range of biological processes, such as how cells encode their state through membrane glycoproteins, pathogen recognition in immune responses, and quorum sensing by bacterial populations.

10/01/2024

Trainee Highlight – Kyle Crocker

Kyle is a postdoc in the Kuehn lab. His doctoral research at Ohio State University centered on building thermodynamic models of DNA mismatch repair and DNA origami. At the University of Chicago, he is studying microbial ecology by combining computational modeling, theory, and experimentation to uncover and understand collective metabolic behavior in the soil microbiome. Since microbial communities typically harbor tens to hundreds of distinct species, microbiome dynamics are potentially highly complex, yet understanding how these populations respond to change is critical for predicting the impact of perturbations, interventions, and climate change. Even though the taxonomic diversity of the microbiome is high, its taxa can often be sorted into functional guilds made of strains with similar metabolic traits. These guilds reduce the complexity of the system and promise to simplify our understanding of how communities respond to environmental change. Microbial communities experience fluctuation on a range of timescales, from rapid changes in moisture, temperature, or light levels to long-term seasonal or climactic variation. In the case of nutrient fluctuations, the Kuehn lab have found that the nature of the response depends on the timescale of the change. Rapid changes in nutrient levels bring about correlated fluctuations in the population of each guild (dynamic cohesion), yielding an overall community response that has its dimensionality set by the number of guilds. In contrast, at slower timescales of nutrient variation the species within a guild begin to compete due to similar nutrient preferences. This competition leads to a community response that is not dominated by guild structure. These results provide a route to understanding the relationship between functional guilds and the community response, as well as an experimental approach to identifying functional guilds via designed nutrient perturbations to complex communities. To further this work, Kyle is looking for collaborators knowledgeable in representation learning, soil biogeochemistry, and the Birch effect. 

10/01/2024

Trainee Highlight – Emily Hinds 

Emily is a graduate student in the Ranganathan group who graduated with a B.S. in biochemistry and computer science from the University of Wisconsin, Madison, where she worked on the laboratory evolution of proteins for structure prediction. At UChicago, she continues to study protein evolution and design by building statistical models mapping protein sequence to function, using these models to design new proteins, and testing the activity of the designs with high-throughput experiment. She is interested in other (real or toy) systems that explore questions of dimensionality and connectivity in epistatic networks. Proteins which evolved from a common ancestor to perform the same cellular function can differ in their linear sequence. by more than 75%. A group of related proteins evolves upon a network limited by mutation and natural selection; all sequences in the network must retain molecular function, and they are each connected to the rest of the network via paths of single mutations. The effect of these constraints on the geometry of sequence spaces—their size, dimensionality, and shape, and the extent of connectivity—is not well characterized for real proteins. To better understand these networks, we predicted new proteins within a specified family by building statistical (Potts) models that collectively reproduce first-order sequence statistics and a targeted subset of second-order statistics for that protein family. These models were used to design functional proteins through an evolution-like sampling process. The designs were then verified through high-throughput growth experiments in E. coli. For the chorismate mutase (AroQ) enzyme family, models built using only 0.4% of the pairwise correlations suffice to design functional synthetic proteins, some up to 51% different than any natural protein. Currently, Emily is investigating the properties of this sparse model in silico. This work will broaden the novelty and diversity of proteins designed by evolution-based statistical methods and provide strategies to analyze natural (evolved) protein sequence spaces.

08/26/2024

Welcome CLS Postdoctoral Fellow Mason Rouches

Mason received his PhD in Molecular Biophysics and Biochemistry from Yale University and comes with experience working on phase separation phenomena in biology, especially the phenomenon of pre-wetting. Here at the Center for Living Systems, he will explore information processing and computation through phase separation in multi-component systems with an eye towards condensates that control gene regulation in the nucleus.