About

Rama Ranganathan, M.D. Ph.D., is the Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker School of Molecular Engineering, and the College at the University of Chicago. His research has focused on understanding the basic principles of structure, function, and evolution in biological systems, particularly emphasizing the atomic and cellular scale. His work has led to new models for the architecture of natural proteins and new experimental tools for studying the physics and evolution of proteins and cellular systems. He leads the University of Chicago Center for Physics of Evolving Systems and is the director of BioCARS beamline, a national user facility for structural biology at the Advanced Photon Source at Argonne National Laboratory. Dr. Ranganathan received his undergraduate degree in Bioengineering from UC Berkeley and his M.D. and Ph.D. degrees from UC San Diego, working jointly with Charles Zuker, Chuck Stevens, and Roger Tsien. He also conducted brief postdoctoral studies at Harvard Medical School with Rod MacKinnon and at the Salk Institute with Joe Noel.

Research topics

• Computer Science
• Biology
• Artificial Intelligence
• Biochemistry
• Environmental health
• Computational biology
• Medicine
• Ecology
• Demography
• Business
• Internal medicine
• Chemistry
• Nanotechnology
• Evolutionary biology
• Data science
• Bioinformatics

Selected publications

• Simple biological controllers drive the evolution of soft modes

  Proceedings of the National Academy of Sciences · 2026-04-21

  preprintOpen access

  Biological systems, with many interacting components, face high-dimensional environmental fluctuations, ranging from diverse nutrient deprivations to toxins, drugs, and physical stresses. Yet, many biological control mechanisms are "simple," i.e., restoring homeostasis through low-dimensional representations of the system's high-dimensional state. How do low-dimensional controllers maintain homeostasis in high-dimensional systems? We develop an analytically tractable model of integral feedback for complex systems in fluctuating environments. We find that selection for homeostasis leads to the emergence of a soft mode that provides the dimensionality reduction required for the functioning of simple controllers. Our theory predicts that simple controllers that buffer environmental perturbations (e.g., stress response pathways) will also buffer mutational perturbation, an equivalence we test using experimental data across 5,000 strains in the yeast knockout collection. We also predict, counterintuitively, that knocking out a simple controller will decrease the dimensionality of the response to environmental change; we outline transcriptomics tests to validate this. Our work suggests an evolutionary origin of soft modes, with implications ranging from cryptic genetic variation to global epistasis.

  PublisherOA PDFDOI

• Breast cancer survival by stage at diagnosis in countries in transition: A population‐based study

  International Journal of Cancer · 2026-01-27

  article

  This study explores variation in stage-specific survival in women diagnosed with breast cancer in transitioning countries. We obtained data of women diagnosed between 2008 and 2012 from 11 population-based cancer registries (PBCRs) in 10 countries, with follow-up until December 2014. Following stage data standardization and multiple imputation for missing data, we estimated age-standardized 1-, 3-, and 5-year net survival (ASNS) by stage and age group. Stage distribution varied significantly across jurisdictions. Puerto Rico had over 4 in 5 patients diagnosed with early stage (stage I/II), while India, Trivandrum, and Thailand, Khon Kaen had <3 in 5 diagnosed with early stage. Stage-specific ASNS was similar across early stages (stage I/II) but varied markedly for stage IV, where the highest 3-year ASNS (Puerto Rico: 43.2% 95% CI: 38.9%-47.6%) was 20 percentage points higher than the lowest 3-year ASNS (India, Trivandrum: 22.8% 95% CI: 10.2%-35.4%). ASNS for patients 60 years and older was generally lower than for patients younger than 60 years across all jurisdictions. Differences in ASNS were subtle in early stages but were especially pronounced for stage IV, where in Puerto Rico, 3-year ASNS for patients younger than 60 (49.0% 95% CI: 42.7%-55.2%) was over 13 percentage points higher than 3-year ASNS for patients 60 or older (35.4% 95% CI: 29.2%-41.7%). Disparities in breast cancer survival were partly explained by differences in early diagnosis and in care at late stages. Stage at diagnosis is an important indicator that should be collected by registries worldwide, particularly for late-stage disease and older patients.

  PublisherDOI

• The Phenotypic Landscape of a Circadian Clock

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-27

  articleOpen access

  Circadian clocks produce near-24-hour oscillations through biochemical feedback loops. To study their architecture, we developed a deep sequencing assay that measures the phenotypes of thousands of mutant clocks in parallel. We reveal a landscape where oscillator properties are factorized: mutations change period without decreasing amplitude and while maintaining a balanced waveform. Mutations that either shorten or lengthen period localize to specific protein-protein interaction surfaces, while a particularly sensitive region near the KaiC interdomain linker can cause extreme effects. After entrainment, high amplitude mutant oscillators form a tunable low-dimensional manifold in the period-phase plane, suggesting that most period mutations leave the coupling to the environment unchanged. In contrast, mutations that reduce amplitude are concentrated in a specific long period phenotype. This correlation structure may support the evolvability of this dynamical molecular system and is a powerful constraint on underlying mechanism.

  PublisherDOI

• BPS2026 – A model for the sequence space of a protein family

  Biophysical Journal · 2026-02-01

  articleSenior author
  PublisherDOI

• BCAR: A fast and general barcode-sequence mapper for correcting sequencing errors

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-31

  articleOpen accessSenior author

  Motivation: DNA barcodes are commonly used as a tool to distinguish genuine mutations from sequencing errors in sequencing-based assays. In the presence of indel errors, utilizing barcodes requires accurate alignment of the raw reads to distinguish genuine indels from indel errors. Existing strategies to do this generally rely on aligners built for homology comparison and do not fully utilize quality scores. We reasoned that developing an aligner purpose-built for error correction could yield higher quality barcode-sequence maps. Results: Here, we present BCAR, a fast barcode-sequence mapper for correcting sequencing errors. BCAR considers all of the evidence for each base call at each position both during alignment and during final consensus generation. BCAR creates high-accuracy barcode-sequence maps from simulated reads across a broad range of error rates and read lengths, outperforming existing methods. We apply BCAR to two experimental datasets, where it generates high-quality barcode-sequence maps. Availability and implementation: BCAR source code, documentation and test data are available from: https://github.com/dry-brews/BCAR.

  PublisherOA PDFDOI

• BPS2026 – A comprehensive deep coupling scan in a PDZ domain

  Biophysical Journal · 2026-02-01

  articleSenior author
  PublisherDOI

• BPS2026 – Modularity within the DNA clamp loader complex

  Biophysical Journal · 2026-02-01

  articleSenior author
  PublisherDOI

• Improved inference of multiscale sequence statistics in generative protein models

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-09

  articleOpen access

  Abstract High dimensionality and multiscale statistical structure are pervasive features of biological data, posing fundamental challenges for modeling. Because model inference generally proceeds with far fewer data than parameters, statistical patterns across scales are often unevenly represented. Protein sequences provide a paradigmatic example: statistics across homologs are inherently multiscale, displaying collective correlations among conserved residue sectors that encode function, alongside localized correlations corresponding to physical contacts outside these sectors. Standard regularization strategies used to mitigate undersampling during model inference have been shown to capture these patterns unevenly, a bias that compromises generative models of protein sequences by limiting their ability to produce both functional and diverse proteins. This limitation is exemplified by Boltzmann machine–based generative models, which so far have required post hoc corrections to recover functionality, at the cost of reduced sequence diversity. Here, we introduce the stochastic Boltzmann Machine (sBM), a new regularization strategy that more accurately captures different correlation scales. Through analyses of theoretical models with known ground-truth parameters and experiments on the chorismate mutase family, we show that sBM effectively mitigates distortions in the estimation of model parameters, enabling the generation of functional sequences with greater diversity and without the need for post hoc corrections. These results advance the inference of generative models that more faithfully reflect the evolutionary constraints shaping protein sequences.

  PublisherDOI

• BPS2026 – Comprehensive epistasis mapping across a protein family with diverging functions

  Biophysical Journal · 2026-02-01

  articleSenior author
  PublisherDOI

• Macromolecular Crystallography at the Upgraded Advanced Photon Source

  Synchrotron Radiation News · 2026-03-04

  articleOpen access
  PublisherDOI

Recent grants

• NIH Grant R01EY018720

  NIH · $1.8M · 2013

• Seeing Protein Mechanics: The Link Between Molecular Structure, Function, and Evolution

  NIH · $2.0M · 2016–2022

Frequent coauthors

• Michael Socolich

  University of Chicago

  44 shared

• Olivier Rivoire

  Centre Interdisciplinaire de Recherche en Biologie

  42 shared

• William P. Russ

  The University of Texas Southwestern Medical Center

  41 shared

• Kar-Wai Ng

  Howard Hughes Medical Institute

  25 shared

• Harold R. Garner

  AtlantiCare

  25 shared

• John W. Fondon

  The University of Texas at Arlington

  25 shared

• Yevgeniy Gelfand

  Multidisciplinary Association for Psychedelic Studies

  25 shared

• Gary Benson

  Belfast City Hospital

  25 shared