THE UNIVERSITY OF CHICAGO
Our Work
The Raman Lab: Mission and Vision
The Raman Lab is a systems biology lab that seeks to uncover the hidden architecture of complex systems. We study how emergent organization arises in biology—from genomes to tissues to ecosystems—and how it can be reverse-engineered, predicted, and ultimately designed. We believe that complexity is not an obstacle to understanding but a signal of underlying constraint. By treating biological diversity as data and placing statistical structure before mechanism, we aim to build a science of emergence: one that unifies discovery, design, and explanation across both natural and artificial systems.
Our research spans three deeply connected areas:
A Physics of Natural Systems
We are developing a new computational systems biology and statistical physics for complex, naturally emergent systems—one that captures how constraint and variation jointly give rise to structure. This effort draws on tools from spectral theory, information geometry, and latent space modeling to uncover axes of meaningful variation that govern system behavior. Whether analyzing the organization of genomes, the spatial structure of tumors, or the landscape of microbiome diversity, our goal is to distill complexity into the governing principles that shape it. In this view, evolution is not noise but compression: a search through a high-dimensional design space that leaves behind a readable statistical trace.
Synthetic Biology and the Design of Emergent Systems
We apply these principles to build synthetic systems de novo, using learned constraints to steer complex function. Our platform for designing microbial consortia—exemplified by our first attempt called SynCom15, a 15-member community that suppresses multidrug-resistant Klebsiella pneumoniae—demonstrates that robust function can be engineered from statistical inference alone. We’ve extended this framework to tissues, designing metrics like spatial lability and spatial groups (SGs) that capture biologically and clinically meaningful architecture from gene expression data.
The Why of Emergence
Beyond modeling and design, we seek to understand why emergent systems take the forms they do. Our work explores how environmental dynamics—acting over long timescales—sculpt the statistical structure of evolved systems and make them not only functional, but also evolvable. Just as riverbeds record the dynamics of water over time, we believe biological architectures reflect the constraints of their ecological and evolutionary past. We aim to decipher these constraints, revealing how selection pressures, ecological dynamics, and adaptive landscapes shape not just outcomes, but the evolvability of living systems. This work connects evolutionary theory, statistical physics, and systems biology to explain emergence not just as a phenomenon, but as a necessity.
Together, these three areas define a scientific worldview: emergence is not a byproduct of complexity—it is the key to its structure. By learning from how nature builds, we aim to transform how we understand living systems, how we design synthetic ones, and how we model intelligence itself.