Research Overview

Self-Organization in Living Systems

I am broadly interested in studying self-organization in living systems using a transdisciplinary approach, where I draw from multiple traditions such as physics, organismal biology, ecology, computation, and mathematics. My research uses insights and tools from dynamical systems and statistical mechanics to explore key conceptual questions regarding collective behavior in social insects and community dynamics in microbial ecosystems. Specifically, I analyze collective motion in social caterpillars, observed in natural field sites and tabletop experiments, and compare these observations to analytical predictions and computer simulations of agent-based and hydrodynamic models. I also study microbial population dynamics, analyze experimental data from bacterial communities, and compare them to predictions of theoretical frameworks drawn from statistical mechanics.

How do living systems, from cells to ecosystems, thrive despite being composed of numerous interacting components, often without central control?

While molecular biology has made significant advances in elucidating the mechanisms of microscopic constituents, they do not readily explain the macroscopic properties of the entire system and how they emerge from the underlying interactions. Macroscopic phenomenology is often invariant to most microscopic details but critically depend on some, analogous to how thermodynamics of gases is invariant to their molecular composition but depends strongly on the temperature. For physical systems, identifying these relevant properties at both microscopic and macroscopic scales has allowed us to make accurate predictions. Living systems offer a new frontier to explore tools and concepts developed in physics in a non-equilibrium context. My research investigates emergent phenomenology at two complementary scales: collective behaviors in social insects and community dynamics in bacterial ecosystems. My work contributes to understanding universal principles that govern complex systems across disciplines, offering a rich platform for inquiry at the intersection of physics, biology, and ecology.

Collective Behavior in Social Insects

Collective behavior in social insects requires both mechanisms of coordination—such as communication and compromise—and a strong fitness advantage to overcome the cost of such coordination. Moreover, these behaviors must prevent cheaters—individuals that benefit without contributing—from destabilizing the group. Despite these challenges, collective behavior has evolved numerous times in insects, resulting in strikingly similar group properties across species with drastically different individual behaviors. Eusociality in bees and termites, for example, emerges from entirely different individual-level behaviors but results in similar macroscopic group properties. A comparative description of collective behaviors in their ecological context can thus offer insights into universal principles of self-organization and how they can emerge from diverse individual behaviors.

The field of active matter has ushered in a new era of non-equilibrium physics in the last three decades and is often used to describe collective motion. However, comparison of models to natural systems is challenging, as the interactions between components are often complex and/or highly-structured. For example, though bird flocks inspired the first models of active matter, birds respond in a complex manner to a variety of cues. Similarly, eusocial insects such as ants and bees feature sophisticated social structures and communication mechanisms. Thus, simple variations in individual behaviors impact group behaviors in uninterpretable ways. A novel understudied system that resolves these issues are social caterpillars that feature striking collective behaviors but simple individual behaviors and no social structure.

Diversity and Stability in Microbial Ecosystems

While in collective behavior coordination among individuals leads to a synergistic benefit, in ecosystems we observe the opposite effect: a diversity of competitive interactions can paradoxically lead to greater stability. Competition for limited resources might suggest that species must specialize and minimize interactions, but the most diverse ecosystems, like tropical forests and microbial communities, actually feature highly interactive networks. Further, the most stable communities often include species that perform similar functions, which should, in theory, exacerbate competition. How does diversity and stability emerge from dense antagonistic interactions? Microbial communities, through the use of techniques from statistical physics, provide the perfect setting to ask these questions.

As individual traits of species are extremely difficult to characterize, techniques from statistical physics, such as those used in the study of spin glasses, are now being used to study "typical" microbial communities as model systems. The hope is that robust statistical signals from measurements in natural communities can be compared to the statistics of model systems to identify key features that enable large complex communities to be stable.