About

Kirill Korolev is an Associate Professor in the Department of Physics at Boston University, having joined the faculty in July 2013. He previously spent three years at MIT as a Pappalardo Postdoctoral Fellow in the Department of Physics, collaborating with labs led by Jeff Gore and Leonid Mirny. Korolev earned his Ph.D. in theoretical condensed matter physics from Harvard University and holds a B.S. with highest honors in applied physics and applied mathematics from the Moscow Institute of Physics and Technology. His research focuses on mathematical models for population dynamics, utilizing simple yet effective mathematical frameworks to study complex phenomena in biology and physics. His work addresses questions related to ecology and evolution of interacting species, including microbial communities such as the human microbiome, as well as the evolutionary dynamics of cancer progression and adaptation during geographic expansion. Korolev's research also explores ecosystem state switching, horizontal gene transfer, epigenetics, and genetic architecture, often drawing on statistical physics and stochastic processes, employing both analytical and computational methods. He has been recognized as a Simons Investigator in the Mathematical Modeling of Living Systems and is a Scialog Fellow.

Research topics

• Computer Science
• Biology
• Ecology
• Computational biology
• Engineering
• Microbiology
• Biochemical engineering
• Evolutionary biology
• Algorithm
• Biological system
• Astrobiology

Selected publications

• Genetically homogeneous sector morphologies emerge from anisotropic colony growth

  Physical review. E · 2026-02-05 · 1 citations

  articleOpen accessSenior author

  Competition during range expansions is of great interest from both practical and theoretical viewpoints. Experimentally, range expansions are often studied in homogeneous Petri dishes, which lack spatial anisotropy that might be present in realistic populations. Here, we analyze a model of anisotropic growth, based on coupled Kardar-Parisi-Zhang and Fisher-Kolmogorov-Petrovsky-Piskunov equations that describe surface growth and lateral competition. The anisotropy is encoded in how strongly genetic boundaries between strains are moved as a result of the expansion front morphology. We completely characterize spatial patterns and invasion velocities in this generalized model. In particular, we find that strong anisotropy results in a distinct morphology of spatial invasion with a kink in the displaced strain ahead of the boundary between the strains. This morphology of the outcompeted strain is similar to a shock wave and serves as a signature of anisotropic growth. We confirm these predictions with a commonly employed reaction-diffusion model of anisotropic growth.

  PublisherOA PDFDOI

• Transition from traveling fronts to diffusion-limited growth in expanding populations

  arXiv (Cornell University) · 2026-02-12

  preprintOpen accessSenior author

  Reaction-diffusion equations describe various spatially extended processes that unfold as traveling fronts moving at constant velocity. We introduce and solve analytically a model that, besides such fronts, supports solutions advancing as the square root of time. These sublinear fronts preserve an invariant shape, with an effective diffusion constant that diverges at the transition to linear spreading. The model applies to dense cellular aggregates of nonmotile cells consuming a diffusible nutrient. The sublinear spread results from biomass redistribution slowing due to nutrient depletion, a phenomenon supported experimentally but often neglected. Our results provide a potential explanation for the linear rather than quadratic increase of colony area with time, which has been observed for many microbes.

  PublisherDOI

• Transition from traveling fronts to diffusion-limited growth in expanding populations

  Physical review. E · 2026-02-11

  articleOpen accessSenior author

  Reaction-diffusion equations describe various spatially extended processes that unfold as traveling fronts moving at constant velocity. We introduce and solve analytically a model that, besides such fronts, supports solutions advancing as the square root of time. These sublinear fronts preserve an invariant shape, with an effective diffusion constant that diverges at the transition to linear spreading. The model applies to dense cellular aggregates of nonmotile cells consuming a diffusible nutrient. The sublinear spread results from biomass redistribution slowing due to nutrient depletion, a phenomenon supported experimentally but often neglected. Our results provide a potential explanation for the linear rather than quadratic increase of colony area with time, which has been observed for many microbes.

  PublisherOA PDFDOI

• Biophysical metabolic modeling of complex bacterial colony morphology

  Cell Systems · 2025-08-01 · 5 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• A mechanistic statistical approach to infer invasion characteristics of human‐dispersed species with complex life cycle

  Ecological Monographs · 2025-02-01 · 2 citations

  articleOpen access

  Abstract The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine spatiotemporal patterns of range expansion. This knowledge gap may stem from two reasons. First, current analytical models fail to integrate the invader's life‐history dynamics with heterogeneity in human‐mediated dispersal patterns. Second, classical statistical methods often fail to provide reliable estimates of model parameters, such as the time and place of species introduction and life‐history characteristics, due to spatial biases in the presence‐only records and lack of informative demographic data. To address these gaps, we first formulate an age‐structured metapopulation model that uses a probability matrix to emulate human‐mediated dispersal patterns. The model reveals that an invader spreads radially along the shortest network path, such that the inter‐patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a hierarchical Bayesian statistical method to estimate model parameters using presence‐only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel ( Dreissena polymorpha ) expansion in North America through the inland commercial shipping network. Our analysis suggests that zebra mussels might have been introduced before 1981, indicating a lag of 5 years between the time of introduction and first detection in late 1986. Furthermore, using our statistical model, we estimated a one in three chance that they were introduced near Kingsville (Ontario, Canada), where they were first reported. We also find that survival, fecundity, and dispersal during early life (1–2 years) play a critical role in determining the expansion success of these mollusks. These results underscore the importance of fusing prior scientific knowledge with observation and demographic processes in a Bayesian framework for conceptual and practical understanding of how invasive species spread by human agency.

  PublisherOA PDFDOI

• Drug-Resistance Mutations Find Strength in Small Numbers

  Physics · 2024-06-03

  articleOpen access1st authorCorresponding

  A new model, vetted by experiments on lung cancer cells, may help to explain how cancer and other diseases accumulate drug-resistance mutations that can compromise the effectiveness of treatments.

  PublisherOA PDFDOI

• Biophysical metabolic modeling of complex bacterial colony morphology

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-03-14 · 2 citations

  preprintOpen accessSenior authorCorresponding

  Summary Microbial colony growth is shaped by the physics of biomass propagation and nutrient diffusion, and by the metabolic reactions that organisms activate as a function of the surrounding environment. While microbial colonies have been explored using minimal models of growth and motility, full integration of biomass propagation and metabolism is still lacking. Here, building upon our framework for Computation of Microbial Ecosystems in Time and Space (COMETS), we combine dynamic flux balance modeling of metabolism with collective biomass propagation and demographic fluctuations to provide nuanced simulations of E. coli colonies. Simulations produced realistic colony morphology, consistent with our experiments. They characterize the transition between smooth and furcated colonies and the decay of genetic diversity. Furthermore, we demonstrate that under certain conditions, biomass can accumulate along “metabolic rings” that are reminiscent of coffee-stain rings, but have a completely different origin. Our approach is a key step towards predictive microbial ecosystems modeling.

  PublisherOA PDFDOI

• A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-02-12

  preprintOpen access

  Abstract The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine patterns of range expansion. This is partly because current analytical models fail to integrate the invader’s life-history dynamics with heterogeneity in human-mediated dispersal patterns. And partly because classical statistical methods often fail to provide reliable estimates of model parameters due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel ( Dreissena polymorpha ) expansion in North America through the commercial shipping network. Our analysis underscores the importance of correcting spatial biases and leveraging priors to answer questions, such as where and when the zebra mussels were introduced and what life-history characteristics make these mollusks successful invaders.

  PublisherOA PDFDOI

• Mechanically-driven growth and competition in a Voronoi model of tissues

  PubMed · 2024-05-13

  preprintOpen accessSenior author

  The mechanisms leading cells to acquire a fitness advantage and establish themselves in a population are paramount to understanding the development and growth of cancer. Although there are many works that study separately either the evolutionary dynamics or the mechanics of cancer, little has been done to couple evolutionary dynamics to mechanics. To address this question, we study a confluent model of tissue using a Self-Propelled Voronoi (SPV) model with stochastic growth rates that depend on the mechanical variables of the system. The SPV model is an out-of-equilibrium model of tissue derived from an energy functional that has a jamming/unjamming transition between solid-like and liquid-like states. By considering several scenarios of mutants invading a resident population in both phases, we determine the range of parameters that confer a fitness advantage and show that the preferred area and perimeter are the most relevant ones. We find that the liquid-like state is more resistant to invasion and show that the outcome of the competition can be determined from the simulation of a non-growing mixture. Moreover, a mean-field approximation can accurately predict the fate of a mutation affecting mechanical properties of a cell. Our results can be used to infer evolutionary dynamics from tissue images, understand cancer-suppressing effects of tissue mechanics, and even search for mechanics-based therapies.

  PublisherOA PDFDOI

• Competition on the edge of an expanding population

  arXiv (Cornell University) · 2023-01-18

  preprintOpen accessSenior author

  In growing populations, the fate of mutations depends on their competitive ability against the ancestor and their ability to colonize new territory. Here we present a theory that integrates both aspects of mutant fitness by coupling the classic description of one-dimensional competition (Fisher equation) to the minimal model of front shape (KPZ equation). We solved these equations and found three regimes, which are controlled solely by the expansion rates, solely by the competitive abilities, or by both. Collectively, our results provide a simple framework to study spatial competition.

  PublisherOA PDFDOI

Frequent coauthors

• José I. Jiménez

  24 shared

• David R. Nelson

  24 shared

• Irene A. Chen

  University of California, Los Angeles

  24 shared

• Oskar Hallatschek

  University of California, Berkeley

  23 shared

• Gabriel Bîrzu

  Stanford University

  20 shared

• Peter Freese

  Grail (United States)

  16 shared

• Ashish B. George

  University of Illinois Urbana-Champaign

  16 shared

• Jeff Gore

  Massachusetts Institute of Technology

  14 shared

Education

• Ph.D., Physics

  Boston University

  2008

• M.S., Physics

  Boston University

  2004

• B.S., Physics

  Moscow Institute of Physics and Technology

  2001

Awards & honors

• Simons Investigator in the Mathematical Modeling of Living S…
• Scialog Fellow