About

Herbert Levine is a University Distinguished Professor of Physics and Bioengineering at Northeastern University College of Engineering. His research focuses on physical modeling of cancer progression, metastasis, and interaction with the immune system, with recent interests including the role of metabolic plasticity in these processes and the co-evolution of tumors and the adaptive immune system. His work also encompasses the spatial organization of the actin cytoskeleton, the mechanics of collective cell motility, and the analysis of genetic circuits involved in cell fate decisions. Levine holds a PhD in Physics from Princeton University, earned in 1979, and a BS in Physics from MIT, obtained in 1976. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and a Fellow of the American Physical Society. His contributions to science include developing quantitative models of eukaryotic cell motility, studying regulation of cellular stemness during epithelial-mesenchymal transition, and investigating the cancer-immune interaction. Levine has been recognized as one of the top cited scientists worldwide and has led multiple research projects and international collaborations in the fields of mechanobiology, cancer, and living systems.

Research topics

• Biology
• Genetics
• Computational biology
• Computer Science
• Sociology
• Cell biology
• Condensed matter physics
• Evolutionary biology
• Physics
• Composite material
• Cancer research
• Mathematics
• Bioinformatics
• Combinatorics
• Materials science
• Data science

Selected publications

• Early Detection of Sudden Transitions in Notch signaling

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-03

  articleOpen accessCorresponding

  Abstract Identifying sudden transitions during phenotypic decision-making of complex biological systems can be crucial for our ability to control a cellular state. Yet, prior determination of these sudden transitions or tipping points remains challenging, as biological systems often exhibit only subtle early changes, which are often masked by inherent noise or rapid transition dynamics. Using Notch signaling as a model, we systematically analyze dynamical transitions in Notch-Delta (ND), Notch-Delta-Jagged (NDJ), and Fringe-mediated NDJ systems for both one and two-cell contexts. In the one-cell ND system, critical slowing down (CSD)-based early warning signals (EWSs) reliably capture transitions between sender ( S ) and receiver ( R ) states and remain robust to variation in forcing rate. We further find that flickering is a precursor to transitions in one-cell NDJ system. In contrast, flickering does not occur in the two-cell Notch model due to the presence of a supercritical bifurcation. Our analysis also offers insight into how NICD (Notch Intracellular Domain)-driven and Fringe-mediated asymmetries, along with the strength of external signals, control the emergence of flickering. Overall, this study identifies sudden transitions in Notch signaling under demographic noise and can be extended to other noisy biological systems, with potential applications in drug development and targeted therapeutic interventions.

  PublisherOA PDFDOI

• Abstract 4142: Metabolic transition analysis from dormancy to awakening in breast cancer

  Cancer Research · 2026-04-03

  article

  Abstract Dormancy and late relapse remain pressing challenges in ER+ breast cancer. Mechanistically, dormancy can reflect a cellular quiescence of micrometastatic cells or tumor-mass dormancy constrained by angiogenic or immune bottlenecks. A long-standing hypothesis is that dormant disseminated tumor cells (DTCs) reside in a glycolysis-high, mesenchymal-like state, whereas successful awakening requires a transition toward an epithelial, OXPHOS-dependent phenotype. Here, we test this hypothesis using a catabolic-anabolic AMPK/HIF-1/MYC regulatory model with four phenotypic states (OXPHOS, Warburg, hybrid W/O, and glutamine-reliant Q), each with distinct metabolic signatures. We integrated these signatures with longitudinal experimental and relevant clinical intervention datasets, including viral infection-induced awakening model, clinical endocrine-therapy time courses, and dormancy models. In addition, three complementary epithelial-mesenchymal transition (EMT) metrics were integrated to assign a consensus epithelial, hybrid, or mesenchymal phenotype to each sample to connect the intrinsic relationship of EMT with metabolic phenotype and dormancy status. Across models, we uncover marked metabolic diversity during cancer awakening, including transitions from quiescent, glycolysis-dependent or mesenchymal-biased states toward hybrid W/O metabolic configurations characterized by coordinated changes of AMPK and HIF-1, altered ROS source balance, and re-engagement of epithelial and proliferative signaling pathways. These metabolic and transcriptional transitions reveal the molecular features associated with dormancy exit and may provide new opportunities to therapeutically prevent awakening and late metastatic relapse. Citation Format: Javier Villela Castrejon, Herbert Levine, Jason T. George. Metabolic transition analysis from dormancy to awakening in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4142.

  PublisherDOI

• A biophysical framework for accurately identifying antigen single-amino acid escape variants and corresponding variant-specific compensatory TCR sequences

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-05

  articleOpen access

  ABSTRACT The impact of single amino acid substitution on T-cell receptor (TCR) recognition is central to understanding the molecular determinants of TCR specificity and degeneracy during viral mutational escape, cancer recognition, and autoimmunity. In this study, we developed a biophysics-informed computational approach integrating experimental alanine-scan mutagenesis data from the autoimmune-associated ALWGPDPAAA peptide bound to HLA-A*02:01 together with coarse-grained structural modeling. Our approach reconstructs the energetics and structural determinants underpinning the observed loss of recognition by the diabetogenic 1E6 TCR upon single-point mutations, specifically at the critical Pro 5 and Asp 6 residues. Leveraging the computational model’s ability to incorporate multiple structural templates into binding predictions, this approach quantitatively reproduces experimentally measured affinity disruptions. Additionally, we apply our approach to identify potential compensatory interactions capable of restoring binding affinity through alternative residue interactions. This integrative computational framework contributes a strategy for inferring TCR-peptide binding energetics at the single amino acid level, guiding the rational design of peptide-based immunotherapeutics, and predicting the functional impacts of clinically relevant peptide variants.

  PublisherOA PDFDOI

• Ecological Dynamics of Pro-tumor and Anti-tumor Teams in the Tumor Microenvironment

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-01

  articleOpen accessSenior authorCorresponding

  Abstract Tumor growth occurs within a complex tumor microenvironment (TME) composed of many interacting cell types. The immune cell types in TME tend to organize into two functional communities: a pro-tumor team and an anti-tumor team, each internally cooperative but mutually antagonistic forming a two-team ecosystem. Quantitatively predicting the ecological outcomes of such interactions remains challenging due to cellular diversity and interaction variability, and the exact dynamical regimes accessible to such a two-team ecosystem remain unknown. Here, we model tumor-immune interactions as a structured ecosystem with two competing teams using a generalized Lotka–Volterra framework and analyze it using the cavity method. We derive phase diagrams that delineate when these two communities coexist, when one dominates, and how these outcomes depend on intra-team cooperation, cross-team inhibition, and ecological heterogeneity. Our work provides a foundation for understanding tumor-immune dynamics from a community ecology perspective.

  PublisherOA PDFDOI

• BPS2026 – Modeling coevolutionary dynamics of CRISPR immunity and viral diversity

  Biophysical Journal · 2026-02-01

  articleSenior author
  PublisherDOI

• Phase-field approach to cellular blebbing

  Physical review. E · 2026-02-12

  article
  PublisherDOI

• A computational approach for perturbation-induced EMT transitions

  npj Systems Biology and Applications · 2025-11-13 · 4 citations

  articleOpen accessSenior authorCorresponding

  The Epithelial-mesenchymal transition (EMT) is a cellular state transition fundamental to development, wound healing, and cancer metastasis. The gene regulatory mechanisms underlying EMT have been extensively documented, revealing gene regulatory networks (GRNs) involving groups of mutually inhibiting transcription factors and microRNAs. Despite significant progress from both experimental and computational approaches, the details of how the EMT GRN initiates EMT in response to various external inputs is still not well understood. Here, we apply both Boolean and ordinary differential equation (ODE)-based methods to simulate a well-studied 26-node, 100-edge EMT GRN, examining its response to a wide range of single- and double-node perturbations. We evaluate the characteristics of effective EMT-inducing signals, particularly examining the amplifying role of transcriptional noise in determining the likelihood and mean transit time of an EMT. Together, these models establish a complementary framework for understanding and predicting drivers of EMT in the context of a GRN. We anticipate that this framework for a systematic study of in-silico GRN perturbations will be useful in developing increasingly accurate dynamical GRN models for various biological processes.

  PublisherOA PDFDOI

• A multilevel formalism to model the hybrid E/M phenotypes in epithelial-mesenchymal plasticity

  Biophysical Journal · 2025-11-01 · 1 citations

  articleSenior author
  PublisherDOI

• Mathematical Characterization of Drug-Induced Persistence in Cancer

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Phase-field modeling of <i>Dictyostelium discoideum</i> chemotaxis

  Physical review. E · 2025-08-26

  article

  A phase-field approach is proposed to model the chemotaxis of Dictyostelium discoideum. In this framework, motion is controlled by active forces as determined by the Meinhardt model of chemical dynamics which is used to simulate directional sensing during chemotaxis. Then the movement of the cell is achieved by the phase-field dynamics, while the reaction-diffusion equations of the Meinhardt model are solved on an evolving cell boundary. This task requires the extension of the usual phase-field formulation to allow for components that are restricted to the membrane. The coupled system is numerically solved by an efficient spectral method under periodic boundary conditions. Numerical experiments show that our model system can successfully mimic the typically observed pseudopodia patterns during chemotaxis.

  PublisherDOI

Recent grants

• Toward an integrative understanding of mammalian cell motility

  NSF · $712k · 2011–2013

• Spatial Patterning in the Progressing Tumor - The Role of Notch

  NSF · $753k · 2019–2023

• Physical and Mathematical Principles of Brain Structure and Function - Spring 2013 in Arlington, VA

  NSF · $183k · 2013–2014

• Spatial Patterning in the Progressing Tumor - The Role of Notch

  NSF · $634k · 2016–2019

• Collaborative Research: PoLS Student Research Network

  NSF · $692k · 2011–2017

Frequent coauthors

• Mohit Kumar Jolly

  Indian Institute of Science Bangalore

  541 shared

• Jason T. George

  Texas A&M University

  410 shared

• José N. Onuchic

  Rice University

  313 shared

• Dongya Jia

  263 shared

• Eshel Ben‐Jacob

  178 shared

• David A. Kessler

  University of Bristol

  160 shared

• Wouter‐Jan Rappel

  University of California, San Diego

  158 shared

• Shubham Tripathi

  Yale University

  150 shared

Awards & honors

• Member, National Academy of Sciences
• Member, American Academy of Arts and Sciences
• Alfred P. Sloan Foundation Research Fellowship, September 19…
• Fellow, American Physical Society