About

Brian Camley leads the Camley Group at Johns Hopkins University, which is part of the Physics & Astronomy and Biophysics Departments. His research employs theoretical and computational approaches to investigate various aspects of cell biology, focusing on cell motility, collective motion, chemotaxis, cell sensing behaviors, and the mechanics of soft matter within cellular contexts. The group studies how cells cooperate to sense chemical gradients, particularly examining the fundamental limits of collective chemotaxis. One key area of research involves understanding how clusters of cells, such as those involved in cancer metastasis, communicate and rearrange themselves to reduce bias caused by cells that send disproportionately large signals. This work highlights the importance of the mechanical dynamics of cell clusters, showing that fluid, deformable clusters with shorter relaxation times are more effective at sensing than solid ones. Additionally, the group explores the dynamics of lipid membranes, membrane proteins, and the behavior of cells in confined environments, contributing to a deeper understanding of the physical principles underlying cell motility and mechanics.

Research topics

• Computer Science
• Biology
• Neuroscience
• Chemistry
• Cell biology
• Psychology
• Optoelectronics
• Biological system
• Biophysics
• Nanotechnology
• Physics
• Materials science
• Communication
• Telecommunications
• Mathematics
• Genetics
• Biochemistry
• Mathematical analysis

Selected publications

• PyAFV: A Python package for active finite Voronoi simulations of nonconfluent tissues

  ArXiv.org · 2026-04-20

  otherOpen accessSenior author

  See the latest version in https://github.com/wwang721/pyafv and full documentation. Explore a collection of usage examples in Jupyter notebooks on Google Colab. See also an interactive simulation demo using PyAFV on Prof. Dapeng (Max) Bi’s homepage! What's Changed ♻️ Refactor plotting code to support per-cell color filling in… by @wwang721 in https://github.com/wwang721/pyafv/pull/43 📝 Add a benchmark comparison against SciPy's Voronoi ✏️ Fix Colab badge link in README 📝 Add paper arXiv link and citation info Full Changelog: https://github.com/wwang721/pyafv/compare/v0.4.8...v0.4.9

  PublisherOA PDFDOI

• wwang721/pyafv: Release v0.3.2

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-04

  otherOpen accessSenior author

  See the latest version in https://github.com/wwang721/pyafv What's Changed Docs update by @Copilot in https://github.com/wwang721/pyafv/pull/10 Update README doc by @wwang721 in https://github.com/wwang721/pyafv/pull/11 Deploy to PyPI API change from `afv `to `pyafv` Update README for new API and PyPI New Contributors @Copilot made their first contribution in https://github.com/wwang721/pyafv/pull/10 Full Changelog: https://github.com/wwang721/pyafv/compare/v0.3.0...v0.3.2

  PublisherDOI

• Guiding isotropic active fluids with anisotropic friction

  arXiv (Cornell University) · 2026-03-03

  preprintOpen accessSenior author

  Inspired by recent experiments of cells accumulating on anisotropic substrates, we study a two-dimensional, compressible, isotropic, active fluid in the presence of anisotropic friction. We find that regions of anisotropic friction that are patterned as positive topological defects may drive accumulation of an active fluid into a clump, but the robustness of this behavior depends on the initial configuration. If the initial azimuthal symmetry is sufficiently broken, we find that patterning asymmetry can instead lead to circular motion of accumulated clumps. We develop an approximate analytical model to qualitatively explain the motion. Finally, we use our simplified model to design a substrate pattern that creates directed motion of accumulated clusters along a given path.

  PublisherDOI

• Perfect adaptation in eukaryotic gradient sensing using cooperative allosteric binding

  Physical review. E · 2026-03-02 · 1 citations

  articleOpen accessSenior author

  Eukaryotic cells generally sense chemical gradients using the binding of chemical ligands to membrane receptors. To perform chemotaxis effectively in different environments, cells need to adapt to different concentrations. We present a model of gradient sensing where the affinity of receptor-ligand binding is increased when a protein binds to the receptor's cytosolic side. This interior protein (allosteric factor) alters the sensitivity of the cell, allowing the cell to adapt to different ligand concentrations. We propose a reaction scheme where the cell alters the allosteric factor's availability to adapt the average fraction of bound receptors to 1/2. We calculate bounds on the chemotactic accuracy of the cell and find that the cell can reach near-optimal chemotaxis over a broad range of concentrations. We find that the accuracy of chemotaxis depends strongly on the diffusion of the allosteric compound relative to other reaction rates. From this, we also find a tradeoff between adaptation time and gradient sensing accuracy.

  PublisherOA PDFDOI

• wwang721/pyafv: Release v0.4.5

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-06

  otherOpen accessSenior author

  See the latest version in https://github.com/wwang721/pyafv and full documentation. What's Changed Refactor API: Rename the attribute lambda_tension of class pyafv.PhysicalParams to Lambda for consistency by @wwang721 in https://github.com/wwang721/pyafv/pull/33 Jump patch version v0.4.4 Full Changelog: https://github.com/wwang721/pyafv/compare/v0.4.3...v0.4.5

  PublisherDOI

• Divergence of detachment forces in the finite Voronoi model.

  PubMed · 2026-04-16

  articleSenior author

  Detachment and fracture are central to many tissue-level processes, but they are challenging to simulate with Voronoi-type models that typically assume a confluent tissue. Here we analyze the finite Voronoi model, a nonconfluent extension of conventional Voronoi models, in which cell boundaries are composed of straight Voronoi edges and circular arcs of fixed radius $\ell$. When the line tension on cell-medium interfaces exceeds the tension on cell-cell contacts, we find that the model exhibits a strong time-step dependence in the fracture timescale of initially intact active clusters: decreasing $Δt$ can unphysically suppress cluster rupture events. We trace this behavior to a divergence of detachment forces in the finite Voronoi model and introduce a simple regularization. Finally, we calibrate the near-detachment mechanics against a deformable polygon model and examine how key physical parameters control the tissue fracture timescale under two different calibration strategies. Our results show that, for studies focused on fracture or intercellular adhesion in nonconfluent monolayers, a physically motivated calibration of near-detachment mechanics in the finite Voronoi model is essential.

  Publisher

• wwang721/py-afv: Release v0.3.0

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-02

  otherOpen accessSenior author

  See the latest version in https://github.com/wwang721/py-afv Breaking Changes We add a small cutoff $\delta$ to regulate the singularity of the detachment forces, and also provide methods to compute the optimal radius $\ell_0$ within the dataclass `PhysicalParams`. The new module structure changes the recommended import pattern from `from afv.finite_voronoi import PhysicalParams, FiniteVoronoiSimulator` to `import afv` followed by `afv.PhysicalParams`, etc. This is a breaking API change. What's Changed Feature/divergence by @wwang721 in https://github.com/wwang721/py-afv/pull/9 Full Changelog: https://github.com/wwang721/py-afv/compare/v0.2.2...v0.3.0

  PublisherDOI

• Guiding isotropic active fluids with anisotropic friction

  ArXiv.org · 2026-03-03

  articleOpen accessSenior author

  Inspired by recent experiments of cells accumulating on anisotropic substrates, we study a two-dimensional, compressible, isotropic, active fluid in the presence of anisotropic friction. We find that regions of anisotropic friction that are patterned as positive topological defects may drive accumulation of an active fluid into a clump, but the robustness of this behavior depends on the initial configuration. If the initial azimuthal symmetry is sufficiently broken, we find that patterning asymmetry can instead lead to circular motion of accumulated clumps. We develop an approximate analytical model to qualitatively explain the motion. Finally, we use our simplified model to design a substrate pattern that creates directed motion of accumulated clusters along a given path.

  PublisherOA PDF

• PyAFV: A Python package for active finite Voronoi simulations of nonconfluent tissues

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-22

  otherOpen accessSenior author

  See the latest version in https://github.com/wwang721/pyafv and full documentation. Explore a collection of usage examples in Jupyter notebooks on Google Colab. See also an interactive simulation demo using PyAFV on Prof. Dapeng (Max) Bi’s homepage! What's Changed ⚡️ Batch plot_2d primitives via LineCollection by @wwang721 in https://github.com/wwang721/pyafv/pull/45 ✨ Add standalone visualize_2d plotting utility by @wwang721 in https://github.com/wwang721/pyafv/pull/47 Full Changelog: https://github.com/wwang721/pyafv/compare/v0.4.9...v0.4.10

  PublisherDOI

• Intrinsic stochasticity in cell polarity and contact inhibition of locomotion

  ArXiv.org · 2026-04-20

  articleOpen accessSenior author

  When cells collide, they often exhibit "contact inhibition of locomotion" (CIL), a behavior in which cells repolarize and migrate away from the site of contact. Experimental CIL outcomes are highly variable - why? Here, we develop a minimal stochastic model to quantify how intrinsic noise in cell polarity, arising from the finite number of signaling molecules, influences CIL decision-making. We simulate polarization dynamics by tracking individual Rho GTPase proteins that diffuse and switch stochastically between the cell membrane and cytosol. In the absence of cell-cell contact, the polarity axis diffuses rotationally - the cell's orientation wanders - with a diffusion coefficient that decreases as Rho GTPase copy number increases. Assuming that cell-cell contact inhibits Rho GTPase activation, we investigate how contact geometry, duration, and strength affect CIL sensitivity. At low protein copy number, weak, brief, or spatially narrow contacts are masked by molecular noise. In contrast, at high protein copy number, intrinsic polarity noise is negligible, and randomness in CIL response is more likely to reflect the variability from collision to collision in the cell-cell contact properties.

  PublisherOA PDF

Recent grants

• Eukaryotic cell motility on patterned substrates and in complex environments

  NIH · $54k · 2015–2016

• Collaborative Research: Theory and experiment of contact inhibition of locomotion in nanofiber geometries

  NSF · $454k · 2021–2025

• CAREER: Theory of Membrane Shape Sensing at the Micron Scale

  NSF · $549k · 2020–2026

• Collective Gradient Sensing and Cell-to-Cell Variability - Theory and Experiment

  NSF · $480k · 2020–2024

• Models of collective migration that integrate single-cell polarity and mechanics

  NIH · $2.0M · 2021–2026

Frequent coauthors

• Wouter‐Jan Rappel

  University of California, San Diego

  28 shared

• Herbert Levine

  Northeastern University

  24 shared

• Frank L. H. Brown

  University of California, Santa Barbara

  17 shared

• Peter Friedl

  The University of Texas MD Anderson Cancer Center

  10 shared

• Yanxiang Zhao

  George Washington University

  10 shared

• Ifunanya Nwogbaga

  Johns Hopkins University

  8 shared

• Emiliano Pérez Ipiña

  Johns Hopkins University

  7 shared

• Juliane Zimmermann

  University of Cologne

  7 shared

Labs

• Camley GroupPI

  The Camley Group uses theoretical and computational approaches to study cell motility, collective motion, chemotaxis and cell sensing behaviors, and the mechanics of soft matter in cell biology.

Awards & honors

• Fannie and John Hertz Foundation support for PhD