Modeling feasible locomotion of nanobots for cancer detection and treatment
Author(s)
Harasha, Noble
Gava, Cristina
Lynch, Nancy
Contini, Claudia
Mallmann-Trenn, Frederik
Type
Journal Article
Abstract
Deploying motile nanosized particles, also known as “nanobots,” in the human body promises to improve selectivity in drug delivery and reduce side effects. We consider a swarm of nanobots locating a single cancerous region and treating it by releasing an onboard payload of drugs at the site. At nanoscale, the computation, communication, sensing, and locomotion capabilities of individual agents are extremely limited, noisy, and/or nonexistent. We present a general model to formally describe the individual and collective behavior of agents in a colloidal environment, such as the bloodstream, for cancer detection and treatment by nanobots. This includes a feasible and precise model of agent locomotion, inspired by actual nanoparticles that, in the presence of an external chemical gradient, move toward areas of higher concentration by means of self-propulsion. We present two variants of our model: the first assumes an endogenous chemical gradient fixed over time and centered at the cancer site; the second is a more speculative, dynamic variant in which agents themselves create and amplify a gradient centered at the cancer site. In both settings, agents sense the gradient and ascend it noisily, locating the cancer site more quickly than by simple Brownian motion. For the first variant, we present simulation and analytical results to bound the time it takes for agents to reach the cancer site. For the second, simulations highlight collective benefit from agent-generated signaling, showing significant runtime improvement via chemical signal amplification.
Date Issued
2025-12-02
Date Acceptance
2025-10-22
Citation
PNAS Nexus, 2025, 122 (48)
ISSN
2752-6542
Publisher
Oxford University Press
Journal / Book Title
PNAS Nexus
Volume
122
Issue
48
Copyright Statement
Copyright © 2025 the Author(s). Published by PNAS. This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Identifier
10.1073/pnas.2510036122/-/DCSupplemental
Subjects
Author contributions: N.H., C.G., N.L., C.C., and F.M.-T. designed research
N.H., C.G., and F.M.-T. performed research
N.H., C.G., N.L., C.C., and F.M.-T. contributed new analytic tools
N.H., C.G., and F.M.-T. analyzed data
Publication Status
Published
Article Number
e2510036122
Date Publish Online
2025-11-24