BibTex format
@article{Harasha:2025,
author = {Harasha, N and Gava, C and Lynch, N and Contini, C and Mallmann-Trenn, F},
journal = {PNAS Nexus},
title = {Modeling feasible locomotion of nanobots for cancer detection and treatment},
year = {2025}
}
Citation
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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 locomotioncapabilities 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 towards areas of higher concentration by means of self-propulsion. We present two variants of our general model: The first variant assumes an endogenous chemical gradient that is fixed over time and centered at the targeted cancer site; the second is a more speculative and dynamicvariant in which agents themselves create and amplify a chemical gradient centered at the cancer site. In both settings, agents can sense the gradient and ascend it noisily, locating the cancer site more quickly than via simple Brownian motion.For the first variant of the model, we present simulation results to show the behavior of agents under our locomotion model, as well as analytical results to bound the time it takes for the agents to reach the cancer site. We show that the agent’s locomotion follows threedistinct phases, determined by its distance from the cancer site. For the second variant, simulation results highlight the collective benefit in having agents issue their own chemical signal. The second variant of the model, while arguably more speculative in its agent capability assumptions, shows a significant improvement in runtime performance over the first variant, resulting f
AU - Harasha,N
AU - Gava,C
AU - Lynch,N
AU - Contini,C
AU - Mallmann-Trenn,F
PY - 2025///
SN - 2752-6542
TI - Modeling feasible locomotion of nanobots for cancer detection and treatment
T2 - PNAS Nexus
ER -
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