Targeted Drug Delivery: Algorithmic Methods for Collecting a Swarm of Particles with Uniform External Forces
<|reference_start|>Targeted Drug Delivery: Algorithmic Methods for Collecting a Swarm of Particles with Uniform External Forces: We investigate algorithmic approaches for targeted drug delivery in a complex, maze-like environment, such as a vascular system. The basic scenario is given by a large swarm of micro-scale particles (''agents'') and a particular target region (''tumor'') within a system of passageways. Agents are too small to contain on-board power or computation and are instead controlled by a global external force that acts uniformly on all particles, such as an applied fluidic flow or electromagnetic field. The challenge is to deliver all agents to the target region with a minimum number of actuation steps.   We provide a number of results for this challenge. We show that the underlying problem is NP-complete, which explains why previous work did not provide provably efficient algorithms. We also develop several algorithmic approaches that greatly improve the worst-case guarantees for the number of required actuation steps. We evaluate our algorithmic approaches by numerous simulations, both for deterministic algorithms and searches supported by deep learning, which show that the performance is practically promising.<|reference_end|>
@article{becker2024targeted,
    title={Targeted Drug Delivery: Algorithmic Methods for Collecting a Swarm of
  Particles with Uniform External Forces},
    author={Aaron T. Becker, S'andor P. Fekete, Li Huang, Phillip Keldenich,
  Linda Kleist, Dominik Krupke, Christian Rieck, Arne Schmidt},
    journal={arXiv preprint arXiv:2408.09729},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09729},
    primaryClass={cs.CG}
}