Bio-inspired Self-regulated In-vivo Computation for Smart Cancer Detection

This paper highlights a novel knowledge-less bio-inspired systemic targeting strategy (STS) for tumor homing in complex human vasculature. We propose that biological organisms at very small scale such as nanoparticles can perform deterministic tasks when they aggregate and migrate together. We aim to demonstrate through computational experiments that nanoparticles which can act as contrast agents, use tumor triggered bio-physical gradients collectively to move towards the tumor and deposit themselves on it to highlight the disease area hence increasing the diagnostic efficiency of different existing medical imaging techniques. Despite the fact that individual nanoparticles have very limited locomotive and computational capability, we show that still when combined together, they can perform complex tasks such as obstacle avoidance while detecting target. We believe that our work motivates a novel non-centralized self-dependent approach for tumor targeting amplification.