Envision a scenario where nanobots travel through the human body, reaching a tumour and deploying their medicinal cargo to eliminate malignant cells while avoiding any unintentional damage. However, before we can successfully employ nanobots to combat cancer, we must surmount various practical and theoretical hurdles. Even for this highly specific application, a multitude of questions needs addressing: How do the nanobots locate the tumour cells? Can they assist each other? Is it safe for them to release the drug when they are 70% certain that the targeted cells are tumorous? Must all nanobots agree before taking action? What happens if some of the nanobots from the multitude malfunction? Considering the large numbers of nanobots involved, how many are likely to malfunction? Is there a risk of a collective, erroneous drug release? Given the critical nature of drug delivery within the human body, we need assurances that extend beyond heuristic predictions.
It would be imprudent to introduce nanobots into a human body based solely on their successful simulations and animal model results, without having a comprehensive theoretical understanding of their function. Therefore, the primary objective of this project is to develop theoretical assurances that facilitate consensus within noisy and harsh environments. The project takes an agent-based perspective, and understands nanobots as agents as understood in AI, improving on techniques for distributed AI to compute together. Other crucial aspects that need exploration include improving the precision with which nanorobots locate tumours, and enhancing the mechanisms that attract other nanorobots to the target site
Throughout the course of this PhD project, we will utilize an extensive range of methods based on mathematical reasoning. This project is truly interdisciplinary, fostering significant collaboration with biologists, chemists, and engineers.
As for the prerequisites, a robust mathematical and biological background is necessary.