In Vivo Computation for Tumor Sensitization and Targeting at Different Tumor Growth Stages.

We look into the novel paradigm of in vivo computation for tumor sensitization and targeting (TST), which aims at detecting a tumor by considering TST as a computational process. Nanorobots are utilized as computational agents to search for the tumor in the high-risk tissue with the aided knowledge of the tumor-triggered biological gradient field (BGF), which is similar to an optimization process. All our previous work is about the detection of tumor with a priori size, which is not convincing enough as the exact size of the tumor targeted cannot be obtained in advance. We focus on the TST for tumor with unknown size by considering the tumor growth process in this paper. The weak priority evolution strategy (WP-ES) based in vivo computational algorithm proposed in our previous work is utilized for the TST at three tumor growth stages for two representative landscapes by considering the nanorobots’ lifespans and other realistic constraints. Furthermore, we propose the “tension and relaxation (T-R)” principle, which is used for the actuating of nanorobots in the TST process for the tumor with unknown size. The experimental results demonstrate the effectiveness of the proposed in vivo computational algorithm and principle for the TST at different tumor growth stages.