A Dynamic In Vivo Computational Strategy for Tumor Targeting

We have proposed a novel framework of in vivo computation by transforming the tumor targeting problem into a computational problem in our previous work. The tumor-triggered biological gradient field (BGF) provides aided knowledge for the swarm intelligence based targeting process, where the externally manipulable and traceable nanorobots act as the agents. All our previous investigations are based on the hypothesis that the BGF landscape remains time-invariant during the tumor targeting process, which is viewed as an in vivo static function optimization problem (SFOP). However, the flow state of body fluid will bring about the dynamic variation of the BGF, which means the targeting process should be rendered into a dynamic function optimization problem (DFOP). Subsequently, we focus on the dynamic in vivo computation problem by considering the different variation rules of the BGF landscape in this paper. A computational strategy named “optimal state extraction (OSE)” is proposed for overcoming the turbulence of the fitness estimation caused by the BGF variation to perform the tumor targeting efficiently. Furthermore, the above process is conducted in a three-dimensional search space, where the tumor vascular network is generated by an invasion percolation algorithm.