Search Space Analysis for In Vivo Computation for Smart Tumor Targeting

In vivo computation transforms smart tumor targeting using swarms of externally manipulable nanorobots into an optimization problem with tumor being the optimal solution to be found, the high-risk tissue as the search space, and nanorobots the computational agents. It poses a unique challenge for search space analysis (SSA) due to physical constraints of the biological phenomenon. In this paper, we propose three new SSA measures and validate them with numerical simulations on two different tumor vascular networks. The less vascularized network was found to result in a more discrete search space, less likelihood of countercurrent desirable search direction, and more significant heterogeneity in various search directions. Finally, numerical results are presented to demonstrate the principles of the proposed strategy.