Dynamic In Vivo Computation for Learning-Based Nanobiosensing in Time-Varying Biological Landscapes

We have recently proposed a framework of in vivo computation (IVC) which transforms the early tumor sensing problem into a computational problem. In the framework, a tumor-triggered biological gradient field (BGF) guides the swarm-intelligence-assisted targeting process, where externally manipulable and trackable magnetic nanorobots act as computational agents for the optimization procedure. As BGF can be viewed as an objective function which is utilized to define the fitness landscape for the agents, the inherent attributes of BGF are critical to the IVC process. All our previous investigations are based on the hypothesis that the BGF landscape remains time invariant during the tumor-targeting process, which results in a static function optimization problem. However, the properties of internal environment, such as the flow state of body fluid, will naturally lead to time-dependent variation of BGF, which means that the targeting process should be modeled as a dynamic function optimization problem. Based on this consideration, we focus on dynamic IVC by considering different variation patterns of BGF in this article. Two computational intelligence strategies named “swarm-based learning” and “individual-based learning” are proposed for dealing with the turbulence of the fitness estimation caused by the BGF variation. The in silico experiments and statistical results demonstrate the effectiveness of the proposed strategies. In addition, the above process is conducted in a 3-D search space, where the tumor vascular network is generated by an invasion percolation algorithm, which is more realistic compared to the 2-D search space in our previous works.