Tension-Relaxation In Vivo Computing Principle for Tumor Sensitization and Targeting

Shaolong SHI, Neda SHARIFI, Yifan CHEN, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

4 Citations (Scopus)


By modeling the tumor sensitization and targeting (TST) as a natural computational process, we have proposed the framework of nanorobots-assisted in vivo computation. The externally manipulable nanorobots are steered to detect the tumor in the high-risk tissue, which is analogous to the process of searching for the optimal solution by the computing agents in the search space. To overcome the constraint of a nanorobotic platform that can only generate a uniform magnetic field to actuate the nanorobots, we have proposed the weak priority evolution strategy (WP-ES) in our previous works. However, these works do not consider the proportions of the nanorobot control and tracking operations, which are part and parcel of in vivo computation as the control operation aims at searching for the tumor effectively while the tracking mode is used for gathering information about the biological gradient function (BGF). Careful planning about the durations spent in these operations is needed for optimal performance of the TST strategy. To account for this issue, in the current article, we propose a novel computational principle, called the tension-relaxation (T-R) principle, to balance the displacements of nanorobots during each control and tracking cycle. Furthermore, we build three tumor vascular models with different sizes to represent three different targeting regions as the morphology of tumor vasculature determined by the tumor growth process is an important factor affecting TST. We carry out the computational experiments for tumors with three different sizes for several representative landscapes by introducing the T-R principle into the WP-ES-based swarm intelligence algorithms and considering the realistic internal constraints. The experimental outcomes demonstrate the effectiveness of the proposed TST strategy. IEEE
Original languageEnglish
JournalIEEE Transactions on Cybernetics
Early online date20 Feb 2021
Publication statusPublished - Sept 2022
Externally publishedYes

Bibliographical note

This work was supported in part by the Guangdong Provincial Key Laboratory under Grant 2020B121201001; in part by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams under Grant 2017ZT07X386; and in part by the Shenzhen Science and Technology Program under Grant KQTD2016112514355531.


  • Control systems
  • Estimation
  • Evolution strategy
  • In vivo
  • in vivo computation
  • magnetic field control
  • Magnetic resonance imaging
  • Nanobioscience
  • nanorobots
  • Process control
  • swarm intelligence
  • tumor detection
  • Tumors


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