Predator-Prey Adaptive Control for Exosome-based Molecular Communications Glioblastoma Treatment

Fonseca, Caio and Barros, Michael Taynnan and Odysseos, Andreani and Balasubramaniam, Sasitharan (2021) Predator-Prey Adaptive Control for Exosome-based Molecular Communications Glioblastoma Treatment. In: ICC 2021 - IEEE International Conference on Communications, Proceedings :. IEEE International Conference on Communications . Institute of Electrical and Electronics Engineers Inc., CAN. ISBN 9781728171227

Full text not available from this repository. (Request a copy)


Glioblastoma Multiform (GBM) is known as one of the most malignant tumours in the brain, and challenges remain in developing effective therapeutic solutions. This paper addresses an open-loop control molecular communication system using an adaptive algorithm that controls engineered induced Neural Stem Cells (iNSCs) to release therapeutic exosomes for treating GBM. The adaptive algorithm is based on the Lotka-Volterra Predator-Prey model, and virtually monitors the tumour growth from an external Brain-Machine Interface to control the release of the exosomes for the treatment. We developed the model to incorporate the control from an external RF signal that controls the production of exosomes as well as the diffusion propagation of exosomes through a 3D simulated Extracellular Space tissue. Based on numerical analysis coupled with simulations, we found that factors such as stochastic propagation of exosomes influence the aggressiveness of the model to tackle the tumour. This work can lay the foundation for future adaptive Brain-Machine Interface that controls molecular communication system for GBM treatment.

Item Type: Book Section
Additional Information: Funding Information: The Fig. 6b illustrates the results for the application of the proportional controller for the first case, where the parameter σ is variable. We observe the influence of the controller in decreasing the time or number of days to eradicate the tumor efficiently. In some cases, the controller can also increase the time to eradicate the tumor, depending on the amplitude modulation value. This happens, because in this scenario, applying different modulations will produce different values for the variable σ and as observed in Fig. 6a, some values of σ can result in a longer time to eliminate the tumor. This means that high values of σ are not necessarily the best values, the best choices for σ are the ones shown in the simulations to eradicate the tumor faster. The second case, the proportional controller, kp, is added into the model, while the parameter σ is fixed in one value. The influence of the controller in the model is similar to the first case, being able to drastically reduce the number of days to eliminate the tumor efficiently. Although, the difference between the two cases is that the second is more dependent on the value of the parameter σ. This can be observed in Fig. 6c, where the results are closer almost independently to the amplitude modulation. This is explained by the fact that the controller cannot change the value of the variable σ, which means that the controller as defined by the amplitude modulation will make all new values oscillate around the fixed variable σ. The results of this system can become more precise and accurate, once the biological parameters relative to the exosomes and the glioblastoma tumor are more studied and modelled further. V. CONCLUSION In this paper, we model the interactions between exosomes and the glioblastoma tumor cells mathematically, as a dynamical predator-prey system, where the exosomes represent the predator, and the glioblastoma tumor cells represent the prey. Additionally, we present an adaptive control algorithm for brain-machine interfaces in the context of molecular communication systems theranostics and targeted drug delivery. An external RF signal is used as an input control signal to control the dynamics of the model, and consequently, eradicate the tumor more efficiently by the application or addition of a proportional controller into the system. We analyze two possible cases for this control system taking. The first one takes into consideration the potential of the control system to modify one of the biological parameters of the system. The second one considers that these biological variables cannot be changed by the external input and so the controller has to be added into the model as another variable of the system. We consider the molecular communication established between the exosomes and glioblastoma tumor cells as well as the propagation of the exosomes through the ECS channel and how this will be influence and be influenced by the adaptive control algorithm. The results were able to show that the controller is efficient in eradicating the tumor by drastically reducing the required time. This work paves the way to novel biotechnology solutions to tumour theranostics using principles of molecular communications. For our future work, we aim to extend our adaptive control algorithm to a closed-loop control system using the response feedback from the messaging NSCs in order to adjust the input signal to have a real-time control signal. ACKNOWLEDGMENT This work was funded by the EU under grant No. 828837 (EU-H2020-FETOpen GLADIATOR Next-generation Thera-nostics of Brain Pathologies with Autonomous Externally Controllable Nanonetworks: a Transdisciplinary Approach with Bio-nanodevice Interfaces). S. Balasubramaniam is also funded in part by FutureNeuro from Science Foundation Ireland (SFI) under Grant Number 16/RC/3948 and co-funded under the European Regional Development Fund and by FutureNeuro industry partners. REFERENCES Publisher Copyright: © 2021 IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1705
Departments or Groups:
Depositing User: Admin SSL
Date Deposited: 19 Oct 2022 23:18
Last Modified: 07 Jun 2023 18:40

Actions (login required)

View Item View Item