An Adaptive Robust Control Strategy in a Cancer Tumor-Immune System under Uncertainties

Optimizing chemotherapy treatment outcomes using metaheuristic optimization algorithms: A case study

BACKGROUND

This study explores the dynamics of a mathematical model, utilizing ordinary differential equations (ODE), to depict the interplay between cancer cells and effector cells under chemotherapy. The stability of the equilibrium points in the model is analysed using the Jacobian matrix and eigenvalues. Additionally, bifurcation analysis is conducted to determine the optimal values for the control parameters.

OBJECTIVE

To evaluate the performance of the model and control strategies, benchmarking simulations are performed using the PlatEMO platform.

METHODS

The Pure Multi-objective Optimal Control Problem (PMOCP) and the Hybrid Multi-objective Optimal Control Problem (HMOCP) are two different forms of optimal control problems that are solved using revolutionary metaheuristic optimisation algorithms. The utilization of the Hypervolume (HV) performance indicator allows for the comparison of various metaheuristic optimization algorithms in their efficacy for solving the PMOCP and HMOCP.

RESULTS

Results indicate that the MOPSO algorithm excels in solving the HMOCP, with M-MOPSO outperforming for PMOCP in HV analysis.

CONCLUSION

Despite not directly addressing immediate clinical concerns, these findings indicates that the stability shifts at critical thresholds may impact treatment efficacy.

Adaptive Tracking Control for Uncertain Cancer-Tumor-Immune Systems

In this paper, the problem of control is investigated for cancer-tumor-immune systems, based on a two-dimension uncertain nonlinear model describing the interaction between immune and cancer cells in a body. First, the control problem is transformed into a state tracking problem. Second, an adaptive control method is proposed to track and stop the growth of cancer and maintain cancer and immune cells at an acceptable level. Different from the existing results in literature, the singularity problem in controller and the inaccuracy in control design have been overcome. From theoretical analysis, it is shown that the resulting closed-loop system is asymptotically stable and the tracking errors converge to the origin. Finally, simulation results illustrate not only the competitive relationship between immune system and tumor, but also the immune system has strong immunity to low level tumor volumes.

Target controllability with minimal mediators in complex biological networks

Controllability of a complex network system is related to finding a set of minimum number of nodes, known as drivers, controlling which allows having a full control on the dynamics of the network. For some applications, only a portion of the network is required to be controlled, for which target control has been proposed. Often, along the controlling route from driver nodes to target nodes, some mediators (intermediate nodes) are also unwillingly controlled, which might cause various side effects. In controlling cancerous cells, unwillingly controlling healthy cells, might result in weakening them, thus affecting the immune system against cancer. This manuscript proposes a suitable candidate solution to the problem of finding minimum number of driver nodes under minimal mediators. Although many others have attempted to develop algorithms to find minimum number of drivers for target control, the newly proposed algorithm is the first one that is capable of achieving this goal and at the same time, keeping the number of the mediators to a minimum. The proposed controllability condition, based on path lengths between node pairs, meets Kalman's controllability rank condition and can be applied on directed networks. Our results show that the path length is a major determinant of in properties of the target control under minimal mediators. As the average path length becomes larger, the ratio of drivers to target nodes decreases and the ratio of mediators to targets increases. The proposed methodology has potential applications in biological networks. The source code of the algorithm and the networks that have been used are available from the following link: https://github.com/LBBSoft/Target-Control-with-Minimal-Mediators.git.

A Review of ULK1-Mediated Autophagy in Drug Resistance of Cancer

The difficulty of early diagnosis and the development of drug resistance are two major barriers to the successful treatment of cancer. Autophagy plays a crucial role in several cellular functions, and its dysregulation is associated with both tumorigenesis and drug resistance. Unc-51-like kinase 1 (ULK1) is a serine/threonine kinase that participates in the initiation of autophagy. Many studies have indicated that compounds that directly or indirectly target ULK1 could be used for tumor therapy. However, reports of the therapeutic effects of these compounds have come to conflicting conclusions. In this work, we reviewed recent studies related to the effects of ULK1 on the regulation of autophagy and the development of drug resistance in cancers, with the aim of clarifying the mechanistic underpinnings of this therapeutic target.