Modeling of Cytokines - Mediated Cancer Cells Signaling Through Artificial Intelligence

Abstract

Modeling of Cytokines - Mediated Cancer Cells Signaling through Artificial Intelligence Cancer is one of the most deadly diseases in the world. Although our knowledge of this disease is constantly increasing, many types of cancer continue to have insufficient treatments. Mathematical modeling of cancer cells has developed significantly over the years, with mathematics being used to comprehend the more complex mechanism of the cancerous tumor. In this research, novel mathematical models are developed to address the complex biological problem of breast cancer signaling and associated biomarkers. We apply analytic approaches such as stability analysis, bifurcation analysis, and sensitivity analysis to validate models. The research is based not only on traditional analytical methods of model validation but also on developing a novel approach to optimize parametric values via explainable artificial intelligence. The computational framework and associated algorithms are simulated on swift software, including MATLAB and Python. The developed models can identify the most critical biological factors during breast tumor invasion, describe how cancer cells and immune cells influence the outcome of chemotherapy and immunotherapy, investigate how drug infusion methods influence chemotherapy success and assess the efficacy of hormonal therapy in bone remodeling disorders. It is expected that the resulting models will help the oncologists to make useful predictions and thus in improving the treatment strategies of breast cancer.