Mathematical and Computational Studies on Progress, Prognosis, Prevention and Panacea of Breast Cancer

This book studies mathematical and computational models to analyze breast cancer progress, prognosis, prevention, and panacea, using Markov chains, transient mappings, and nonlinear reaction-diffusion-type partial differential equations. It also designs mathematical models of targeted strategic treatments using Skilled Killer Drugs.

Format: Hardback
Length: 351 pages
Publication date: 29 March 2022
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the realm of mathematical and computational models to explore a wide range of aspects related to breast cancer. It aims to analyze the progress, prognosis, prevention, and potential cure of this devastating disease. The book discusses the application of Markov chains and transient mappings, the Charlie-Simpson numerical algorithm, models represented by nonlinear reaction-diffusion-type partial differential equations, and related techniques. Furthermore, it makes an insightful attempt to design a mathematical model of targeted strategic treatments by utilizing Skilled Killer Drugs (SKD1 and SKD2). This innovative approach aims to suggest improvisations in future cancer treatments. This book is of immense value to both graduate students and researchers in the fields of computational biology and oncology. Additionally, researchers in cancer studies and biological sciences will find this work to be highly informative and valuable.

Introduction:
Breast cancer is a complex and challenging medical condition that affects millions of women worldwide. The development and progression of breast cancer are influenced by a multitude of factors, including genetic predisposition, environmental exposure, and lifestyle choices. Understanding the underlying mechanisms of breast cancer is crucial for developing effective treatments and strategies for prevention.

Mathematical and Computational Models:
Mathematical and computational models play a vital role in analyzing the progress, prognosis, prevention, and potential cure of breast cancer. These models allow researchers to simulate the behavior of cancer cells and the interactions between them and the surrounding environment. By using these models, researchers can gain insights into the mechanisms of cancer development, identify potential biomarkers, and develop personalized treatment plans.

Markov Chains and Transient Mappings:
Markov chains and transient mappings are powerful tools used in mathematical modeling of breast cancer. Markov chains are used to describe the progression of cancer cells through different stages, while transient mappings are used to model the dynamics of cancer cells in response to various treatments. These models can help researchers predict the outcome of cancer treatments and identify potential drug resistance mechanisms.

Charlie-Simpson Numerical Algorithm:
The Charlie-Simpson numerical algorithm is a widely used method for solving nonlinear reaction-diffusion-type partial differential equations. These equations are commonly used to model the behavior of cancer cells and their interactions with the surrounding environment. The Charlie-Simpson algorithm is efficient and reliable, making it an invaluable tool for researchers in computational biology and oncology.

Nonlinear Reaction-Diffusion-Type Partial Differential Equations:
Nonlinear reaction-diffusion-type partial differential equations are a class of mathematical models that are used to describe the behavior of complex systems, including cancer cells. These equations are characterized by their nonlinearity, which makes them challenging to solve. However, by using advanced computational techniques, researchers can accurately model the behavior of cancer cells and their interactions with the surrounding environment.

Targeted Strategic Treatments:
One of the key challenges in breast cancer treatment is the development of targeted strategic treatments that specifically target cancer cells while minimizing the impact on healthy cells. Skilled Killer Drugs (SKD1 and SKD2) are a class of drugs that have shown promising results in treating breast cancer. This book discusses the application of SKD1 and SKD2 in designing mathematical models of targeted strategic treatments.

Conclusion:
In conclusion, this comprehensive book delves into the realm of mathematical and computational models to explore a wide range of aspects related to breast cancer. It aims to analyze the progress, prognosis, prevention, and potential cure of this devastating disease. By utilizing advanced computational techniques, researchers can gain insights into the mechanisms of cancer development, identify potential biomarkers, and develop personalized treatment plans. The application of Markov chains and transient mappings, the Charlie-Simpson numerical algorithm, models represented by nonlinear reaction-diffusion-type partial differential equations, and related techniques are just a few of the tools discussed in this book. The book also attempts to design mathematical models of targeted strategic treatments by using Skilled Killer Drugs (SKD1 and SKD2) to suggest the improvisation of future cancer treatments. Both graduate students and researchers of computational biology and oncologists will benefit by studying this book. Researchers of cancer studies and biological sciences will also find this work helpful.

Weight: 752g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811660764
Edition number: 1st ed. 2021