Diabetes, atherosclerosis, and cancer are the three diseases that contribute to most morbidity and mortality in the world. Mathematical Modeling of Atherosclerosis, Cancer, and Diabetes: Analysis and Simulation provides state-of-art mathematical models describing the dynamical behavior of these three diseases, focusing not only on numerical simulations but also on the mathematical analysis of the models themselves. Mathematical analysis in this context means rigorously studying some theoretical issues such as the ‘well-posedness’ of the models, steady solution, and their stability, and asymptotical behavior of the solutions.

The book is aimed at professional mathematical analysts, applied mathematicians, biophysicists, and biomedical engineers. The book can be used to teach graduate students, while it should be valuable reference for researchers in mathematical biology, dynamical systems, PDEs, and stochastic differential equations.

Features

• Collects the most recent research on both mathematical analysis and numerical simulation of mathematical models for diabetes, atherosclerosis, and cancer
• Includes both analysis and simulation of stochastic models for the diseases
• Provides a self-contained review of dynamical systems, partial differential equations, and stochastic differential equations
• Software code for readers who wish to practice data on any one of the existing models. The code is available in the programming languages of MATLAB® and/or Python.

Diabetes, atherosclerosis, and cancer are the three diseases that contribute to most morbidity and mortality in the world. Mathematical Modeling of Atherosclerosis, Cancer, and Diabetes: Analysis and Simulation provides state-of-art mathematical models describing the dynamical behavior of these three diseases, focusing not only on numerical simulations but also on the mathematical analysis of the models themselves. Mathematical analysis in this context means rigorously studying some theoretical issues such as the ‘well-posedness’ of the models, steady solution, and their stability, and asymptotical behavior of the solutions.

The book is aimed at professional mathematical analysts, applied mathematicians, biophysicists, and biomedical engineers. The book can be used to teach graduate students, while it should be valuable reference for researchers in mathematical biology, dynamical systems, PDEs, and stochastic differential equations.

Features

• Collects the most recent research on both mathematical analysis and numerical simulation of mathematical models for diabetes, atherosclerosis, and cancer
• Includes both analysis and simulation of stochastic models for the diseases
• Provides a self-contained review of dynamical systems, partial differential equations, and stochastic differential equations
• Software code for readers who wish to practice data on any one of the existing models. The code is available in the programming languages of MATLAB® and/or Python.

Diabetes, atherosclerosis, and cancer are the three diseases that contribute to most morbidity and mortality in the world. Mathematical Modeling of Atherosclerosis, Cancer, and Diabetes: Analysis and Simulation provides state-of-art mathematical models describing the dynamical behavior of these three diseases, focusing not only on numerical simulations but also on the mathematical analysis of the models themselves. Mathematical analysis in this context means rigorously studying some theoretical issues such as the ‘well-posedness’ of the models, steady solution, and their stability, and asymptotical behavior of the solutions.

The book is aimed at professional mathematical analysts, applied mathematicians, biophysicists, and biomedical engineers. The book can be used to teach graduate students, while it should be valuable reference for researchers in mathematical biology, dynamical systems, PDEs, and stochastic differential equations.

Features

• Collects the most recent research on both mathematical analysis and numerical simulation of mathematical models for diabetes, atherosclerosis, and cancer
• Includes both analysis and simulation of stochastic models for the diseases
• Provides a self-contained review of dynamical systems, partial differential equations, and stochastic differential equations
• Software code for readers who wish to practice data on any one of the existing models. The code is available in the programming languages of MATLAB® and/or Python.