Multiscale Modelling in Biomedical Engineering (IEEE Press Series on Biomedical Engineering)

✍ Scribed by Antonis I. Sakellarios, Vassiliki T. Potsika, Dimitrios I. Fotiadis

• Publisher: Wiley-IEEE Press
• Year: 2023
• Tongue: English
• Leaves: 402
• Edition: 1
• Category: Library

✦ Synopsis

Multiscale Modelling in Biomedical Engineering

Discover how multiscale modeling can enhance patient treatment and outcomes

In Multiscale Modelling in Biomedical Engineering, an accomplished team of biomedical professionals delivers a robust treatment of the foundation and background of a general computational methodology for multi-scale modeling. The authors demonstrate how this methodology can be applied to various fields of biomedicine, with a particular focus on orthopedics and cardiovascular medicine.

The book begins with a description of the relationship between multiscale modeling and systems biology before moving on to proceed systematically upwards in hierarchical levels from the molecular to the cellular, tissue, and organ level. It then examines multiscale modeling applications in specific functional areas, like mechanotransduction, musculoskeletal, and cardiovascular systems.

Multiscale Modelling in Biomedical Engineering offers readers experiments and exercises to illustrate and implement the concepts contained within. Readers will also benefit from the inclusion of:

• A thorough introduction to systems biology and multi-scale modeling, including a survey of various multi-scale methods and approaches and analyses of their application in systems biology
• Comprehensive explorations of biomedical imaging and nanoscale modeling at the molecular, cell, tissue, and organ levels
• Practical discussions of the mechanotransduction perspective, including recent progress and likely future challenges
• In-depth examinations of risk prediction in patients using big data analytics and data mining

Perfect for undergraduate and graduate students of bioengineering, biomechanics, biomedical engineering, and medicine, Multiscale Modelling in Biomedical Engineering will also earn a place in the libraries of industry professional and researchers seeking a one-stop reference to the basic engineering principles of biological systems.

✦ Table of Contents

Cover
Title Page
Copyright Page
Contents
Author Biographies
Preface
List of Abbreviations
List of Terms
Chapter 1 Systems Biology and Multiscale Modeling
1.1 Introduction
1.2 Systems Biology
1.3 Systems Biology Modeling Goals
1.4 Systems Biology Modeling Approach
1.5 Application of Multiscale Methods in Systems Biology
1.5.1 Introduction
1.6 The Use of Systems Biology and Multiscale Modeling in Biomedical and Medical Science
1.7 Application of Computational Methods in Biomedical Engineering
1.7.1 Fundamental Principles
1.7.2 Finite Element Method
1.7.3 Boundary Element Method
1.7.4 Finite Differences Method
1.8 Challenges
References
Chapter 2 Biomedical Imaging
2.1 Introduction
2.2 X-ray Radiography
2.2.1 X-ray Interaction with Tissues
2.2.2 Medical Applications of X-rays
2.3 Computed Tomography
2.3.1 The Principle of CT Imaging
2.3.2 The Evolution of CT Scanners
2.3.3 Medical Applications of CT Imaging
2.3.3.1 Application of CT Imaging in Cancer
2.3.3.2 Application of CT Imaging in Lungs
2.3.3.3 Application of CT Imaging in Cardiovascular Disease
2.3.3.4 Application of CT Imaging in Other Fields
2.3.4 Radiation of CT Imaging
2.4 Diagnostic Ultrasound
2.4.1 The Principle of US
2.4.2 Medical Applications of US
2.5 Magnetic Resonance Imaging
2.5.1 MRI Principle
2.5.2 Medical Applications of MRI
2.6 Positron Emission Tomography (PET)
2.6.1 The Principle of PET
2.6.2 Medical Applications of PET
2.7 Single Photon Emission Computed Tomography
2.7.1 The Principle of SPECT
2.7.2 Medical Applications of SPECT
2.8 Endoscopy
2.8.1 Medical Applications of Endoscopy
2.9 Elastography
2.9.1 Elastographic Techniques
2.9.2 Elastographic Medical Applications
2.10 Conclusions and Future Trends
References
Chapter 3 Computational Modeling at Molecular Level
3.1 Introduction
3.2 Introduction to Molecular Mechanics
3.2.1 Chemical Formulas
3.2.2 Molecular Structure and Polarity
3.2.2.1 Mathematical Modeling of Polarizing Biochemical Systems
3.3 Molecular Bioengineering in Areas Critical to Human Health
3.3.1 Cell Biology
3.3.1.1 Biology of Growth Factor Systems
3.3.2 Diagnostic Medicine
3.3.2.1 Lab-on-a-Chip Devices
3.3.2.2 Biosensors
3.3.3 Preventive Medicine
3.3.4 Therapeutic Medicine
3.3.4.1 Drug Delivery
3.3.4.2 Tissue Engineering
References
Chapter 4 Computational Modeling at Cell Level
4.1 Introduction
4.2 Introduction to Cell Mechanics
4.2.1 Cell Material Properties
4.2.2 Cell Composition and Structure
4.3 Cellular Bioengineering in Areas Critical to Human Health
4.3.1 Biology
4.3.2 Diagnostic Medicine
4.3.2.1 Organ Chip Technology
4.3.2.2 Mechanosensors
4.3.3 Therapeutic Medicine
4.3.3.1 Drug Delivery
4.3.3.2 Tissue Engineering
4.3.4 P4 Medicine
References
Chapter 5 Computational Modeling at Tissue Level
5.1 Introduction
5.2 Epithelial Tissue
5.2.1 Composition and Properties of Epithelial Tissue
5.2.2 Computational Modeling of Epithelial Tissue
5.3 Connective Tissue
5.3.1 Composition and Properties of Connective Tissue
5.3.2 Computational Modeling of Connective Tissue
5.4 Muscle Tissue
5.4.1 Composition and Properties of Muscle Tissue
5.4.2 Computational Modeling of Muscle Tissue
5.4.2.1 Computational Modeling of Skeletal Muscle Tissue
5.4.2.2 Computational Modeling of Smooth Muscle Tissue
5.4.2.3 Computational Modeling of Cardiac Muscle Tissue
5.4.2.4 Musculotendon Models
5.5 Nervous Tissue
5.5.1 Computational Modeling of Brain Tissue
5.5.2 Computational Modeling of the Spinal Cord Tissue
5.5.3 Computational Modeling of Peripheral Nerves
5.6 Conclusion
References
Chapter 6 Macroscale Modeling at the Organ Level
6.1 Introduction
6.2 The Respiratory System
6.2.1 Computational Modeling of the Respiratory System
6.3 The Digestive System
6.3.1 Computational Modeling of the Digestive System
6.4 The Cardiovascular System
6.4.1 Computational Modeling of the Cardiovascular System
6.5 The Urinary System
6.5.1 Computational Modeling of the Urinary System
6.6 The Integumentary System
6.6.1 Computational Modeling of the Integumentary System
6.7 The Musculoskeletal System
6.7.1 Introduction to the Skeletal System
6.7.2 Introduction to the Muscular System
6.7.3 Computational Modeling of the Muscular-Skeletal System
6.8 The Endocrine System
6.8.1 Computational Modeling of the Endocrine System
6.9 The Lymphatic System
6.9.1 Computational Modeling of the Lymphatic System
6.10 The Nervous System
6.10.1 Computational Modeling of the Nervous System
6.11 The Reproductive System
6.11.1 Computational Modeling of the Reproductive System
6.12 Conclusion
References
Chapter 7 Mechanotransduction Perspective, Recent Progress and Future Challenges
7.1 Introduction
7.2 Methods for Studying Mechanotransduction
7.2.1 How Mechanical Forces Are Detected
7.2.2 Transmission of Mechanical Forces
7.2.3 Conversion of Mechanical Forces to Signals
7.3 Mathematical Models of Mechanotransduction
7.3.1 ODE Based Computational Model
7.3.2 PDE Based Computational Model
7.3.2.1 Mechanical Factors that Affect Cell Differentiation and Proliferation
7.3.2.2 A Case Example of Multi-Scale Modeling Cell Differentiation and Proliferation
7.3.3 Methodology of a Hybrid Multi-Scale Approach
7.3.3.1 The Agent-Based Model (ABM)
7.3.3.2 Mechanical Model
7.4 Challenges
References
Chapter 8 Multiscale Modeling of the Musculoskeletal System
8.1 Introduction
8.2 Structure of the Musculoskeletal System
8.2.1 Structure of the Skeletal System Components
8.2.2 Structure of the Muscular System Components
8.3 Elasticity
8.4 Mechanical Characteristics of Muscles
8.5 Multiscale Modeling Approaches of the Musculoskeletal System
8.5.1 Multiscale Modeling of Bones
8.5.2 Multiscale Modeling of Articular Cartilage
8.5.3 Multiscale Modeling of Tendons and Ligaments
8.5.3.1 Advances in Multiscale Modeling of Tendons
8.5.3.2 Advances in Multiscale Modeling of Ligaments
8.5.4 Multiscale Modeling of the Skeletal Muscle
8.5.5 Multiscale Modeling of the Smooth Muscle
8.6 Conclusion
References
Chapter 9 Multiscale Modeling of Cardiovascular System
9.1 Introduction
9.2 Cardiovascular Mechanics
9.2.1 Visualization of the Cardiovascular System and 3D Arterial Reconstruction
9.2.2 Blood Flow Modeling
9.2.2.1 Steady and Pulsatile Flow of Blood
9.2.2.2 Computational Fluid Dynamics Modeling
9.2.2.3 Newtonian and Non-Newtonian Behavior of Blood
9.2.3 Plaque Growth Modeling
9.2.4 Agent-Based Modeling
9.2.4.1 Key Components of Agent-Based Modelling
9.2.4.2 Agent-Based Modelling and Simulation Approach
9.2.4.3 Problem Definition
9.2.4.4 ABM Applications in Cardiovascular Systems
9.2.5 Discrete Particle Dynamics
9.2.6 Multiscale Model of Drug Delivery/Restenosis
9.2.6.1 Benefits of Multiscale Model of Drug Delivery/Restenosis
9.3 Conclusions
References
Chapter 10 Risk Prediction
10.1 Introduction
10.2 Medical Data Preprocessing
10.2.1 Data Sharing
10.2.2 Data Harmonization
10.3 Machine Learning and Data Mining
10.3.1 Supervised Learning Algorithms
10.3.1.1 Regression Analysis
10.3.1.2 Support Vector Machines
10.3.1.3 Naïve Bayes
10.3.1.4 Decision Trees
10.3.1.5 Ensemble Classifiers
10.3.1.6 Artificial Neural Networks
10.3.1.7 K-Means
10.3.1.8 Spectral Clustering
10.3.1.9 Hierarchical Clustering
10.4 Explainable Machine Learning
10.4.1 Transparency
10.4.2 Evaluation and Types of Explanation
10.5 Example of Predictive Models in Cardiovascular Disease
10.6 Conclusion
References
Chapter 11 Future Trends
11.1 Virtual Populations
11.1.1 Methods for Virtual Population Generation
11.1.2 A Methodological Approach for a Virtual Population
11.1.2.1 Multivariate Log-Normal Distribution (log-MVND)
11.1.2.2 Supervised Tree Ensembles
11.1.2.3 Unsupervised Tree Ensembles
11.1.2.4 Radial Basis Function-Based Artificial Neural Networks
11.1.2.5 Bayesian Networks
11.1.2.6 Performance Evaluation of the Quality of the Generated Virtual Patient Data
11.1.3 A Novel Approach for a Virtual Population Combining Multiscale Modeling
11.2 Digital Twins
11.2.1 Ecosystem of the Digital Twin for Health
11.2.2 An Example Workflow of a Digital Twin
11.3 Integrating Multiscale Modeling and Machine Learning
11.3.1 Physics-Informed NN (PINN)
11.3.2 Deep NN Algorithms Inspired by Statistical Physics and Information Theory
11.4 Conclusion and Future Trends
References
Index
EULA