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Multiscale Modelling in Biomedical Engineering [Kõva köide]

(University of Ioannina, Greece), ,
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Multiscale Modelling in Biomedical EngineeringSuurem pilt
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Püsilink: https://www.kriso.ee/db/9781119517344.html
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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.
Author Biographies xi
Preface xiii
List of Abbreviations
xvii
List of Terms
xxiii
1 Systems Biology and Multiscale Modeling
1(28)
1.1 Introduction
1(1)
1.2 Systems Biology
2(1)
1.3 Systems Biology Modeling Goals
3(2)
1.4 Systems Biology Modeling Approach
5(3)
1.5 Application of Multiscale Methods in Systems Biology
8(2)
1.5.1 Introduction
8(2)
1.6 The Use of Systems Biology and Multiscale Modeling in Biomedical and Medical Science
10(1)
1.7 Application of Computational Methods in Biomedical Engineering
10(12)
1.7.1 Fundamental Principles
11(6)
1.7.2 Finite Element Method
17(3)
1.7.3 Boundary Element Method
20(2)
1.7 A Finite Differences Method
22(1)
1.8 Challenges
23(6)
References
24(5)
2 Biomedical Imaging
29(36)
2.1 Introduction
29(1)
2.2 X-ray Radiography
29(4)
2.2.1 X-ray Interaction with Tissues
31(1)
2.2.2 Medical Applications of X-rays
32(1)
2.3 Computed Tomography
33(6)
2.3.1 The Principle of CT Imaging
33(2)
2.3.2 The Evolution of CT Scanners
35(2)
2.3.3 Medical Applications of CT Imaging
37(1)
2.3.3.1 Application of CT Imaging in Cancer
37(1)
2.3.3.2 Application of CT Imaging in Lungs
37(1)
2.3.3.3 Application of CT Imaging in Cardiovascular Disease
38(1)
2.3.3.4 Application of CT Imaging in Other Fields
38(1)
2.3.4 Radiation of CT Imaging
39(1)
2.4 Diagnostic Ultrasound
39(3)
2.4.1 The Principle of US
40(1)
2.4.2 Medical Applications of US
41(1)
2.5 Magnetic Resonance Imaging
42(3)
2.5.1 MRI Principle
43(1)
2.5.2 Medical Applications of MRI
44(1)
2.6 Positron Emission Tomography (PET)
45(3)
2.6.1 The Principle of PET
46(1)
2.6.2 Medical Applications of PET
47(1)
2.7 Single Photon Emission Computed Tomography
48(2)
2.7.1 The Principle of SPECT
49(1)
2.7.2 Medical Applications of SPECT
50(1)
2.8 Endoscopy
50(2)
2.8.1 Medical Applications of Endoscopy
52(1)
2.9 Elastography
52(3)
2.9.1 Elastographic Techniques
52(2)
2.9.2 Elastographic Medical Applications
54(1)
2.10 Conclusions and Future Trends
55(10)
References
57(8)
3 Computational Modeling at Molecular Level
65(26)
3.1 Introduction
65(2)
3.2 Introduction to Molecular Mechanics
67(4)
3.2.1 Chemical Formulas
67(1)
3.2.2 Molecular Structure and Polarity
68(2)
3.2.2.1 Mathematical Modeling of Polarizing Biochemical Systems
70(1)
3.3 Molecular Bioengineering in Areas Critical to Human Health
71(20)
3.3.1 Cell Biology
72(1)
3.3.1.1 Biology of Growth Factor Systems
73(2)
3.3.2 Diagnostic Medicine
75(1)
3.3.2.1 Lab-on-a-Chip Devices
75(1)
3.3.2.2 Biosensors
76(2)
3.3.3 Preventive Medicine
78(2)
3.3.4 Therapeutic Medicine
80(1)
3.3.4.1 Drug Delivery
80(2)
3.3.4.2 Tissue Engineering
82(3)
References
85(6)
4 Computational Modeling at Cell Level
91(26)
4.1 Introduction
91(2)
4.2 Introduction to Cell Mechanics
93(5)
4.2.1 Cell Material Properties
94(1)
4.2.2 Cell Composition and Structure
95(3)
4.3 Cellular Bioengineering in Areas Critical to Human Health
98(19)
4.3.1 Biology
99(2)
4.3.2 Diagnostic Medicine
101(1)
4.3.2.1 Organ Chip Technology
101(2)
4.3.2.2 Mechanosensors
103(1)
4.3.3 Therapeutic Medicine
104(1)
4.3.3.1 Drug Delivery
105(2)
4.3.3.2 Tissue Engineering
107(2)
4.3.4 P4 Medicine
109(1)
References
110(7)
5 Computational Modeling at Tissue Level
117(36)
5.1 Introduction
117(3)
5.2 Epithelial Tissue
120(3)
5.2.1 Composition and Properties of Epithelial Tissue
120(1)
5.2.2 Computational Modeling of Epithelial Tissue
121(2)
5.3 Connective Tissue
123(7)
5.3.1 Composition and Properties of Connective Tissue
123(4)
5.3.2 Computational Modeling of Connective Tissue
127(3)
5.4 Muscle Tissue
130(10)
5.4.1 Composition and Properties of Muscle Tissue
130(4)
5.4.2 Computational Modeling of Muscle Tissue
134(1)
5.4.2.1 Computational Modeling of Skeletal Muscle Tissue
134(3)
5.4.2.2 Computational Modeling of Smooth Muscle Tissue
137(1)
5.4.2.3 Computational Modeling of Cardiac Muscle Tissue
138(1)
5.4.2.4 Musculotendon Models
139(1)
5.5 Nervous Tissue
140(7)
5.5.1 Computational Modeling of Brain Tissue
141(3)
5.5.2 Computational Modeling of the Spinal Cord Tissue
144(2)
5.5.3 Computational Modeling of Peripheral Nerves
146(1)
5.6 Conclusion
147(6)
References
147(6)
6 Macroscale Modeling at the Organ Level
153(42)
6.1 Introduction
153(1)
6.2 The Respiratory System
154(3)
6.2.1 Computational Modeling of the Respiratory System
155(2)
6.3 The Digestive System
157(4)
6.3.1 Computational Modeling of the Digestive System
159(2)
6.4 The Cardiovascular System
161(2)
6.4.1 Computational Modeling of the Cardiovascular System
161(2)
6.5 The Urinary System
163(3)
6.5.1 Computational Modeling of the Urinary System
163(3)
6.6 The Integumentary System
166(4)
6.6.1 Computational Modeling of the Integumentary System
167(3)
6.7 The Musculoskeletal System
170(4)
6.7.1 Introduction to the Skeletal System
170(1)
6.7.2 Introduction to the Muscular System
171(1)
6.7.3 Computational Modeling of the Muscular-Skeletal System
172(2)
6.8 The Endocrine System
174(2)
6.8.1 Computational Modeling of the Endocrine System
174(2)
6.9 The Lymphatic System
176(4)
6.9.1 Computational Modeling of the Lymphatic System
177(3)
6.10 The Nervous System
180(3)
6.10.1 Computational Modeling of the Nervous System
180(3)
6.11 The Reproductive System
183(3)
6.11.1 Computational Modeling of the Reproductive System
184(2)
6.12 Conclusion
186(9)
References
186(9)
7 Mechanotransductlon Perspective, Recent Progress and Future Challenges
195(30)
7.1 Introduction
195(1)
7.2 Methods for Studying Mechanotransduction
196(2)
7.2.1 How Mechanical Forces Are Detected
196(1)
7.2.2 Transmission of Mechanical Forces
197(1)
7.2.3 Conversion of Mechanical Forces to Signals
197(1)
7.3 Mathematical Models of Mechanotransduction
198(16)
7.3.1 ODE Based Computational Model
198(3)
7.3.2 PDE Based Computational Model
201(4)
7.3.2.1 Mechanical Factors that Affect Cell Differentiation and Proliferation
205(2)
7.3.2.2 A Case Example of Multi-Scale Modeling Cell Differentiation and Proliferation
207(4)
7.3.3 Methodology of a Hybrid Multi-Scale Approach
211(1)
7.3.3.1 The Agent-Based Model (ABM)
211(2)
7.3.3.2 Mechanical Model
213(1)
7.4 Challenges
214(11)
References
218(7)
8 Multiscale Modeling of the Musculoskeletal System
225(46)
8.1 Introduction
225(1)
8.2 Structure of the Musculoskeletal System
225(8)
8.2.1 Structure of the Skeletal System Components
225(5)
8.2.2 Structure of the Muscular System Components
230(3)
8.3 Elasticity
233(8)
8.4 Mechanical Characteristics of Muscles
241(2)
8.5 Multiscale Modeling Approaches of the Musculoskeletal System
243(21)
8.5.1 Multiscale Modeling of Bones
243(11)
8.5.2 Multiscale Modeling of Articular Cartilage
254(2)
8.5.3 Multiscale Modeling of Tendons and Ligaments
256(1)
8.5.3.1 Advances in Multiscale Modeling of Tendons
256(2)
8.5.3.2 Advances in Multiscale Modeling of Ligaments
258(2)
8.5.4 Multiscale Modeling of the Skeletal Muscle
260(2)
8.5.5 Multiscale Modeling of the Smooth Muscle
262(2)
8.6 Conclusion
264(7)
References
264(7)
9 Multiscale Modeling of Cardiovascular System
271(32)
9.1 Introduction
271(1)
9.2 Cardiovascular Mechanics
272(23)
9.2.1 Visualization of the Cardiovascular System and 3D Arterial Reconstruction
272(1)
9.2.2 Blood Flow Modeling
273(1)
9.2.2.1 Steady and Pulsatile Flow of Blood
274(1)
9.2.2.2 Computational Fluid Dynamics Modeling
275(1)
9.2.2.3 Newtonian and Non-Newtonian Behavior of Blood
276(6)
9.2.3 Plaque Growth Modeling
282(4)
9.2.4 Agent-Based Modeling
286(2)
9.2.4.1 Key Components of Agent-Based Modelling
288(1)
9.2.4.2 Agent-Based Modelling and Simulation Approach
289(1)
9.2.4.3 Problem Definition
289(1)
9.2.4.4 ABM Applications in Cardiovascular Systems
290(2)
9.2.5 Discrete Particle Dynamics
292(1)
9.2.6 Multiscale Model of Drug Delivery/Restenosis
293(1)
9.2.6.1 Benefits of Multiscale Model of Drug Delivery/Restenosis
294(1)
9.3 Conclusions
295(8)
References
296(7)
10 Risk Prediction
303(28)
10.1 Introduction
303(1)
10.2 Medical Data Preprocessing
304(3)
10.2.1 Data Sharing
304(1)
10.2.2 Data Harmonization
305(2)
10.3 Machine Learning and Data Mining
307(7)
10.3.1 Supervised Learning Algorithms
309(1)
10.3.1.1 Regression Analysis
309(1)
10.3.1.2 Support Vector Machines
309(1)
10.3.1.3 Naive Bayes
310(1)
10.3.1.4 Decision Trees
311(1)
10.3.1.5 Ensemble Classifiers
312(1)
10.3.1.6 Artificial Neural Networks
312(1)
10.3.1.7 K-Means
313(1)
10.3.1.8 Spectral Clustering
313(1)
10.3.1.9 Hierarchical Clustering
314(1)
10.4 Explainable Machine Learning
314(3)
10.4.1 Transparency
314(1)
10.4.2 Evaluation and Types of Explanation
315(2)
10.5 Example of Predictive Models in Cardiovascular Disease
317(5)
10.6 Conclusion
322(9)
References
322(9)
11 Future Trends
331(19)
11.1 Virtual Populations
331(10)
11.1.1 Methods for Virtual Population Generation
332(5)
11.1.2 A Methodological Approach for a Virtual Population
337(1)
11.1.2.1 Multivariate Log-Normal Distribution (log-MVND)
337(1)
11.1.2.2 Supervised Tree Ensembles
337(1)
11.1.2.3 Unsupervised Tree Ensembles
338(1)
11.1.2.4 Radial Basis Function-Based Artificial Neural Networks
338(1)
11.1.2.5 Bayesian Networks
338(1)
11.1.2.6 Performance Evaluation of the Quality of the Generated Virtual Patient Data
339(1)
11.1.3 A Novel Approach for a Virtual Population Combining Multiscale Modeling
339(2)
11.2 Digital Twins
341(6)
11.2.1 Ecosystem of the Digital Twin for Health
342(1)
11.2.2 An Example Workflow of a Digital Twin
342(5)
11.3 Integrating Multiscale Modeling and Machine Learning
347(2)
11.3.1 Physics-Informed NN (PINN)
348(1)
11.3.2 Deep NN Algorithms Inspired by Statistical Physics and Information Theory
349(1)
11.4 Conclusion and Future Trends
349(1)
References 350(5)
Index 355
Dimitrios I. Fotiadis, PhD, is Editor of IEEE Journal of Biomedical and Health Informatics. He is Professor of Biomedical Engineering in the Department of Materials Science and Engineering, University of Ioannina in Greece. He is also a Researcher at the Institute of Molecular Biology and Biotechnology - FORTH, Greece.

Antonis I. Sakellarios, PhD, is a Research Associate at the Unit of Medical Technology and Intelligent Information Systems at the Department of Materials Science and Engineering of the University of Ioannina and Researcher at the Institute of Molecular Biology and Biotechnology - FORTH, Greece.

Vassiliki T. Potsika, PhD, is Managing Editor of the IEEE Journal of Biomedical and Health Informatics and Senior Researcher at the Unit of Medical Technology and Intelligent Information Systems at the University of Ioannina.