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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: IEEE Press Series on Biomedical Engineering
- Sprache: Englisch
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.
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Author Biographies
Preface
List of Abbreviations
List of Terms
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
1.6 The Use of Systems Biology and Multiscale Modeling in Biomedical and Medical Science
1.7 Application of Computational Methods in Biomedical Engineering
1.8 Challenges
References
2 Biomedical Imaging
2.1 Introduction
2.2 X‐ray Radiography
2.3 Computed Tomography
2.4 Diagnostic Ultrasound
2.5 Magnetic Resonance Imaging
2.6 Positron Emission Tomography (PET)
2.7 Single Photon Emission Computed Tomography
2.8 Endoscopy
2.9 Elastography
2.10 Conclusions and Future Trends
References
3 Computational Modeling at Molecular Level
3.1 Introduction
3.2 Introduction to Molecular Mechanics
3.3 Molecular Bioengineering in Areas Critical to Human Health
References
4 Computational Modeling at Cell Level
4.1 Introduction
4.2 Introduction to Cell Mechanics
4.3 Cellular Bioengineering in Areas Critical to Human Health
References
5 Computational Modeling at Tissue Level
5.1 Introduction
5.2 Epithelial Tissue
5.3 Connective Tissue
5.4 Muscle Tissue
5.5 Nervous Tissue
5.6 Conclusion
References
6 Macroscale Modeling at the Organ Level
6.1 Introduction
6.2 The Respiratory System
6.3 The Digestive System
6.4 The Cardiovascular System
6.5 The Urinary System
6.6 The Integumentary System
6.7 The Musculoskeletal System
6.8 The Endocrine System
6.9 The Lymphatic System
6.10 The Nervous System
6.11 The Reproductive System
6.12 Conclusion
References
7 Mechanotransduction Perspective, Recent Progress and Future Challenges
7.1 Introduction
7.2 Methods for Studying Mechanotransduction
7.3 Mathematical Models of Mechanotransduction
7.4 Challenges
References
8 Multiscale Modeling of the Musculoskeletal System
8.1 Introduction
8.2 Structure of the Musculoskeletal System
8.3 Elasticity
8.4 Mechanical Characteristics of Muscles
8.5 Multiscale Modeling Approaches of the Musculoskeletal System
8.6 Conclusion
References
9 Multiscale Modeling of Cardiovascular System
9.1 Introduction
9.2 Cardiovascular Mechanics
9.3 Conclusions
References
10 Risk Prediction
10.1 Introduction
10.2 Medical Data Preprocessing
10.3 Machine Learning and Data Mining
10.4 Explainable Machine Learning
10.5 Example of Predictive Models in Cardiovascular Disease
10.6 Conclusion
References
11 Future Trends
11.1 Virtual Populations
11.2 Digital Twins
11.3 Integrating Multiscale Modeling and Machine Learning
11.4 Conclusion and Future Trends
References
Index
IEEE Press Series in Biomedical Engineering
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Modeling approaches and typical examples per scale in systems bio...
Chapter 2
Table 2.1 The types of endoscopy.
Table 2.2 Comparison of the main medical imaging modalities.
Chapter 4
Table 4.1 Difference between prokaryotic and eukaryotic cells.
List of Illustrations
Chapter 1
Figure 1.1 The iterative cycle of wet and dry laboratory research.
Figure 1.2 The space scales in biological systems.
Figure 1.3 The relation of the modeling and experimental approach according ...
Figure 1.4 Schematic illustration of the biological levels of significant re...
Figure 1.5 Characteristics of system models.
Figure 1.6 The solution of problems in biomedical engineering using FDM, FEM...
Figure 1.7 The main stages of computer‐based simulations.
Figure 1.8 Conceptual map of modeling techniques divided into continuous and...
Figure 1.9 Indicative scheme of the application of the Finite Element Method...
Figure 1.10 Different types of elements for discretization using the Finite ...
Figure 1.11 Typical application of the Boundary Element Method.
Figure 1.12 Challenges in multiscale methods in systems biology.
Chapter 2
Figure 2.1 Simplified representation of X‐ray imaging.
Figure 2.2 The principles of CT imaging.
Figure 2.3 The four historical generations of X‐ray CT: (a and b) Translatio...
Chapter 3
Figure 3.1 (a) Levels of structural organization in the human body, (b) a mo...
Figure 3.2 The bond distance and angle for the formaldehyde molecule (H
2
CO)....
Figure 3.3 Cellular and molecular bioengineering role in areas critical to h...
Figure 3.4 Configuration of a biosensor showing biorecognition, interface, a...
Figure 3.5 Disease‐specific models at the center of data integration. Differ...
Figure 3.6 Computer‐aided tissue engineering.
Chapter 4
Figure 4.1 Elastic modulus of eukaryotic cells and comparison with other ent...
Figure 4.2 Typical structure of eukaryotic cells.
Figure 4.3 Cellular and Molecular Bioengineering role in areas critical to h...
Figure 4.4 A timeline showing the development of different cancer organ chip...
Figure 4.5 (a) A three‐compartment model of drug distribution, (b) a represe...
Chapter 5
Figure 5.1 The four types of human tissues: epithelial, connective, muscle, ...
Figure 5.2 The four categories of epithelial tissues: (a) squamous, (b) stra...
Figure 5.3 The three main categories of connective tissues: (a) loose, (b) d...
Figure 5.4 The three main categories of muscle tissues: (a) skeletal, (b) sm...
Figure 5.5 (a) A passive length‐tension curve for muscle (
L
1
is the length a...
Chapter 7
Figure 7.1 (a)–(f) Multistability in the MSC differentiation network. The re...
Figure 7.2 The geometry used in the presented model.
Figure 7.3 Predicted spatiotemporal evolution of fibrous tissue, cartilage b...
Figure 7.4 (a) Spherical configuration of the cell in which sensing forces a...
Figure 7.5 Computational algorithm of cell mechano‐sensing and consequent ce...
Figure 7.6 A hybrid multi‐scale model of mechanotransduction combining an ag...
Chapter 8
Figure 8.1 The structure of a long bone.
Figure 8.2 The hierarchical structure of a typical long bone at various leng...
Figure 8.3 The hierarchical structure of skeletal muscle.
Figure 8.4 Load‐displacement curve, ultimate load, and rigidity.
Figure 8.5 Stress–strain curve for cortical and trabecular bone.
Figure 8.6 Force–velocity relationship of muscle.
Figure 8.7 Force–length relationship of human skeletal muscle.
Figure 8.8 General scheme applied in computational modeling of bone using th...
Figure 8.9 Computational model of the femur: (a and b) boundary conditions i...
Figure 8.10 Scanning acoustic microscopy images for: (a) week 2, (b) week 3,...
Figure 8.11 Predicted healing patterns when cells are immediately distribute...
Chapter 9
Figure 9.1 Blood flow and shear stress at the carotid bifurcation. Change in...
Figure 9.2 Case example of plaque growth modeling [22]. (a and b) show the r...
Chapter 10
Figure 10.1 A taxonomic view on XAI.
Chapter 11
Figure 11.1 ROC curves depicting the classification performance of the XGBoo...
Figure 11.2 Demo page of the cardiovascular virtual population.
Figure 11.3 Ecosystem of the Digital Twin for Health and Well‐being.
Figure 11.4 Proposed X73‐based Digital Twin (DT) System Architecture.
Figure 11.5 The two pillars of the digital twin, mechanistic and statistical...
Figure 11.6 Clinical workflow using the fully developed digital twin concept...
Guide
Cover Page
Series Page
Title Page
Copyright Page
Author Biographies
Preface
List of Abbreviations
List of Terms
Table of Contents
Begin Reading
Index
IEEE Press Series in Biomedical Engineering
Wiley End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial Board Sarah Spurgeon, Editor in Chief
Jón Atli Benediktsson
Behzad Razavi
Jeffrey Reed
Anjan Bose
Jim Lyke
Diomidis Spinellis
James Duncan
Hai Li
Adam Drobot
Amin Moeness
Brian Johnson
Tom Robertazzi
Desineni Subbaram Naidu
Ahmet Murat Tekalp
Multiscale Modelling in Biomedical Engineering
Dimitrios I. Fotiadis
University of Ioannina, Ioannina, Greece
Antonis I. Sakellarios
University of Ioannina, Ioannina, Greece
Vassiliki T. Potsika
University of Ioannina, Ioannina, Greece
Copyright © 2023 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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Author Biographies
Dimitrios I. Fotiadis is a professor of biomedical engineering at the Department of Materials Science and Engineering at the University of Ioannina, the director of the Unit of Medical Technology and Intelligent Information Systems, and an affiliated researcher at the Biomedical Research Institute – FORTH and the Michailideion Cardiology Center. He is the editor in chief of the IEEE Journal of Biomedical and Health Informatics. His research interests include multiscale modeling, biomedical informatics, medical and biological data engineering, wearable and implantable devices, and machine/deep learning.
Antonis I. Sakellarios is currently an associate researcher and adjunct lecturer in the Department of Materials Science and Engineering of University of Ioannina and in the Foundation for Research and Technology – Greece, Department of Biomedical Research. He has published over 45 journal articles, 100 conference papers, and 4 book chapters.
Vassiliki T. Potsika is a senior researcher at the Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Greece, and project manager for relevant R&D‐funded projects. She is the managing editor of IEEE Journal of Biomedical and Health Informatics. Her research interests include biomechanics, bone mechanics, ultrasonic evaluation of fractured and osteoporotic bones, wave scattering in composite materials, computational modeling of cardiovascular diseases, biomedical signal processing and rehabilitation engineering, and robotics.
Preface
Nowadays, multiscale modeling is considered a fundamental numerical modeling approach in several engineering disciplines such as materials science, biomedical engineering, and fluid mechanics. The application of numerical methods in biomedical research has increased tremendously in the last two decades due to the exponential growth in computer power and the evolution of imaging modalities. The aim of this book is to describe the basic engineering principles, and how multiscale modeling can enhance clinical practice, support clinical trials, improve patient treatment options, and thus ameliorate the quality of life by focusing on biological systems that are the most complex structures in scientific research. It provides fundamental knowledge from the molecular to the cellular functions, as well as at the tissue and organ levels. Considering the significant diversity of the topic, this book approaches the implementation of multiscale modeling to biological systems focusing on the musculoskeletal and cardiovascular systems. For a complete understanding of pathologies such as osteoporosis, fracture healing, and atherosclerosis, the musculoskeletal and cardiovascular systems should be considered as complex systems combining different modeling scales.
More specifically, Chapter 1 deals with the role of systems biology in biomedical engineering and presents the fundamental principles in multiscale modeling, as well as the most popular multiscale methods and approaches. Systems biology refers to biology at a global scale where biological functions are analyzed as a result of complex mechanisms that evolve at different scales, from the nanoscale (molecular) to macroscale (organ). This chapter explains how multiscale modeling and simulation can play a key role in the description, prediction, and better understanding of those mechanisms in a quantitative and integrative manner.
In Chapter 2, biomedical imaging principles and applications related to multiscale modeling are presented by providing in‐depth coverage from the engineering point of view. It presents the fundamentals and applications of primary medical imaging techniques, such as magnetic resonance imaging, ultrasound, nuclear medicine, X‐ray/computed tomography, and molecular imaging. Additionally, the physical principles, equipment design, data acquisition, image reconstruction techniques, and clinical applications of each modality are discussed. Recent hybrid developments such as multislice spiral computed tomography or fused and combined imaging techniques are also presented.
An introduction to molecular mechanics is presented in Chapter 3 and its fundamental principles are presented. Advanced mathematical modeling, simulation, and data analysis methods are described and applied to biological problems at the molecular level. The crucial role of molecular bioengineering in areas critical to human health is explained focusing on the effect of computational modeling at molecular level on cell biology, diagnostic medicine, preventive medicine, and therapeutic medicine.
Computational modeling at the cell level is presented in Chapter 4. An introduction to cell mechanics is presented and fundamental principles in cell material properties, composition, and structure are described. Cell mechanics is considered a research domain that plays a significant role in cellular and tissue biology, from tissue and organ development to wound healing and cancer cell metastasis and migration. To study the mechanical behavior of cells, computational models have been presented, which aim to explain experimental observations by providing a framework of underlying cellular mechanisms.
Chapter 5 deals with computational modeling at the tissue level. The mechanical and structural properties of the four main categories of human tissues are presented that are the epithelial, connective, muscle, and nervous tissues. Computational modeling approaches are presented as a promising means aiming to provide insight into the complex nature of the different tissue types and provide quantitative and qualitative information on tissue physics for clinical and scientific purposes.
Starting from the molecular level in Chapter 3, we move from the cellular and tissue levels in Chapters 4 and 5 to Chapter 6 describing the organ level. Each organ performs a particular function in the body and is made up of different tissues. The eleven distinct organ systems of humans are presented that form the basis of anatomy, and the implementation of computational modeling approaches is discussed as a means to provide supplementary information to experimental and clinical research.
Then, in Chapter 7, the mechanotransduction perspective, recent progress, and future challenges are described. Mechanotransduction provides a clear bridge between experimental results performed at separate scales. In particular, it involves quantitative measurements versus more qualitative approaches used by the “general” cell biology making connections that do not follow a simple linear chain of events. The effects of mechanotransduction include growth and remodeling, which are typically considered as unique processes, representing mass–volume changes due to bulk material deposition or resorption versus structural changes including trabecular or fiber realignment, respectively. The development of models of these processes requires both constitutive formulation and computational implementation of the constitutive model, typically within a finite volume of finite difference.
Chapters 8 and 9 focus on multiscale modeling approaches of the musculoskeletal and cardiovascular systems, respectively. Chapter 8 describes the main components of the musculoskeletal system and their hierarchical structure and presents cornerstone research in the area of computational modeling in the macro, micro, and nano scales. The basic principles of the theory of elasticity and mechanics of the soft tissues of the musculoskeletal system are presented as fundamental knowledge to establish a computational model. With the assistance of multidimensional computational models, the unknown mechanisms underlying pathologies of the musculoskeletal system can be tested and clinicians can conduct optimal treatment strategies.
Chapter 9 aims to explain how computational modeling has been used to understand the mechanisms of atherosclerosis and to provide insights into the atherosclerosis process in arteries and the development of de novo plaques. The current approaches to plaque growth modeling, as well as a new multilevel modeling approach, which is based on the major mechanisms of plaque growth, are presented. The model can be used as a decision support tool for the medical doctor or the researcher to predict regions that are prone to plaque development. Additionally, decision support for diagnosis is also provided by calculating noninvasively the fractional flow reserve (FFR) index. Finally, it is discussed how treatment decision support can be achieved by modeling the stent positioning and deployment in the arteries.
Chapter 10 presents risk prediction approaches in chronic diseases as a possible top level of a multiscale computational approach in the domain of predictive medicine. In recent years, we observe an increasing acquisition and accumulation of various types of medical data, which, however, have not been used successfully to fully understand the mechanisms or pathways of diseases. Data are also collected in Real World Settings, such as electronic health records (EHRs), wearable systems, and registries. Machine learning methods have been employed for the development of decision support systems to make prediction, risk assessment, diagnosis, prognosis, and treatment management, especially for chronic diseases. This top level of multiscale modeling could be the development of predictive models for the prediction of disease outcomes such as events or for patients' risk stratification.
Chapter 11 deals with future trends. It presents some future and prospective developments in biomedical engineering considering multiscale modeling. More and more data are collected and acquired generating large databases. However, accurate development of the multiscale models for personalized application requires additional data. Virtual populations are considered as a way to generate and use data for such purposes. Another future perspective that is discussed in this chapter is the use of digital twins in healthcare. Due to the increased use of machine learning and artificial intelligence approaches, the potential integration of multiscale modeling with machine learning is also presented.
This book is intended for pregraduate and postgraduate students, as well as for researchers in the domains of biology, biomechanics, biomedical engineering, biomedical informatics, materials science, and medicine. Our aim is twofold: (i) to establish a good background in the principles of mathematics, physics, and engineering, as well as biology and physiology and (ii) to provide knowledge on advanced computational approaches and the status of the current state of the art in multiscale modeling.
Antonis I. Sakellarios, Vassiliki T. Potsika, Dimitrios I. Fotiadis
Ioannina, Greece
List of Abbreviations
Abbreviation
Explanation
2D
Two‐dimensional
3D
Three‐dimensional
ABM
Agent‐based models
ACS
Activation cycle switch
ACW
Antral contraction waves
AIx
Augmentation index
ALE
Arbitrary Lagrange–Euler
ANN
Artificial neural network
AP
Action potential
BEM
Boundary element method
BMI
Body mass index
BMImean
Mean body mass index
BMP
Bone morphogenetic protein
BMU
Basic multicellular unit
CAD
Computer‐aided design
CFD
Computational fluid dynamics
CL
Culprit lesions
CMBE
Cellular and molecular bioengineering
CNS
Central nervous system
Creat
Creatinine
CSK
Actin cytoskeleton
CT
Computed tomography
CTCA
Computed tomography coronary angiography
CTDI
Computed tomography dose index
CV
Cardiovascular
CVD
Cardiovascular disease
DBP
Diastolic blood pressure
DE
Difference equations
DES
Drug‐eluting stent
Discrete event simulation
DMD
Duchenne muscular dystrophy
DNA
Deoxyribonucleic acid
DS
Dynamic system
DSM
Detrusor smooth muscle
DT
Digital twin
DXA
Dual‐energy X‐ray absorptiometry
ECG
Electrocardiogram
ECM
Extracellular matrix
EEG
Electroencephalography
EHR
Electronic health record
eNOS
Endothelial nitric oxide synthases
ESS
Endothelial shear stress
EVD
Eigenvalue decomposition
FCBF
Fast correlation‐based filter
FDG
Fluorescent analogue of glucose
FDM
Finite difference method
FE
Finite elements
FEM
Finite element method
FES
Functional electrical stimulation
FFNN
Feed‐forward neural network
FFR
Fractional flow reserve
FFRCT
Fractional flow reserve from computed tomography
FLAME
Flexible large agent‐based modeling environment
fMRI
Functional magnetic resonance imaging
FNNs
Feedforward neural networks
FRPVE
Fiber‐reinforced poro‐viscoelastic
FSI
Fluid–structure interaction
FVM
Finite volume method
GAN
Generative adversarial network
GFR
Growth factor receptor
GLS
Generalized least squares
Glyc
Glycemia
GOF
Goodness of fit
HDL
High‐density lipoprotein
Hmean
Mean body height
ICA
Angiography
IMAG
Interagency modeling and analysis group
ISR
In‐stent restenosis
IVUS
Intravascular ultrasound
KL
Kullback–Leibler
LDL
Low‐density lipoprotein
LINC
Linker of nucleoskeleton and cytoskeleton
LLLT
Low‐level light therapy
LOC
Lab‐on‐a‐chip
LV
Linear viscoelastic
MAPEL
Mechanistic axes population ensemble linkage
MCP‐1
Monocyte chemoattractant protein
Mech‐ABM
Mechano‐agent‐based model
MIF
Migration inhibitory factor
ML
Machine learning
MMP
Matrix metalloproteinase
MPR
Multiplanar reformations
MRA
Magnetic resonance angiography
MRI
Magnetic resonance ιmaging
MSC
Mesenchymal stem cell
MSCT
Multislice CT scanners
MVND
Multivariate normal distributions
NLV
Nonlinear viscoelastic
NMR
Nuclear magnetic resonance
NN
Neural network
OCT
Optical coherence tomography
OLS
Ordinary least squares
PAC
Pia‐arachnoid complex
PCT
Perfusion CT
PDEs
Partial differential equations
PDGF
Platelet‐derived growth factor
PDMS
Polydimethylsiloxane
PET
Positron emission tomography
PK/PD
Pharmacokinetic/Pharmacodynamics
PNS
Peripheral nervous system
POC
Point‐of‐care
PP
Pulse pressure
pQCT
peripheral quantitative computed tomography
PyEL
Python edge loading
QCT
Quantitative computed tomography
QLV
Quasi‐linear viscoelastic
RDE
Reaction‐diffusion equation
RFA
Radiofrequency ablation
RLS
Recursive least squares
RP
Rapid prototyping
sAPs
spontaneously evoked action potentials
SBP
Systolic blood pressure
SCI
Spinal cord injury
SCPC
Spinal‐cord‐pia‐arachnoid construct
SMC
Smooth muscle cell
SPECT
Single photon emission computed tomography
STN
Signal transduction network
SVM
Support vector machines
TAZ
Transcriptional co‐activator
TBI
Traumatic brain injury
TC
Total cholesterol
TIMP
Tissue inhibitor of metalloproteinase
tNIRS
transcranial near‐infrared stimulation
TRH
Thyrotropin‐releasing hormone
TSH
Thyroid‐stimulating hormone
US
Ultrasound
VEGFR
Vascular endothelial growth factor and its receptor
VSEPR
Valence shell electron pair repulsion
WLS
Weighted least squares
XAI
Explainable artificial intelligence
YAP
Yes‐associated protein
List of Terms
Biological systems
It is a complex network that connects several biologically relevant entities. Biological organization spans several scales and is determined based on different structures depending on what the system is.
Bone remodeling
Bone remodeling (or bone metabolism) is a lifelong process where mature bone tissue is removed from the skeleton (a process called bone resorption) and new bone tissue is formed (a process called ossification or new bone formation). These processes control the reshaping or replacement of bone following injuries like fractures and also microdamage, which occurs during normal activity. Remodeling also responds to functional demands of mechanical loading.
Cell differentiation
It is the process by which dividing cells change their functional or phenotypical type. All cells presumably derive from stem cells and obtain their functions as they mature.
Cell migration
It is a central process in the development and maintenance of multicellular organisms. Tissue formation during embryonic development, wound healing, and immune responses all require the orchestrated movement of cells in particular directions to specific locations. Cells often migrate in response to specific external signals, including chemical signals and mechanical signals.
Cell proliferation
It is the process by which a cell grows and divides to produce two daughter cells. Cell proliferation leads to an exponential increase in cell number and is therefore a rapid mechanism of tissue growth. Cell proliferation requires both cell growth and cell division to occur at the same time, such that the average size of cells remains constant in the population. Cell division can occur without cell growth, producing many progressively smaller cells (as in cleavage of the zygote), while cell growth can occur without cell division to produce a single larger cell (as in growth of neurons).
Chemical bonds
They hold molecules together and create temporary connections that are essential to life. Types of chemical bonds include covalent, ionic, and hydrogen bonds and London dispersion forces.
Computed tomography
It combines a series of X‐ray images taken from different angles around your body and uses computer processing to create cross‐sectional images (slices) of the bones, blood vessels, and soft tissues inside your body.
Drug delivery
It refers to approaches, formulations, manufacturing techniques, storage systems, and technologies involved in transporting a pharmaceutical compound to its target site to achieve a desired therapeutic effect.
Enzyme
It is a biological catalyst and is almost always a protein. It speeds up the rate of a specific chemical reaction in the cell.
Flexible large agent‐based modeling environment
FLAME (flexible large‐scale agent‐based modeling environment) is a very general system for building detailed agent‐based models that generate highly efficient simulation software that can run on any computing platform.
Gene expression
It is the process by which the information encoded in a gene is used either to make RNA molecules that code for proteins or to make noncoding RNA molecules that serve other functions.
Gene networks
A gene regulatory network is a set of genes, or parts of genes, that interact with each other to control a specific cell function. Gene regulatory networks are important in development, differentiation, and responding to environmental cues.
General Data Protection Regulation (GDPR)
It is a regulation in EU law on data protection and privacy in the European Union (EU) and the European Economic Area (EEA). The GDPR is an important component of EU privacy law and of human rights law, in particular Article 8(1) of the Charter of Fundamental Rights of the European Union.
Hardness
It is the resistance of a material to localized plastic deformation.
Health Insurance Portability and Accountability (HIPPA)
The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law in the United States that required the creation of national standards to protect sensitive patient health information from being disclosed without the patient's consent or knowledge. The US Department of Health and Human Services (HHS) issued the HIPAA Privacy Rule to implement the requirements of HIPAA. The HIPAA Security Rule protects a subset of information covered by the Privacy Rule.
Heat conduction
It is the transfer of internal thermal energy by the collisions of microscopic particles and movement of electrons within a body. The microscopic particles in heat conduction can be molecules, atoms, and electrons. Internal energy includes kinematic and potential energy of microscopic particles.
Hill equation
In biochemistry and pharmacology, the Hill equation refers to two closely related equations that reflect the binding of ligands to macromolecules, as a function of the ligand concentration.
Hodgkin–Huxley model
The Hodgkin–Huxley model, or conductance‐based model, is a mathematical model that describes how action potentials in neurons are initiated and propagated.
Homogenization model
The numerical homogenization method accurately considers the geometry and spatial distribution of the phases and also precisely estimates the propagation of damage to accurately predict the failure strength.
Hyperelasticity
They exhibit highly nonlinear elastic response when subjected to very large strains.
In vitro
In vitro
(meaning in glass, or in the glass) studies are performed with microorganisms, cells, or biological molecules outside their normal biological context.
In vivo
Studies that are
in vivo
(Latin for “within the living,” often not italicized in English) are those in which the effects of various biological entities are tested on whole, living organisms or cells, usually animals, including humans and plants, as opposed to a tissue extract or dead organism.
Implant
It is a medical device manufactured to replace a missing biological structure, support a damaged biological structure, or enhance an existing biological structure. Medical implants are human‐made devices, in contrast to a transplant, which is a transplanted biomedical tissue. The surface of implants that contacts the body might be made of a biomedical material such as titanium, silicone, or apatite depending on what is the most functional. In some cases, implants contain electronics, e.g. artificial pacemaker and cochlear implants. Some implants are bioactive, such as subcutaneous drug delivery devices in the form of implantable pills or drug‐eluting stents.
In silico
In biology and other experimental sciences, an
in silico
experiment is one performed on a computer or via computer simulation.
Isotropic material
Isotropic materials are materials whose properties remain the same when tested in different directions. Isotropic materials differ from anisotropic materials, which display varying properties when tested in different directions. Common isotropic materials include glass, plastics, and metals.
Lab on a chip
It is a device that integrates one or several laboratory functions on a single integrated circuit (commonly called a “chip”) of only millimeters to a few square centimeters to achieve automation and high‐throughput screening.
Lewis structures
These are also called Lewis dot diagrams, electron dot structures, or Lewis electron dot structures, which represent atoms and their positions in the molecular structure with their chemical symbols.
Lipidomics
It is the study of reaction pathways involved in lipid metabolism within biological systems.
Magnetic resonance imaging
It is a medical imaging technique that uses a magnetic field and computer‐generated radio waves to create detailed images of the organs and tissues in your body.
Mechanosensors
It is a sensory neuron that responds to mechanical stimuli.
Metabolomics
It is the large‐scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues, or organisms. Collectively, these small molecules and their interactions within a biological system are known as the metabolome.
Microfluidics
It is both the science that studies the behavior of fluids through microchannels and the technology of manufacturing microminiaturized devices containing chambers and tunnels through which fluids flow or are confined. Microfluidics deal with very small volumes of fluids, down to femtoliters (fL), which is a quadrillionth of a liter.
Molecular bonds
A molecular or covalent bond is formed when atoms bond by sharing pairs of electrons.
Molecular structure
It describes the location of the atoms, not the electrons. We differentiate between these two situations by naming the geometry that includes all electron pairs and the electron‐pair geometry. The structure that includes only the placement of the atoms in the molecule is called the molecular structure.
Nanotechnology
It is the manipulation of matter on a near‐atomic scale to produce new structures, materials, and devices. The technology promises scientific advancement in many sectors such as medicine, consumer products, energy, materials, and manufacturing.
Non‐Newtonian fluid
A non‐Newtonian fluid is a fluid whose flow (viscosity) properties differ from those of Newtonian fluids, described by Sir Isaac Newton. Non‐Newtonian fluids have viscosities that change according to the amount of force that is applied to the fluid. The viscosity changes as the force applied changes.
Orthotropic material
In materials science and solid mechanics, orthotropic materials have material properties at a particular point, which differ along three orthogonal axes, where each axis has twofold rotational symmetry. These directional differences in strength can be quantified with Hankinson's equation.
Pharmacodynamics
It is the study of how the drug affects the organism.
Pharmacokinetics
It is the study of how an organism affects a drug.
Photoelectric effect
It is the emission of electrons when electromagnetic radiation, such as light, hits a material.
Photon
A photon (from Ancient Greek ϕς, ϕωτός (phôs, phōtós) “light”) is an elementary particle that is a quantum of the electromagnetic field, including electromagnetic radiation such as light and radio waves, and the force carrier for the electromagnetic force.
Polarity
It is, in chemical bonding, the distribution of electrical charge over the atoms joined by the bond.
Poroelastic medium
It is a field in materials science and mechanics that studies the interaction between fluid flow and solid's deformation within a linear porous medium, and it is an extension of elasticity and porous medium flow (diffusion equation). The deformation of the medium influences the flow of the fluid and vice versa.
Porosity
It is a measure of the void (i.e. empty) spaces in a material and is a fraction of the volume of voids over the total volume, between 0 and 1, or as a percentage between 0% and 100%.
Positron emission tomography
It is an applied method of medical imaging based on two fundamental natural phenomena: (i) the phenomenon of radioactivity β
+
, and (ii) the phenomenon of positron‐electron annihilation.
Protein activity
A critical function of proteins is their activity as enzymes, which are needed to catalyze almost all biological reactions.
Proteomics
It is the systematic, large‐scale analysis of proteins. It is based on the concept of the proteome as a complete set of proteins produced by a given cell or organism under a defined set of conditions.
Pseudoplastic fluid
It is a fluid that increases viscosity as force is applied. A typical example is a suspension of cornstarch in water with a concentration of one to one. This cornstarch behaves like water when no force is applied; however, it is solidified as force is applied.
Radiation
It is energy that comes from a source and travels through space at the speed of light. This energy has an electric field and a magnetic field associated with it and has wave‐like properties. You could also call radiation “electromagnetic waves.”
Reynolds number
It is the ratio of inertial forces to viscous forces within a fluid that is subjected to relative internal movement due to different fluid velocities. A region where these forces change behavior is known as a boundary layer, such as the bounding surface in the interior of a pipe.
Single photon emission computed tomography
It is a nuclear medicine tomographic imaging technique using gamma rays.
Stent
It is a tiny tube that your doctor can insert into a blocked passageway to keep it open. The stent restores the flow of blood or other fluids, depending on where it is placed.
Stiffness
It is defined as the resistance to a force causing a member to bend.
Strain
Proportional deformation.
Stress
Force per unit area.
Transcriptomics
It is the study of the transcriptome – the complete set of RNA transcripts that are produced by the genome, under specific circumstances or in a specific cell – using high‐throughput methods, such as microarray analysis.
Ultrasonic waves
Ultrasonic waves are sound waves whose frequencies are higher than those of waves normally audible to the human ear. The angular frequencies of the ultrasonic waves produced in laboratories lie from about 10
5
s
−1
to about 3 × 10
9
s
−1
, the former value representing the limit of audibility of the human ear.
Ultrasound imaging
It uses high‐frequency sound waves to view inside the body.
Viscoelastic materials
They combine two different properties. The term “viscous” implies that they deform slowly when exposed to an external force. The term “elastic” implies that once a deforming force has been removed the material will return to its original configuration.
Viscosity
It is a measure of its resistance to deformation at a given rate.
Weibel model
It is a model of the respiratory airway tree. In it, each parent airway, starting with the trachea, splits into two daughter airways. Based on measurements from several cadavers, characteristic branching angles, airway diameters, and lengths for the different airway generations are prescribed.
Windkessel models
The four elements of the Windkessel models are aortic compliance, aortic impedance (resistance and inductance of the aorta), resistance of the peripheral arteries, and compliance of the peripheral arteries.
Wound healing
It is a complex biological process that consists of hemostasis, inflammation, proliferation, and remodeling. Large numbers of cell types – including neutrophils, macrophages, lymphocytes, keratinocytes, fibroblasts, and endothelial cells – are involved in this process.
X‐ray radiography
It uses a very small dose of ionizing radiation to produce pictures of the body's internal structures. X‐rays are the oldest and most frequently used form of medical imaging. They are often used to help diagnose fractured bones, look for injury or infection, and to locate foreign objects in soft tissue.
Young's modulus
It is a measure of the ability of a material to withstand changes in length when under lengthwise tension or compression. Sometimes referred to as the modulus of elasticity, Young's modulus is equal to the longitudinal stress divided by the strain.
1Systems Biology and Multiscale Modeling
1.1 Introduction
The biological systems are characterized by a significant complexity ideally described using networks and pathways as well as their potential interconnections between their parts or with other external systems. They present temporal and spatial dimensions, which enable the definition of the system's evolution, growth, and development. Furthermore, the biological systems consist of several scales of representation starting from the cell level (e.g. gene pathways, molecular pathways, protein translation, and function) to higher levels such as tissue or organ level. The modeling of such systems is based on the proper definition of the outcomes and goals and according to them on the selection of the necessary subsets of features, which consist of the specific component parts which are necessary for the modeling goals' achievement. Then, the model of the system comprises the set of features and processes requiring some inputs and providing some outputs. First, the inputs are possible external “forces” which affect our set of features or even noise, which does not affect it. Outputs are the responses of the set of features and processes to the input stimuli, as this is observed from outside of the system.
The usual representation of a system model is provided by a mathematical model, which is defined by one or more mathematical equations and the necessary operations among them. The major advantage of using mathematical formulation for the representation of a biological system is that it is based on mathematical theorems and laws, which enable the implementation of simple as well as highly complex models empowering the proper evaluation of our hypothesis. Usually, differential equations are used for the development of biological mathematical models, but in other cases, simpler ones based on algebra or geometry models can be employed. After the definition of a mathematical model, which describes a biological system, it is possible to perform the computational simulation. This means that the computational model simulates the biological system and its functions as those have been defined by the mathematical equations. Besides the computational models, other approaches in biological systems are: the stochastic models, which employ probabilistic mathematical laws and deterministic models whose components, mathematical variables, and parameters are represented by symbols with unique and not random values. The deterministic models are used to present the relationships between large numbers of entities, such as the molecules. Finally, the compartment models are distinguished by discrete boundaries between components called compartments.
1.2 Systems Biology
Systems biology is an evolving research field with many definitions, though all of them encompass a biological system, which describes some conditions, pathways, or mechanisms studied at one or more scales. The scales may have two orientations: (i) the first one is the categorization according to the organism (from molecular to organismic scale), and (ii) the second is the temporal scale (e.g. from nanoseconds to years) meaning the time scale under which the process is performed. Additionally, all definitions of systems biology conclude with the same aim: a better description and understanding of the biological system as a whole. Although the trend in computational biology is to employ systems biology for the description of a biological system, the concept is not new. On the other hand, it is being used and implemented for over a century. Schmitt and Schmitt [1] and Rashevsky [2] attempted to describe biological systems using mathematics and physics. Initially, the concepts were applied to neurophysiological systems [3]. The developed models were at organ‐system level and they were considered as systems physiology models, while independent studies presenting systems pharmacology models were also reported. Pharmacokinetics and pharmacodynamics were modeled at all levels from cells to tissues and organs in order to describe the interaction of the body with the drug as well as its consequent effect on the body.
After the 1990s, an acceleration of big data collection has been observed meaning that omics types of data are collected. Such data include transcriptomics, lipidomics, proteomics, and metabolomics and they provide the opportunity to develop systems biology models at the microscopic level usually called molecular systems biology. In that case, the interaction between different components and molecules is usually described by creating dynamical networks and pathways. The pathways and networks provide an overall perspective of the mechanism and can describe all the interactions between these molecules as well as the consequences in the case of external factors and forces such as a mutation of a gene and the infection from a pathogenic organism. The first studies, which were based on omics data used available algorithms and methodologies for the development of systems biology models. In most cases, these approaches were adequate, especially in the case of simple systems or in the case of modeling one level or at least for the interaction between two scales.
The collection of big data leads to the development of models in the field of genetics. Computational models not only support the implementation of gene networks but they are also used for the understanding of their functionality [4–7]. In a similar manner, systems biology is used to understand the mechanisms of absorption, distribution, excretion, and toxicity of substances and/or molecules in order to prevent their potential negative effects before their use in clinical practice [8–10]. For that purpose, many algorithms and techniques have been employed such as Pathway Assist™ (http://www.ariadnegenomics.com/), PathArt™ (http://jubilantbiosys.com/), MetaCore™ (http://www.genego.com), and Pathways Analysis™ (http://www.ingenuity.com/). Other methodologies include clustering of gene‐expression data and generation of interaction networks [11], superparamagnetic clustering [12], simulated annealing [13], probabilistic graphical models [14], and Monte Carlo optimization [15].
1.3 Systems Biology Modeling Goals
One of the main goals of systems biology modeling is to develop models of biological systems described mechanistically and/or mathematically to understand the biological details and interactions. Such models target usually at simulating the biological experiment by predicting its outcome, and in the case of accurate predictions, an important step is the understanding of the biological system's mechanisms [16–19]. As it is expected, the goals of systems biology range from the scale or level of the models. Thus, it is possible to identify very complex aims in the case of microscale modeling, where interactions between molecular components exist and their expressions and concentrations are required as boundary and initial conditions in order to describe adequately the biosystem in the specific scale or in the interaction with other scales. Similarly, simpler aims can be defined for: (i) macroscale models, where the biosystem can be described by simple mathematical equations, differential, or even algebraic and (ii) microscale models, where a simple chemical reaction is enough to describe the dynamics of the system. Ideally, the modeling goals are satisfied when a multidisciplinary approach is adapted which integrates the base theory with the basic experiment and the corresponding mathematical representation. All together support each other in a circular way: theory is necessary to define the experiment, which provides data for the development of the mathematical model but the mathematical model is validated by the experiment, which refines the current theory and determines new. Integrative systems biology involves the iterative cycle of wet and dry laboratory research (Figure 1.1).
Figure 1.1 The iterative cycle of wet and dry laboratory research.
Other systems biology modeling goals are a better understanding of the interaction between various systems inside an organization. Such interactions include signaling pathways, biochemical pathways, and gene networks. The homeostatic interactions define the functional states between the systems and may be responsible for pathologic conditions. Thus, the developed models aim to identify the role of each component or feature inside the network or pathway and the degree to which it may affect the organization. These models can describe the cell cycle from the division to its apoptosis or differentiation. Other models focus on the description of the transcription of genes and translation of proteins under specific regulatory pathways, e.g. under the regulation by an enzyme and under specific conditions such as increased body temperature. More complex biosystems include energy generation and intercellular communication which is very common in neurological applications and models.
Another major goal of systems biology modeling is to test a specific biological hypothesis about a biological function. Such models usually attempt to identify the behavior or response of the system to stimuli even internal or external. Moreover, they aim to predict the interaction between other organisms or to identify the effect of pathologic and abnormal conditions in the system. Such models are usual in pharmacodynamics and contribute to the development of new treatment approaches for pathologic conditions and diseases [20, 21]. Traditionally, drug development is based on the identification of the substance, which interacts with the compounds; their experimental and using animal testing; and, finally, their testing in patients in clinical studies. Unfortunately, potential side effects are discovered at a late stage, e.g. after their use in patients. On the other hand, using systems biology models in pharmacology or even in toxicology provides the in silico clinical trials and obviously benefits the proper identification of side effects at earlier stages and, in many cases, even before the animal testing. In this way, the benefit is considered huge not only in terms of socioeconomic factors by reducing the costs of clinical trials and experiments in animals but also socially by improving the healthcare of patients by reducing the potential side effects. Furthermore, a reduction in the population participating in the clinical trials is achieved.
1.4 Systems Biology Modeling Approach
Modeling biological processes often requires accounting for action and feedback involving a wide range of spatial and temporal scales. Biological systems are organized at scales of many orders of magnitude in space and time. Space spanning ranges from the molecular scale (10−10 m) to the living organism scale (1 m), and time ranges from nanoseconds (10−9 s) to years (108 s) (Figure 1.2).
Besides the multidisciplinary character of a systems biology model, the scales of the biosystem also define the type of approach which will be employed. For example, the typical modeling approaches for microscale biosystem modeling are the reaction kinetics using ordinary differential equations (ODEs), the lattice reaction‐annihilation processes, and others. These approaches can be used for the modeling of molecular and subcellular processes such as mutations, gene alterations, signaling, metabolic pathways, and parts of the cell cycle. At the mesoscopic scale, the approaches include cell‐level ODEs, cellular automata, and evolution rules in order to model cell–cell interactions and cell–matrix interactions such as the phenomena of angiogenesis, the immune response, the local remodeling of the Extracellular matrix (ECM), etc. Finally, at the macroscopic scale, the typical approaches are usually based on the solution of partial differential equations (PDEs) such as the reaction‐diffusion, the continuous mechanics, and the convection equations. These models are used for the simulation not only of processes at the tissue level, e.g. diffusion of nutrients, cell migration, and invasion, but also of the blood flow dynamics, plaque growth modeling, and bone dynamics, which will be described in detail in the next chapters (Chapters 3–9). Table 1.1 presents the modeling approaches for each scale and potential applications in systems biology. It is worth noting that interactions between these scales and approaches can be defined.
Figure 1.2 The space scales in biological systems.
From the aforementioned, it is clear that there is a strong relation of the biological scale with the chosen experimental approach and the corresponding modeling approach. Figure 1.3 presents the relation of the modeling and experimental approach in correspondence to the biological scale. However, this scheme lacks one major characteristic of systems biology modeling concerning the interaction between the different scales (spatial and temporal) providing the ability to develop multiscale models able to simulate and describe complex phenomena in the biological systems.
To this end, Figure 1.4 presents a schematic illustration of the biological scales of significant relevance for cancer modeling including atomic, molecular, microscopic (tissue/multicellular), and macroscopic (organ) scales [22–24]. Compared to Figure 1.2, different scales represent both different spatial and temporal ranges. Multiscale cancer modeling requires the establishment of the linking between those scales.
Table 1.1 Modeling approaches and typical examples per scale in systems biology modeling.
Scale
Typical modeling approaches
Examples
Microscopic scale
Reaction kinetics using ODEs, the lattice reaction‐annihilation processes
Mutations, gene alterations, signaling, metabolic pathways and parts of the cell cycle, mitosis, apoptosis, etc.
Mesoscopic scale
Cell level ODEs, cellular automata, and evolution rules
Angiogenesis, the immune response, the local remodeling of the ECM, etc.
Macroscopic scale
Reaction‐diffusion, continuous mechanics, convection equations
Diffusion of nutrients, cell migration and invasion, cardiovascular dynamics, bone dynamics, etc.
Figure 1.3 The relation of the modeling and experimental approach according to the biological scale.
Figure 1.4 Schematic illustration of the biological levels of significant relevance for kidney cancer modeling.
Fundamentally, a multiscale model must explicitly account for more than one level of resolution across measurable domains of time, space, and/or function. To clarify, many models of physical systems implicitly account for multiple spatial scales by simplifying their boundary conditions into “black boxes” where assumptions about other spatial or temporal domains are summarized by governing equations. Further, explicitly modeled tiers of resolution must also provide additional information that could not be obtained by independently exploring a single scale in isolation.
1.5 Application of Multiscale Methods in Systems Biology
1.5.1 Introduction
Multiscale modeling is applied in various research areas ranging from the study of protein conformational dynamics to multiphase processes in granular media or hemodynamics, and from nuclear reactor physics to astrophysics [25]. Although a significant diversity is observed in research applications, there are many common principles and challenges such as the need for the development of advanced tools for programming and executing multiscale simulations, the validation of the numerical methods and results using experimental procedures, the selection of the proper numerical method according to the computational cost, and the biological substance or mathematical and physical aspects of the examined problem. In addition, despite the area of application, it is widely accepted that computer simulations are usually more cost‐effective, efficient, and time‐consuming compared to laboratory experiments and clinical studies.
According to Hoekstra et al. [26], a major question which arises is the determination of the concepts which orchestrate the multiscale modeling approaches which are inherent in multiscale systems. Traditional modeling approaches are focused on one scale. Considering the example of solid media, engineers may be interested in the macro‐scale behavior of solids using continuum models and represent the atomistic effects by constitutive relations, while physicists may be more interested in the behavior of solids at the atomic or electronic level, often working under the assumption that the relevant processes are homogeneous at the macroscopic scale [27]. Under this assumption, civil engineers are able to design structures (buildings, bridges, etc.), without the need to deepen the origin of the interactions between the atoms in the materials. On the other hand, physicists can give insight on the evolution of phenomena at a fundamental level, but they may face several difficulties when dealing with an engineering problem at the macro‐scale level.
The common aim of mathematics, engineering, and systems biology is to achieve a thorough understanding of biological systems at different hierarchical levels. The Interagency Modelling and Analysis Group (IMAG) has suggested the following definition for the term “multiscale modeling” [28–32]:
“Multiscale, biomedical modeling uses mathematics and computations to represent and simulate a physiological system at more than one biological scale. Biological scales include atomic, molecular, molecular complexes, subcellular, cellular, multicell systems, tissue, organ, multiorgan systems, organism, population, and behavior. These multiscale biomedical models may also include dynamical processes which span multiple time and length scales.”
A holistic understanding of many biological processes requires multiscale models which capture the relevant properties on all these scales [28–32]. It can be questioned whether the identification of general laws is relevant as a research aim for biology, but universal design principles, without doubt, play a critical role in engineering approaches that inspire a large part of systems biology [33].
For example, cancer is considered a complex, heterogeneous disease, characterized by many interaction processes evolving in multiple scales in time and space that act in parallel to drive cancer formation, progression, invasion, and metastasis [34]. These processes range from molecular reactions to cell–cell interactions, to tumor growth and invasion on the tissue scale, and even to larger scales, such as the physiology, pathophysiology, and population scales. In addition, many cancer properties (e.g. size, cell density, extracellular ligands, cellular receptors, mutation type(s), phenotypic distribution, vasculature status, blood vessel permeability, and treatment prognosis) are dynamic and patient‐dependent, changing and evolving with both time and treatments (e.g. cell death rate may vary over time when the patient is subjected to chemotherapy). All these dynamically changing cancer properties make the development of effective cancer therapies extremely difficult. Computational models which include patient‐specific parameters could be a supplementary tool to current statistical approaches to enhance personalized medicine and prediction of complex behaviors of cancer.
1.6 The Use of Systems Biology and Multiscale Modeling in Biomedical and Medical Science
At the organism level, an “infinite” number of processes is happening throughout its life. Moreover, these processes may be complex and define the interactions between pathways of the same level or multilevel interactions. The basic research in biomedical and medical science aims to identify the role of each process in order to clarify the causative mechanisms and pathways, which promote disease evolution. The overall target of such research is to provide the knowledge for the diagnosis, prognosis, and prediction of events related to the disease.
Computational modeling has an incremental role in understating the mechanisms, which underlie the disease in order to provide predictors potentially used for the diagnosis, prognosis, and prediction. For example, machine learning techniques and systems biology models are being implemented nowadays for risk stratification in cardiovascular disease [35]. In the same field, computational modeling is used for the estimation of hemodynamics and the calculation of variables such as endothelial shear stress or lipid accumulation for the prediction of regions, which are prone to atherosclerotic plaque growth [36–38]. Such models are implemented in many other diseases such as in cancer and oncology research [39], in arthritis [40], brain [41], etc. [42, 43].
