Part I Applied Methodology
1 Introduction to ntitative Physiology . . . . . . . . 3
1.1 Understanding Physiology . . . . . . . . . . . . . . . . 3
1.2 Towards ntitative Science . . . . . . . . . . . . . 4
1.3 FromGenome to Physiome . . . . . . . . . . . . . . . 5
1.4 Dealing with Complexity . . . . . . . . . . . . . . . . . 6
1.5 Why It Is Timely to Study ntitative Physiology . . . . . . . . . . 6
1.5.1 Multi-Omi Rvotion in Biology . 6
1.5.2 Big Data and PersonalisedMedicine 7
1.5.3 Genetic Editing and Synthetic Biology . . . . . . . . . . 8
1.6 estions . . . . . 8
References . . . . . . . . . . 8
2 Systems and Modelling . . . . . . . . . 11
2.1 Modelling Process . . . . . . . . 11
2.2 Physiological Organ Systems . . . . . . . . 13
. EquationModels . . . . . . . . . . 14
2.4 Using ODEs in Modelling Physiology . . . . . . 16
2.4.1 Modelling Oscillations . . . . . . . . . . . 16
2.4.2 Linear Stability Analysis . . . . . . . . . . 16
2.4.3 Solving ODEs with the δ-Function . 17
2.5 Conservation Laws in Physiology . . . . . . . . . . 18
2.5.1 Conservation ofMomentumand Energy . . . . . . . . .18
2.5.2 Boxing With and Without Gloves . . 19
2.5.3 RotationalMovement . . . . . . . . . . . . 20
2.6 estions . . . . . 20
References . . . . 21
3 Introduction to Basic Modelling . . . . . . .
3.1 Building a SimpleMathematicalModel . . . . .
3.1.1 Model of Falling Flea . . . . . . . . . . . .
3.1.2 Scaling Arguments. . . . . . 25
3.1.3 Example: How High Can an Animal Jump? . . . . . . . 25
3.1.4 Example: How Fast Can we Walk before Breaking into a Run? . . . 25
3.2 Models that InvolveMetabolic Rate . . . . . . . . 26
3.2.1 Modelling Metabolic Rate . . . . . . . . 26
3.2.2 Example:Why do Large Birds find it Harder to Fly? . . . . . . . . . . . 27
3.. Ludwig von Bertalanffy’s GrowthModel . . . . . . . 28
3.3 estions . . . . . 29
Reference . . . . . . . . . . 29
xv
xvi Contents
4 Modelling Resources 31
4.1 Open Courses. . 31
4.2 Modelling Software . . . . . . . 31
4.3 Model Repositories . . . . . . . 34
4.4 estions . . . . . 35
References . . . . . . . . . . 35
Part II Basic Case Studies
5 Modelling Gene Expression . . . . . . . 39
5.1 Modelling Transcriptional Regulation and Simple Networks . . . . . . . . . . . . . 39
5.1.1 Basic Notions and Equations. . . . . . . 39
5.1.2 Equations for Transcriptional Regulation . . . . . . . 39
5.1.3 Examples of Some Common Genetic Networks . . . . . . 41
5.2 Simultaneous Regulation by Inhibition and Activation . . . . . . . .. 42
5.3 Autorepressor with Delay. . . . . . . . 43
5.4 Bistable Genetic Switch . . . . . . . . . 44
5.5 estions . . . . . 44
References . . . . . . . . . . 45
6 Metabolic Network . . 47
6.1 Metabolismand Network . . . . . . . 47
6.2 ConstructingMetabolic Network . . . . . . . . . . 49
6.3 Flux Balance Analysis . . . . . . . . 50
6.4 MyocardialMetabolic Network. . . . . . . . . . . . 51
6.5 estions . . . . . 51
References . . . . . . . . . . 52
7 Calcium Signalling . . 53
7.1 Functions of Calcium . . . . . . 53
7.2 Calcium Oscillations . . . . . . . . 54
7.3 CalciumWaves 59
7.4 estions . . . . . 59
References . . . . . . . . . . 60
8 Modelling Neural Activity . . . . . . . 61
8.1 Introduction to Brain Research . . . . . . . . . . . . 61
8.2 The Hodgkin–Huxley Model of Neuron Firing . . . . . . . . . 62
8.3 The FitzHugh–Nagumo Model: A Model of the HH Model . . . . . . . . . . . . . 63
8.3.1 Analysis of Phase Plane with Case Ia = 0 . . . . . . . . 63
8.3.2 Case Ia &g;0 ndCnditions to Observe a Limit Cycle . . . . . . . . . . 64
8.4 estions . . . . . 65
References . . . . . . . . . . 66
9 Blood Dynamics . . . . 67
9.1 Blood Hydrodynamics . . . . . . . 67
9.1.1 Basic Equations . . . . . . . . . . . . . . . . . 67
9.1.2 Poiseuille’s Law . . . . . . . . 67
9.2 Properties of Blood and ESR . . . . . . . 68
9.3 Elasticity of Blood Vessels . . . . . . 69
9.4 The PulseWave 69
9.5 Bernoulli’s Equation and What Happened to Arturo Toscanini in 1954 . . . . 70
9.6 The Korotkoff Sounds . . . . . . . . 71
9.7 estions . . . . . 71
Reference . . . . . . . . . . . 72
Contents xvii
10 Bone and Body Mechanics . . . . . 73
10.1 Elastic Deformations and the Hooke’s Law. . 73
10.2 Why Long Bones are Hollow or Bending of Bones . . . . . . . . 74
10.3 Viscoelasticity of Bones . . . . . . . 77
10.4 estions . . . . . 83
Reference . . . . . . . . . . 83
Part III Complex Applications
11 Constructive Effects of Noise . . . . . . . 87
11.1 Influence of Stochasticity . . . . . . 87
11.2 Review of Noise-Induced Effects . . . . . . . . . . 89
11.3 NewMechanisms of Noise-Induced Effects . 91
11.4 Noise-Induced Effects . . . . . . 93
11.4.1 Stochastic Resonance in Bone Remodelling as a Tool to Prevent
Bone Loss in Osteopenic Conditions 93
11.4.2 Transitions in the Presence of Additive Noise and On-Off
Intermittency . . . . . . 98
11.4.3 Phase Transitions Induced by Additive Noise. . . . . . . . 103
11.4.4 Noise-Induced Excitability . . . . . . . . 109
11.5 Doubly Stochastic Effects . . . . . . . . 113
11.5.1 Doubly Stochastic Resonance . . . . . . 113
11.5.2 A Simple Electronic Circuit Model for Doubly Stochastic
Resonance . . . . . . . . . . 117
11.5.3 Doubly Stochastic Coherence: PeriodiciyviNise-Induced
Symmetry in Bistable NeuralModels . . . . . . . . . . . 120
11.6 New Effects in Noise-Induced Propagation . . 125
11.6.1 Noise-Induced Propagation in Monostable Media . . . . . . . . .125
11.6.2 Noise-Induced Propagation and Frequency Selection of
Bichromatic Signals in BistableMedia . . . . . . . . 128
11.7 Noise-Induced Resonant Effects and Resonant Effects in the Presence of
Noise . . . . . . . . 129
11.7.1 Vibrational Resonance in a Noise-Induced Structure . . . . . . . . . . . . 129
11.7.2 System Size Resonance in Coupled Noisy Systems . . . . . . . . . . . . . 133
11.7.3 Coherence Resonance and Polymodality in Inhibitory Coupled
Excitable Oscillators . . . . . . . . . . . . . 136
11.8 Applications and Open estions . . . . . . . . . . 140
11.9 estions . . . . . 141
References . . . . . . . . . . 141
12 Complex and Surprising Dynamics in Gene Regulatory Networks . . . . . . . . . . 147
12.1 Nonlinear Dynamics in Synthetic Biology. . . 147
12.2 Clustering and Oscillation Death in Genetic Networks . . . . . . . 148
12.2.1 The Repressilator with orumSensing Coupling . . . . . . . . . . . . . 148
12.2.2 The Dynamical Regimes for a Minimal System of Repressilators
Coupled via Phase-Repulsive orum Sensing . . . . . . . . 150
1. Systems Size Effects in Coupled Genetic Networks . . . . . . . 152
1..1 Clustering and Enhanced Complexity of the Inhomogeneous
Regimes . . . . . . . . 153
1..2 Clustering Due to Regular Oscillations in Cell Colonies . . . . . . . . . 154
1.. Parameter Heterogeneity on the Regular-Attractor Regime . . . . . . 155
1..4 Irregular and Chaotic Self-Oscillations in Colonies of Identical
Cells . 155
xviii Contents
12.4 The Constructive Role of Noise in Genetic Networks. . . . . . . . . 157
12.4.1 Noise-Induced Oscillations in Circadian Gene Networks . . . . . . . . 157
12.4.2 Noise-Induced Synchronisation and Rhythms. . . . . . . . . 158
12.5 Speed Dependent Cellular Decision Making (SdCDM) in Noisy Genetic
Networks . . . . . 160
12.5.1 Speed Dependent Cellular Decision Making in a Small Genetic
Switch 161
12.5.2 Speed Dependent Cellular Decision Making in Large Genetic
Networks . . . . . . . . . 162
12.6 What is a Genetic Intelligence? . . . . . . . . . . . . 164
12.6.1 Supervised Learning . . . . . . . . . . . . . 164
12.6.2 Associative Learning . . . . . . . . . . . . . 166
12.6.3 Classification of Complex Inputs . . . 166
12.6.4 Applications and Implications of Bio-Artificial Intelligence . . . . . . 169
12.7 Effect of Noise in Intelligent Cellular Decision Making . . . . . . . . . . . . . . . . . 169
12.7.1 Stochastic Resonance in an Intrallar Associative Genetic
Perceptron . . . . . . . 169
12.7.2 Stochastic Resonance in Classifying Genetic Perceptron . . . . . . . . 174
12.8 estions . . . . . 183
References . . . . . . . . . . 183
13 Modelling Complex Phenomena in Physiology . . . 189
13.1 Cortical Spreading Depression (CSD) . . . . . . 189
13.1.1 What is CSD . . . . . . . . 189
13.1.2 Models of CSD . . . . . . . 189
13.1.3 Applications of CSDModels . . . . . . 192
13.1.4 estions. . . . . . . . 196
13.2 Heart Physiome 197
13.2.1 Cardiovascular System. . . . . . . . . . . . 197
13.2.2 Heart Physiome . . . . . . . . . . . . . . . . . 198
13.. Multi-Level Modelling . . . . . . . . . . . . 199
13.2.4 estions. . . . . . . 201
13.3 Modelling of Kidney Autoregulation . . . . . . . 202
13.3.1 Renal Physiology . . . . . . . 202
13.3.2 Experimental Observations . . . . . . . . 204
13.3.3 Model of Nephron Autoregulation . . 205
13.3.4 estions. . . . . . . . . .209
13.4 Brain Project . . 209
13.4.1 Mystery of Brain . . . . . . . . . . . . . . . . 209
13.4.2 Brain Projects . . . . . . . . . . . . . . . . . . . 210
13.4.3 Brain Simulation . . . . . . . 212
13.4.4 Mammalian Brain as a Network of Networks . . . . . . .215
13.4.5 Calculation of Integrated Information . . . . . . . . . . . . . . 2
13.4.6 Astrocytes and Integrated Information Theory of Consciousness . . 224
13.4.7 estions. . . . . . . . . . . .
References . . . . . . . . . .
Acronyms
3M The modelling, model, and modeller are introduced in this book of ntitative Physiology
AcCoA Acetyl-CoA: It is an intermediary molecule that participates in many biochemical reactions in carbohydrates,
fatty acids, and amino acids metabolism
ADP Adenosine diphosphate: It is an important organic compound in metabolism and is essential to the flow of
energy in living cells
AI Artificial intelligence: It is sometimes called machine intelligence, in contrast to the human intelligence
AS Acquired immunodeficiency synoe: It is a transmissible disease caused by the human immunodeficiency
virus (HIV)
AP Action potential: An action potential is a rapid rise and subsequent fall in membrane potential of a neuron
ATP Adenosine triphosphate: The ubiquitous molecule necessary for intrallar energy storage and transfer
BMI Body mass index: It is a measure of body fat based on height and weight that applies to adult men and women
BRAIN Brain Research through Advancing Innovative Neurotechnologies: The BRAIN Initiative launched in April
2013 is focused on revolutionising our understanding of the human brain
CA Cellular automaton: It is a specifically shaped group of model cells known for evolving through multiple and
discrete time steps according to a rule set depending on neiouring cell states
CICR Calcium-induced calcium release: The autocatalytic release of Ca2 from the endoplasmic or sarcoplasmic
reticulum through IP3 receptors or ryanodine receptors. CICR causes the fast release o are amounts of
Ca2 from internal stores and is the basis for Ca2 oscillations and waves in a wide variety of cell types
CNS Central nervous system: It is the part of the nervous system consisting of the brain and spinal cord
CR Coherence resonance: It refers to a phenomenon in which addition of certain amount of external noise in
excitable system makes its oscillatory responses most coherent
CSD Cortical spreading depression: It is characterised by the propagation of depolarisation waves across the grey
matter at a velocity of 2–5 mm/min
CVD Cardiovascular disease: It is a class of diseases that involve the heart or blood vessels
DFBA Dynamic flux balance analysis: It is the dynamic extension of flux balance analysis (FBA)
DNA Deoxyribonucleic acid: It is a molecule comprised of two chains that coil around each other to form a double
helix carrying the genetic information
DSC Doubly stochastic coherence
DSE Doubly stochastic effects
EC coupling Excitation–contraction coupling: It describes a series of events, from the production of an electrical impulse
(action potential) to the contraction of muscles
ECF Extrallar fluid: The portion of the body fluid comprises the interstitial fluid and blood plasma
ECG Electrocardiogram (or EKG): The record is produced by electrocardiography to represent the heart’s electrical
action
ECS Extrallar space: It is usually taken to be outside the plasma membranes and occupied by fluid
EEG Electroencephalography: It is an electrophysiological monitoring method to record electrical activity of the
brain
ER Endoplasmic reticulum: An internal llar compartment in non-muscle cells acting as an important Ca2
store. The analogous compartment in muscle cells is termed the sarcoplasmic reticulum (SR)
ETC Electron transport chain
FA Fatty acid: It is the building block of the fat in our bodies and in the food we eat
FBA Flux balance analysis: It is a widely used approach for studying biochemical networks
xix
xx Acronyms
FHC Familial hypertrophic cardiomyopathy: It is a heart condition characterised by thickening (hypertrophy) of
the heart (cardiac) muscle
FHN FitzHugh–Nagumo model: It is named after Richard FitzHugh and Jin-Ichi Nagumo for describing a
prototype of an excitable system (e.g., a neuron)
GFP Green fluorescent protein: A protein, originally derive fo a jellyfish, that ehts bright green fluorescence
when exposed to blue or ultraviolet lit
GRN Gene regulatory network or genetic regulatory network: It is a collection of regulators that interact with each
other and with other substances in the cell to govern the gene expression levels of mRNA and proteins
Glu Glucose: Glucose is a simple sugar with the molecular formula C6H12O6
Gly Glycogen: It is amultibranched polysaccharide of glucose that serves as a form of energy storage in organisms
HBP Human Brain Project: It is a European Commission Future and Emerging Technologies Flagship started on
1 October 2013
HGP Human Genome Project: It is an international project with the goal of determining the sequence of nucleotide
base pairs that make up human DNA and of identifying and mapping all genes of the human genome from
both a physical and a functional standpoint
HH model The Hodgkin–Huxley model: It is a mathematical model that describes how action potentials in neurons are
initiated and propagated
II Integrated information: It is ameasure of the degree to which the components of a system areworking together
to produce outputs
IP3 Inositl ,4,5-trisphosphate: A second messenger responsible for the release of intrallar Ca2 from
internal stores, through IP3 receptors
ICS Intrallar space: It is taken to be inside the cell
iPS Induced pluripotent stem cells: They are a type of pluripotent stem cell that can be generated directly from
adult cells
ISIH Interspike interval histogram
IUPS The International Union of Physiological Societies
Lac Lactate (or Lactic acid): It has the molecular formula CH3CH(OH)CO2H
LC Limit cycle
MFT Mean field theory: It studies the behaviour o are and complex stochastic models by using a simpler model
MOMA Minimisation of metabolic adjustment: It is used as an objective function for FBA
NADH Nicotinamide adenine dinucleotide hydride
NADPH Nicotinamide adenine dinucleotide phosphate
NIE Noise-induced excitability
NIT Noise-induced transition
NSR National Simulation Resource
ODE Ordinary differential equation: It is a differential equation containing one or more functions of one
independent variable and its derivatives
PC Phosphocreatine: It is a phosphorylated creatine molecule that serves as a rapidly mobilisable reserve of
high-energy phosphates in skeletal muscle and the brain
PDE Partial differential equation: It is a differential equation that contains beforehand unknown multivariable
functions and their partial derivatives
Probability distribution function
PE Potential energy
PNS Peripheral nervous system
Pyr Pyruvate: It is a key intermediate in several metabolic pathways throughout the cell
RD Reaction–diffusion: A reaction–diffusion system consists of the diffusion of material and the production of
that material by reaction
RFP Red fluorescent protein
SCN The suprachiasmatic nuclei
SdCDM Speed dependent llar decision making
SERCA Sarcoplasmic/endoplasmic reticulum Ca2 ATPase: A Ca2 ATPase pump that transports Ca2 up its
concentration gradient from the cytoplasm to the ER/SR
SGN Synthetic gene network
Acronyms xxi
SNR Signal to noise ratio
SR Sarcoplasmic reticulum:An internal llar compartment in muscle cells that functions as an important Ca2
store. The analogous compartment in non-muscle cells is called the endoplasmic reticulum (ER)
SR Stochastic resonance: It is a phenomenon where a signal can be boosted by adding white noise to the signal
TCA cycle Tricarboxylic acid cycle or the Krebs cycle: It is a series of chemical reactions used by all aerobic organisms
to generate energy through the otion of acetyl-CoA into carbon dioe nd chemical energy in the form
of guanosine triphosphate (GTP)
TF Transcription factor: It is a protein that binds to specific DNA sequences, thereby controlling the rate of
transcription of genetic information from DNA to messenger RNA
TGF Tubuloglomerular feedback
UCS Ultimate compressive stress
VGCC Voltage-gated Ca2 channels: Membrane Ca2 channels that open in response to depolarisation of the cell
membrane
VR Vibrational resonance
WHO World Health Organization: It is a specialised agency of the United Nations to direct international health
陈尚宾博士，武汉光电研究中心副教授，博士生导师。2001年于湖北师范学院获物理学士学，2006年于华中科技大学获生物医学工程博士。2006年8月至今在华中科技大学工作；其间2008年2-5月在英国Bradford大学做访问学者，2010-2012在加拿大英属哥伦比亚大学（UBC）做博士后研究两年。其研究工作涉及神经光学成像、神经系统建模、定量生理学，已主持完成自然科学两项。已在Journal of Neuroscience，Biophysical Journal，Frontiers in Neuroscience等期刊发表*作者（含通讯）十余篇。2007年荣获湖北省自然科学奖一等奖（排名5）。合作者Alexey Zaikin教授是世界*高校伦敦大学学院（UCL）系统医学和应用数学讲席教授，研究兴趣包括系统生物学、理论生物物理学、生物非线动力学和随机建模等。Zaikin教授已发表学术逾百篇，包含Physical Review Letters多篇，谷歌学术统h指29。Zaikin教授自2016年以来短期受聘于华中科技大学工程科学学院，参与《定量生理学》课程教学。
欧洲科学院院士Jurgen Kurths评价很高。他的评价摘要为：In this textbook, the two very active researchers in ntitative Physiology,Shangbin Chen and Alexey Zaikin very successfully and originally bring some order in this “zoo of models and their treatment” to become understandable for newcomers in the field.This very well written textbook by Shangbin Chen and Alexey Zaikin is a systematic presentation of the strongly evolving field of ntitative Physiology. It provides the basic principles of this difficult kind of modelling as well as the treatment of the corresponding equations. This book is theoutcome of a joint lecture both gave to students of biomedical engineering at the undergraduate elite School of Engineering Science, Huazhong University of Science and Technology. It is a very useful introduction for starters in the field but it provides also important information and suggestions for researchers in physiology and complex systems science as well as for a broad range of specialists in bioengineering, biology, computer science and others.
这是一本整合数学、物理、信息科学等来理学的英文教材，其目的在于倡导以建模、定量化和系统论的方式来更好地理解生理系统。本书将面向生理学的应用需求（A）、介绍一些基础方法学（B）、提供一些练习案例（C）来构建书本框架。结合生命科学领域迅展的各类组学，示范地建立从基因组到生理组的多层次建模。书本将以系统和模型的新视角来描述和介绍基因表达、骨骼力学、血流动力学、神经活动等生理学主要内容，期望从理论和实践相结合的角度更深入地理解生命系统。
本书适合作为生物医学工程方向生的教材，同时也应适合相关研究者作为一本值得参考的著作。
本书与斯普林格在全球同步出版，华中科技大学出版社出版的英文版在中国大陆地区发行。
Stephen Hawking says that the next 21st century will be the century of complexity and indeed now Systems Biology or Mediie eans dealing with complexity. Both genome and physiome have been emerged in studying complex physiological systems. Computational and mathematical modelling has been regarded as an efficient tool to boost understanding about the living systems in normal or pathophysiological states. This textbook introduces the students and researchers to the modelling and computational study of physiology (i.e. quantitative physiology), which is an increasingly important branch of systems biology. The topics cover basic methodology, case practices and advance ppictons. This book aims to buil mtscale model for investigating the function in living systems, or, how organisms, organ systems, organs, cells, and biomolecules carry out the chemical or physical functions that exist in a living system. Some of the models related on gene expression, cacm gnalling, neural activity, blood dynamics and bone mechanics have been addressed. This book is devoted to set a paradigm for quantitative physiology by integrating biology, mathematics, physics and informatics etc.
非常抱歉，您前期未参加预订活动，
无法支付尾款哦！
