定量生理学(Quantitative Physiology)
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作者 陈尚宾(Shangbin Chen);[俄罗斯]Alexey Zaikin(阿列克谢。扎伊金
出版社 华中科技大学出版社
出版时间 2021-04
版次 1
装帧 平装
上书时间 2024-09-22
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品相描述:全新
图书标准信息
作者
陈尚宾(Shangbin Chen);[俄罗斯]Alexey Zaikin(阿列克谢。扎伊金
出版社
华中科技大学出版社
出版时间
2021-04
版次
1
ISBN
9787568066785
定价
138.00元
装帧
平装
开本
16开
纸张
胶版纸
页数
260页
字数
678千字
【内容简介】
Stephen Hawking says that the next 21st century will be the century of complexity and indeed now Systems Biology or Medicine means 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 advanced applications. This book aims to build multiscale 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, calcium signalling, 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.
【作者简介】
陈尚宾博士,武汉光电国家研究中心副教授,博士生导师。2001年于湖北师范学院获物理学学士学位,2006年于华中科技大学获生物医学工程博士学位。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,已主持完成国家自然科学基金两项。已在Journal of Neuroscience,Biophysical Journal,Frontiers in Neuroscience等期刊发表第一作者(含通讯)论文十余篇。2007年荣获湖北省自然科学奖一等奖(排名5)。合作者Alexey Zaikin教授是世界顶尖高校伦敦大学学院(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、生物非线性动力学和随机性建模等。Zaikin教授已发表学术论文逾百篇,包含Physical Review Letters多篇,谷歌学术统计h指数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。
【目录】
Part I Applied Methodology 1 Introduction to Quantitative Physiology . . . . . . . . 3 1.1 Understanding Physiology . . . . . . . . . . . . . . . . 3 1.2 Towards Quantitative Science . . . . . . . . . . . . . 4 1.3 FromGenome to Physiome . . . . . . . . . . . . . . . 5 1.4 Dealing with Complexity . . . . . . . . . . . . . . . . . 6 1.5 Why It Is Timely to Study Quantitative Physiology . . . . . . . . . . 6 1.5.1 Multi-Omic Revolution in Biology . 6 1.5.2 Big Data and PersonalisedMedicine 7 1.5.3 Genetic Editing and Synthetic Biology . . . . . . . . . . 8 1.6 Questions . . . . . 8 References . . . . . . . . . . 8 2 Systems and Modelling . . . . . . . . . 11 2.1 Modelling Process . . . . . . . . 11 2.2 Physiological Organ Systems . . . . . . . . 13 2.3 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 Questions . . . . . 20 References . . . . 21 3 Introduction to Basic Modelling . . . . . . . 23 3.1 Building a SimpleMathematicalModel . . . . . 23 3.1.1 Model of Falling Flea . . . . . . . . . . . . 23 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 3.2.3 Ludwig von Bertalanffy’s GrowthModel . . . . . . . 28 3.3 Questions . . . . . 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 Questions . . . . . 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 Questions . . . . . 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 Questions . . . . . 51 References . . . . . . . . . . 52 7 Calcium Signalling . . 53 7.1 Functions of Calcium . . . . . . 53 7.2 Calcium Oscillations . . . . . . . . 54 7.3 CalciumWaves 59 7.4 Questions . . . . . 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 > 0 and Conditions to Observe a Limit Cycle . . . . . . . . . . 64 8.4 Questions . . . . . 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 Questions . . . . . 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 Questions . . . . . 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: Periodicity via Noise-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 Questions . . . . . . . . . . 140 11.9 Questions . . . . . 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 QuorumSensing Coupling . . . . . . . . . . . . . 148 12.2.2 The Dynamical Regimes for a Minimal System of Repressilators Coupled via Phase-Repulsive Quorum Sensing . . . . . . . . 150 12.3 Systems Size Effects in Coupled Genetic Networks . . . . . . . 152 12.3.1 Clustering and Enhanced Complexity of the Inhomogeneous Regimes . . . . . . . . 153 12.3.2 Clustering Due to Regular Oscillations in Cell Colonies . . . . . . . . . 154 12.3.3 Parameter Heterogeneity on the Regular-Attractor Regime . . . . . . 155 12.3.4 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 Intracellular Associative Genetic Perceptron . . . . . . . 169 12.7.2 Stochastic Resonance in Classifying Genetic Perceptron . . . . . . . . 174 12.8 Questions . . . . . 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 Questions. . . . . . . . 196 13.2 Heart Physiome 197 13.2.1 Cardiovascular System. . . . . . . . . . . . 197 13.2.2 Heart Physiome . . . . . . . . . . . . . . . . . 198 13.2.3 Multi-Level Modelling . . . . . . . . . . . . 199 13.2.4 Questions. . . . . . . 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 Questions. . . . . . . . . .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 . . . . . . . . . . . . . . 223 13.4.6 Astrocytes and Integrated Information Theory of Consciousness . . 224 13.4.7 Questions. . . . . . . . . . . . 233 References . . . . . . . . . . 233 Acronyms 3M The modelling, model, and modeller are introduced in this book of Quantitative 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 AIDS Acquired immunodeficiency syndrome: 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 intracellular 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 neighbouring 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 of large 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 Extracellular 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 Extracellular 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 cellular 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 derived from a jellyfish, that exhibits bright green fluorescence when exposed to blue or ultraviolet light 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 Inositol 1,4,5-trisphosphate: A second messenger responsible for the release of intracellular Ca2 from internal stores, through IP3 receptors ICS Intracellular 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 of large 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 PDF 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 cellular 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 cellular 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 oxidation of acetyl-CoA into carbon dioxide and 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
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