Physical biology of the cell Second editionPDF|Epub|txt|kindle电子书版本网盘下载

Physical biology of the cell Second editionPDF格式电子书版下载

注意：本站所有压缩包均有解压码： 点击下载压缩包解压工具

PART 1 THE FACTS OF LIFE1

Chapter 1 Why：Biology by the Numbers3

1.1 BIOLOGICAL CARTOGRAPHY3

1.2 PHYSICAL BIOLOGY OF THE CELL4

Model Building Requires a Substrate of Biological Facts and Physical （or Chemical） Principles5

1.3 THE STUFF OF LIFE5

Organisms Are Constructed from Four Great Classes of Macromolecules6

Nucleic Acids and Proteins Are Polymer Languages with Different Alphabets7

1.4 MODEL BUILDING IN BIOLOGY9

1.4.1 Models as Idealizations9

Biological Stuff Can Be Idealized Using Many Different Physical Models11

1.4.2 Cartoons and Models16

Biological Cartoons Select Those Features of the Problem Thought to Be Essential16

Quantitative Models Can Be Built by Mathematicizing the Cartoons19

1.5 QUANTITATIVE MODELS AND THE POWER OF IDEALIZATION20

1.5.1 On the Springiness of Stuff21

1.5.2 The Toolbox of Fundamental Physical Models22

1.5.3 The Unifying Ideas of Biology23

1.5.4 Mathematical Toolkit25

1.5.5 The Role of Estimates26

1.5.6 On Being Wrong29

1.5.7 Rules of Thumb：Biology by the Numbers30

1.6 SUMMARY AND CONCLUSIONS32

1.7 FURTHER READING32

1.8 REFERENCES33

Chapter 2 What and Where：Construction Plans for Cells and Organisms35

2.1 AN ODE TO E.COLI35

2.1.1 The Bacterial Standard Ruler37

The Bacterium E.coli Will Serve as Our Standard Ruler37

2.1.2 Taking the Molecular Census38

The Cellular Interior Is Highly Crowded，with Mean Spacings Between Molecules That Are Comparable to Molecular Dimensions48

2.1.3 Looking Inside Cells49

2.1.4 Where Does E.coli Fit？51

Biological Structures Exist Over a Huge Range of Scales51

2.2 CELLS AND STRUCTURES WITHIN THEM52

2.2.1 Cells：A Rogue’s Gallery52

Cells Come in a Wide Variety of Shapes and Sizes and with a Huge Range of Functions52

Cells from Humans Have a Huge Diversity of Structure and Function57

2.2.2 The Cellular Interior：Organelles59

2.2.3 Macromolecular Assemblies：The Whole is Greater than the Sum of the Parts63

Macromolecules Come Together to Form Assemblies63

Helical Motifs Are Seen Repeatedly in Molecular Assemblies64

Macromolecular Assemblies Are Arranged in Superstructures65

2.2.4 Viruses as Assemblies66

2.2.5 The Molecular Architecture of Cells：From Protein Data Bank （PDB） Files to Ribbon Diagrams69

Macromolecular Structure Is Characterized Fundamentally by Atomic Coordinates69

Chemical Groups Allow Us to Classify Parts of the Structure of Macromolecules70

2.3 TELESCOPING UP IN SCALE：CELLS DON’T GO IT ALONE72

2.3.1 Multicellularity as One of Evolution’s Great Inventions73

Bacteria Interact to Form Colonies such as Biofilms73

Teaming Up in a Crisis：Lifestyle of Dictyostelium discoideum75

Multicellular Organisms Have Many Distinct Communities of Cells76

2.3.2 Cellular Structures from Tissues to Nerve Networks77

One Class of Multicellular Structures is the Epithelial Sheets77

Tissues Are Collections of Cells and Extracellular Matrix77

Nerve Cells Form Complex，Multicellular Complexes78

2.3.3 Multicellular Organisms78

Cells Differentiate During Development Leading to Entire Organisms78

The Cells of the Nematode Worm，Caenorhabditis Elegans，Have Been Charted，Yielding a Cell-by-Cell Picture of the Organism80

Higher-Level Structures Exist as Colonies of Organisms82

2.4 SUMMARY AND CONCLUSIONS83

2.5 PROBLEMS83

2.6 FURTHER READING84

2.7 REFERENCES85

Chapter 3 When：Stopwatches at Many Scales87

3.1 THE HIERARCHY OF TEMPORAL SCALES87

3.1.1 The Pageant of Biological Processes89

Biological Processes Are Characterized by a Huge Diversity of Time Scales89

3.1.2 The Evolutionary Stopwatch95

3.1.3 The Cell Cycle and the Standard Clock99

The E.coli Cell Cycle Will Serve as Our Standard Stopwatch99

3.1.4 Three Views of Time in Biology105

3.2 PROCEDURAL TIME106

3.2.1 The Machines （or Processes） of the Central Dogma107

The Central Dogma Describes the Processes Whereby the Genetic Information Is Expressed Chemically107

The Processes of the Central Dogma Are Carried Out by Sophisticated Molecular Machines108

3.2.2 Clocks and Oscillators110

Developing Embryos Divide on a Regular Schedule Dictated by an Internal Clock111

Diurnal Clocks Allow Cells and Organisms to Be on Time Everyday111

3.3 RELATIVE TIME114

3.3.1 Checkpoints and the Cell Cycle115

The Eukaryotic Cell Cycle Consists of Four Phases Involving Molecular Synthesis and Organization115

3.3.2 Measuring Relative Time117

Genetic Networks Are Collections of Genes Whose Expression Is Interrelated117

The Formation of the Bacterial Flagellum Is Intricately Organized in Space and Time119

3.3.3 Killing the Cell：The Life Cycles of Viruses120

Viral Life Cycles Include a Series of Self-Assembly Processes121

3.3.4 The Process of Development122

3.4 MANIPULATED TIME125

3.4.1 Chemical Kinetics and Enzyme Turnover125

3.4.2 Beating the Diffusive Speed Limit126

Diffusion Is the Random Motion of Microscopic Particles in Solution127

Diffusion Times Depend upon the Length Scale127

Diffusive Transport at the Synaptic Junction Is the Dynamical Mechanism for Neuronal Communication128

Molecular Motors Move Cargo over Large Distances in a Directed Way129

Membrane-Bound Proteins Transport Molecules from One Side of a Membrane to the Other130

3.4.3 Beating the Replication Limit131

3.4.4 Eggs and Spores：Planning for the Next Generation132

3.5 SUMMARY AND CONCLUSIONS133

3.6 PROBLEMS133

3.7 FURTHER READING136

3.8 REFERENCES136

Chapter 4 Who：“Bless the Little Beasties”137

4.1 CHOOSING A GRAIN OF SAND137

Modern Genetics Began with the Use of Peas as a Model System138

4.1.1 Biochemistry and Genetics138

4.2 HEMOGLOBIN AS A MODEL PROTEIN143

4.2.1 Hemoglobin，Receptor-Ligand Binding，and the Other Bohr143

The Binding of Oxygen to Hemoglobin Has Served as a Model System for Ligand-Receptor Interactions More Generally143

Quantitative Analysis of Hemoglobin Is Based upon Measuring the Fractional Occupancy of the Oxygen-Binding Sites as a Function of Oxygen Pressure144

4.2.2 Hemoglobin and the Origins of Structural Biology144

The Study of the Mass of Hemoglobin Was Central in the Development of Centrifugation145

Structural Biology Has Its Roots in the Determination of the Structure of Hemoglobin145

4.2.3 Hemoglobin and Molecular Models of Disease146

4.2.4 The Rise of Allostery and Cooperativity146

4.3 BACTERIOPHAGES AND MOLECULAR BIOLOGY147

4.3.1 Bacteriophages and the Origins of Molecular Biology148

Bacteriophages Have Sometimes Been Called the “Hydrogen Atoms of Biology”148

Experiments on Phages and Their Bacterial Hosts Demonstrated That Natural Selection Is Operative in Microscopic Organisms148

The Hershey-Chase Experiment Both Confirmed the Nature of Genetic Material and Elucidated One of the Mechanisms of Viral DNA Entry into Cells149

Experiments on Phage T4 Demonstrated the Sequence Hypothesis of Collinearity of DNA and Proteins150

The Triplet Nature of the Genetic Code and DNA Sequencing Were Carried Out on Phage Systems150

Phages Were Instrumental in Elucidating the Existence of mRNA151

General Ideas about Gene Regulation Were Learned from the Study of Viruses as a Model System152

4.3.2 Bacteriophages and Modern Biophysics153

Many Single- Molecule Studies of Molecular Motors Have Been Performed on Motors from Bacteriophages154

4.4 A TALE OF TWO CELLS：E.COLI AS A MODEL SYSTEM154

4.4.1 Bacteria and Molecular Biology154

4.4.2 E.coli and the Central Dogma156

The Hypothesis of Conservative Replication Has Falsifiable Consequences156

Extracts from E.coli Were Used to Perform In Vitro Synthesis of DNA，mRNA，and Proteins157

4.4.3 The lac Operon as the “Hydrogen Atom” of Genetic Circuits157

Gene Regulation in E.coli Serves as a Model for Genetic Circuits in General157

The lac Operon Is a Genetic Network That Controls the Production of the Enzymes Responsible for Digesting the Sugar Lactose158

4.4.4 Signaling and Motility：The Case of Bacterial Chemotaxis159

E.coli Has Served as a Model System for the Analysis of Cell Motility159

4.5 YEAST：FROM BIOCHEMISTRY TO THE CELL CYCLE161

Yeast Has Served as a Model System Leading to Insights in Contexts Ranging from Vitalism to the Functioning of Enzymes to Eukaryotic Gene Regulation161

4.5.1 Yeast and the Rise of Biochemistry162

4.5.2 Dissecting the Cell Cycle162

4.5.3 Deciding Which Way Is Up：Yeast and Polarity164

4.5.4 Dissecting Membrane Traffic166

4.5.5 Genomics and Proteomics167

4.6 FLIES AND MODERN BIOLOGY170

4.6.1 Flies and the Rise of Modern Genetics170

Drosophila melanogaster Has Served as a Model System for Studies Ranging from Genetics to Development to the Functioning of the Brain and Even Behavior170

4.6.2 How the Fly Got His Stripes171

4.7 OF MICE AND MEN173

4.8 THE CASE FOR EXOTICA174

4.8.1 Specialists and Experts174

4.8.2 The Squid Giant Axon and Biological Electricity175

There Is a Steady-State Potential Difference Across the Membrane of Nerve Cells176

Nerve Cells Propagate Electrical Signals and Use Them to Communicate with Each Other176

4.8.3 Exotica Toolkit178

4.9 SUMMARY AND CONCLUSIONS179

4.10 PROBLEMS179

4.11 FURTHER READING181

4.12 REFERENCES183

PART 2 LIFE AT REST185

Chapter 5 Mechanical and Chemical Equilibrium in the Living Cell187

5.1 ENERGY AND THE LIFE OF CELLS187

5.1.1 The Interplay of Deterministic and Thermal Forces189

Thermal Jostling of Particles Must Be Accounted for in Biological Systems189

5.1.2 Constructing the Cell：Managing the Mass and Energy Budget of the Cell190

5.2 BIOLOGICAL SYSTEMS AS MINIMIZERS200

5.2.1 Equilibrium Models for Out of Equilibrium Systems200

Equilibrium Models Can Be Used for Nonequilibrium Problems if Certain Processes Happen Much Faster Than Others201

5.2.2 Proteins in “Equilibrium”202

Protein Structures are Free-Energy Minimizers203

5.2.3 Cells in “Equilibrium”204

5.2.4 Mechanical Equilibrium from a Minimization Perspective204

The Mechanical Equilibrium State is Obtained by Minimizing the Potential Energy204

5.3 THE MATHEMATICS OF SUPERLATIVES209

5.3.1 The Mathematization of Judgement：Functions and Functionals209

Functionals Deliver a Number for Every Function They Are Given210

5.3.2 The Calculus of Superlatives211

Finding the Maximum and Minimum Values of a Function Requires That We Find Where the Slope of the Function Equals Zero211

5.4 CONFIGURATIONAL ENERGY214

In Mechanical Problems，Potential Energy Determines the Equilibrium Structure214

5.4.1 Hooke’s Law：Actin to Lipids216

There is a Linear Relation Between Force and Extension of a Beam216

The Energy to Deform an Elastic Material is a Quadratic Function of the Strain217

5.5 STRUCTURES AS FREE-ENERGY MINIMIZERS219

The Entropy is a Measure of the Microscopic Degeneracy of a Macroscopic State219

5.5.1 Entropy and Hydrophobicity222

Hydrophobicity Results from Depriving Water Molecules of Some of Their Configurational Entropy222

Amino Acids Can Be Classified According to Their Hydrophobicity224

When in Water，Hydrocarbon Tails on Lipids Have an Entropy Cost225

5.5.2 Gibbs and the Calculus of Equilibrium225

Thermal and Chemical Equilibrium are Obtained by Maximizing the Entropy225

5.5.3 Departure from Equilibrium and Fluxes227

5.5.4 Structure as a Competition228

Free Energy Minimization Can Be Thought of as an Alternative Formulation of Entropy Maximization228

5.5.5 An Ode to ΔG230

The Free Energy Reflects a Competition Between Energy and Entropy230

5.6 SUMMARY AND CONCLUSIONS231

5.7 APPENDIX：THE EULER-LAGRANGE EQUATIONS，FINDING THE SUPERLATIVE232

Finding the Extrema of Functionals Is Carried Out Using the Calculus of Variations232

The Euler-Lagrange Equations Let Us Minimize Functionals by Solving Differential Equations232

5.8 PROBLEMS233

5.9 FURTHER READING235

5.10 REFERENCES236

Chapter 6 Entropy Rules！237

6.1 THE ANALYTICAL ENGINE OF STATISTICAL MECHANICS237

The Probability of Different Microstates Is Determined by Their Energy240

6.1.1 A First Look at Ligand-Receptor Binding241

6.1.2 The Statistical Mechanics of Gene Expression：RNA Polymerase and the Promoter244

A Simple Model of Gene Expression Is to Consider the Probability of RNA Polymerase Binding at the Promoter245

Most Cellular RNA Polymerase Molecules Are Bound to DNA245

The Binding Probability of RNA Polymerase to Its Promoter Is a Simple Function of the Number of Polymerase Molecules and the Binding Energy247

6.1.3 Classic Derivation of the Boltzmann Distribution248

The Boltzmann Distribution Gives the Probability of Microstates for a System in Contact with a Thermal Reservoir248

6.1.4 Boltzmann Distribution by Counting250

Different Ways of Partitioning Energy Among Particles Have Different Degeneracies250

6.1.5 Boltzmann Distribution by Guessing253

Maximizing the Entropy Corresponds to Making a Best Guess When Faced with Limited Information253

Entropy Maximization Can Be Used as a Tool for Statistical Inference255

The Boltzmann Distribution is the Maximum Entropy Distribution in Which the Average Energy is Prescribed as a Constraint258

6.2 ON BEING IDEAL259

6.2.1 Average Energy of a Molecule in a Gas259

The Ideal Gas Entropy Reflects the Freedom to Rearrange Molecular Positions and Velocities259

6.2.2 Free Energy of Dilute Solutions262

The Chemical Potential of a Dilute Solution Is a Simple Logarithmic Function of the Concentration262

6.2.3 Osmotic Pressure as an Entropic Spring264

Osmotic Pressure Arises from Entropic Effects264

Viruses，Membrane-Bound Organelles，and Cells Are Subject to Osmotic Pressure265

Osmotic Forces Have Been Used to Measure the Interstrand Interactions of DNA266

6.3 THE CALCULUS OF EQUILIBRIUM APPLIED：LAW OF MASS ACTION267

6.3.1 Law of Mass Action and Equilibrium Constants267

Equilibrium Constants are Determined by Entropy Maximization267

6.4 APPLICATIONS OF THE CALCULUS OF EQUILIBRIUM270

6.4.1 A Second Look at Ligand-Receptor Binding270

6.4.2 Measuring Ligand-Receptor Binding272

6.4.3 Beyond Simple Ligand-Receptor Binding：The Hill Function273

6.4.4 ATP Power274

The Energy Released in ATP Hydrolysis Depends Upon the Concentrations of Reactants and Products275

6.5 SUMMARY AND CONCLUSIONS276

6.6 PROBLEMS276

6.7 FURTHER READING278

6.8 REFERENCES278

Chapter 7 Two-State Systems：From Ion Channels to Cooperative Binding281

7.1 MACROMOLECULES WITH MULTIPLE STATES281

7.1.1 The Internal State Variable Idea281

The State of a Protein or Nucleic Acid Can Be Characterized Mathematically Using a State Variable282

7.1.2 Ion Channels as an Example of Internal State Variables286

The Open Probability （σ） of an Ion Channel Can Be Computed Using Statistical Mechanics287

7.2 STATE VARIABLE DESCRIPTION OF BINDING289

7.2.1 The Gibbs Distribution：Contact with a Particle Reservoir289

The Gibbs Distribution Gives the Probability of Microstates for a System in Contact with a Thermal and Particle Reservoir289

7.2.2 Simple Ligand-Receptor Binding Revisited291

7.2.3 Phosphorylation as an Example of Two Internal State Variables292

Phosphorylation Can Change the Energy Balance Between Active and Inactive States293

Two-Component Systems Exemplify the Use of Phosphorylation in Signal Transduction295

7.2.4 Hemoglobin as a Case Study in Cooperativity298

The Binding Affinity of Oxygen for Hemoglobin Depends upon Whether or Not Other Oxygens Are Already Bound298

A Toy Model of a Dimeric Hemoglobin （Dimoglobin） Illustrate the Idea of Cooperativity298

The Monod-Wyman-Changeux （MWC） Model Provides a Simple Example of Cooperative Binding300

Statistical Models of the Occupancy of Hemoglobin Can Be Written Using Occupation Variables301

There is a Logical Progression of Increasingly Complex Binding Models for Hemoglobin301

7.3 ION CHANNELS REVISITED：LIGAND-GATED CHANNELS AND THE MWC MODEL305

7.4 SUMMARY AND CONCLUSIONS308

7.5 PROBLEMS308

7.6 FURTHER READING310

7.7 REFERENCES310

Chapter 8 Random Walks and the Structure of Macromolecules311

8.1 WHAT IS A STRUCTURE：PDB OR R G？311

8.1.1 Deterministic versus Statistical Descriptions of Structure312

PDB Files Reflect a Deterministic Description of Macromolecular Structure312

Statistical Descriptions of Structure Emphasize Average Size and Shape Rather Than Atomic Coordinates312

8.2 MACROMOLECULES AS RANDOM WALKS312

Random Walk Models of Macromolecules View Them as Rigid Segments Connected by Hinges312

8.2.1 A Mathematical Stupor313

In Random Walk Models of Polymers，EveryMacromolecular Configuration Is Equally Probable313

The Mean Size of a Random Walk Macromolecule Scales as the Square Root of the Number of Segments，？N314

The Probability of a Given Macromolecular State Depends Upon Its Microscopic Degeneracy315

Entropy Determines the Elastic Properties of Polymer Chains316

The Persistence Length Is a Measure of the Length Scale Over Which a Polymer Remains Roughly Straight319

8.2.2 How Big Is a Genome？321

8.2.3 The Geography of Chromosomes322

Genetic Maps and Physical Maps of Chromosomes Describe Different Aspects of Chromosome Structure322

Different Structural Models of Chromatin Are Characterized by the Linear Packing Density of DNA323

Spatial Organization of Chromosomes Shows Elements of Both Randomness and Order324

Chromosomes Are Tethered at Different Locations325

Chromosome Territories Have Been Obsered in Bacterial Cells327

Chromosome Territories in Vibrio cholerae Can Be Explored Using Models of Polymer Confinement and Tethering328

8.2.4 DNA Looping：From Chromosomes to Gene Regulation333

The Lac Repressor Molecule Acts Mechanistically by Forming a Sequestered Loop in DNA334

Looping of Large DNA Fragments Is Dictated by the Difficulty of Distant Ends Finding Each Other334

Chromosome Conformation Capture Reveals the Geometry of Packing of Entire Genomes in Cells336

8.3 THE NEW WORLD OF SINGLE-MOLECULE MECHANICS337

Single-Molecule Measurement Techniques Lead to Force Spectroscopy337

8.3.1 Force-Extension Curves：A New Spectroscopy339

Different Macromolecules Have Different Force Signatures When Subjected to Loading339

8.3.2 Random Walk Models for Force-Extension Curves340

The Low-Force Regime in Force-Extension Curves Can Be Understood Using the Random Walk Model340

8.4 PROTEINS AS RANDOM WALKS344

8.4.1 Compact Random Walks and the Size of Proteins345

The Compact Nature of Proteins Leads to an Estimate of Their Size345

8.4.2 Hydrophobic and Polar Residues：The HP Model346

The HP Model Divides Amino Acids into Two Classes：Hydrophobic and Polar346

8.4.3 HP Models of Protein Folding348

8.5 SUMMARY AND CONCLUSIONS351

8.6 PROBLEMS351

8.7 FURTHER READING353

8.8 REFERENCES353

Chapter 9 Electrostatics for Salty Solutions355

9.1 WATER AS LIFE’S AETHER355

9.2 THE CHEMISTRY OF WATER358

9.2.1 pH and the Equilibrium Constant358

Dissociation of Water Molecules Reflects a Competition Between the Energetics of Binding and the Entropy of Charge Liberation358

9.2.2 The Charge on DNA and Proteins359

The Charge State of Biopolymers Depends upon the pH of the Solution359

Different Amino Acids Have Different Charge States359

9.2.3 Salt and Binding360

9.3 ELECTROSTATICS FOR SALTY SOLUTIONS360

9.3.1 An Electrostatics Primer361

A Charge Distribution Produces an Electric Field Throughout Space362

The Flux of the Electric Field Measures the Density of Electric Field Lines363

The Electrostatic Potential Is an Alternative Basis for Describing the Electrical State of a System364

There Is an Energy Cost Associated With Assembling a Collection of Charges367

The Energy to Liberate Ions from Molecules Can Be Comparable to the Thermal Energy368

9.3.2 The Charged Life of a Protein369

9.3.3 The Notion of Screening：Electrostatics in Salty Solutions370

Ions in Solution Are Spatially Arranged to Shield Charged Molecules Such as DNA370

The Size of the Screening Cloud Is Determined by a Balance of Energy and Entropy of the Surrounding Ions371

9.3.4 The Poisson-Boltzmann Equation374

The Distribution of Screening Ions Can Be Found by Minimizing the Free Energy374

The Screening Charge Decays Exponentially Around Macromolecules in Solution376

9.3.5 Viruses as Charged Spheres377

9.4 SUMMARY AND CONCLUSION379

9.5 PROBLEMS380

9.6 FURTHER READING382

9.7 REFERENCES382

Chapter 10 Beam Theory：Architecture for Cells and Skeletons383

10.1 BEAMS ARE EVERYWHERE：FROM FLAGELLA TO THE CYTOSKELETON383

One-Dimensional Structural Elements Are the Basis of Much of Macromolecular and Cellular Architecture383

10.2 GEOMETRY AND ENERGETICS OF BEAM DEFORMATION385

10.2.1 Stretch，Bend，and Twist385

Beam Deformations Result in Stretching，Bending，and Twisting385

A Bent Beam Can Be Analyzed as a Collection of Stretched Beams385

The Energy Cost to Deform a Beam Is a Quadratic Function of the Strain387

10.2.2 Beam Theory and the Persistence Length：Stiffness is Relative389

Thermal Fluctuations Tend to Randomize the Orientation of Biological Polymers389

The Persistence Length Is the Length Over Which a Polymer Is Roughly Rigid390

The Persistence Length Characterizes the Correlations in the Tangent Vectors at Different Positions Along the Polymer390

The Persistence Length Is Obtained by Averaging Over All Configurations of the Polymer391

10.2.3 Elasticity and Entropy：The Worm-Like Chain392

The Worm-Like Chain Model Accounts for Both the Elastic Energy and Entropy of Polymer Chains392

10.3 THE MECHANICS OF TRANSCRIPTIONAL REGULATION：DNA LOOPING REDUX394

10.3.1 The Iac Operon and Other Looping Systems394

Transcriptional Regulation Can Be Effected by DNA Looping395

10.3.2 Energetics of DNA Looping395

10.3.3 Putting It All Together：The J-Factor396

10.4 DNA PACKING：FROM VIRUSES TO EUKARYOTES398

The Packing of DNA in Viruses and Cells Requires Enormous Volume Compaction398

10.4.1 The Problem of Viral DNA Packing400

Structural Biologists Have Determined the Structure of Many Parts in the Viral Parts List400

The Packing of DNA in Viruses Results in a Free-Energy Penalty402

A Simple Model of DNA Packing in Viruses Uses the Elastic Energy of Circular Hoops403

DNA Self-Interactions Are also Important in Establishing the Free Energy Associated with DNA Packing in Viruses404

DNA Packing in Viruses Is a Competition Between Elastic and Interaction Energies406

10.4.2 Constructing the Nucleosome407

Nucleosome Formation Involves Both Elastic Deformation and Interactions Between Histones and DNA408

10.4.3 Equilibrium Accessibility of Nucleosomal DNA409

The Equilibrium Accessibility of Sites within the Nucleosome Depends upon How Far They Are from the Unwrapped Ends409

10.5 THE CYTOSKELETON AND BEAM THEORY413

Eukaryotic Cells Are Threaded by Networks of Filaments413

10.5.1 The Cellular Interior：A Structural Perspective414

Prokaryotic Cells Have Proteins Analogous to the Eukaryotic Cytoskeleton416

10.5.2 Stiffness of Cytoskeletal Filaments416

The Cytoskeleton Can Be Viewed as a Collection of Elastic Beams416

10.5.3 Cytoskeletal Buckling419

A Beam Subject to a Large Enough Force Will Buckle419

10.5.4 Estimate of the Buckling Force420

Beam Buckling Occurs at Smaller Forces for Longer Beams420

10.6 SUMMARY AND CONCLUSIONS421

10.7 APPENDIX：THE MATHEMATICS OF THE WORM-LIKE CHAIN421

10.8 PROBLEMS424

10.9 FURTHER READING426

10.10 REFERENCES426

Chapter 11 Biological Membranes：Life in Two Dimensions427

11.1 THE NATURE OF BIOLOGICAL MEMBRANES427

11.1.1 Cells and Membranes427

Cells and Their Organelles Are Bound by Complex Membranes427

Electron Microscopy Provides a Window on Cellular Membrane Structures429

11.1.2 The Chemistry and Shape of Lipids431

Membranes Are Built from a Variety of Molecules That Have an Ambivalent Relationship with Water431

The Shapes of Lipid Molecules Can Induce Spontaneous Curvature on Membranes436

11.1.3 The Liveliness of Membranes436

Membrane Proteins Shuttle Mass Across Membranes437

Membrane Proteins Communicate Information Across Membranes439

Specialized Membrane Proteins Generate ATP439

Membrane Proteins Can Be Reconstituted in Vesicles439

11.2 ON THE SPRINGINESS OF MEMBRANES440

11.2.1 An Interlude on Membrane Geometry440

Membrane Stretching Geometry Can Be Described by a Simple Area Function441

Membrane Bending Geometry Can Be Described by a Simple Height Function，h（x，y）441

Membrane Compression Geometry Can Be Described by a Simple Thickness Function，w（x，y）444

Membrane Shearing Can Be Described by an Angle Variable，θ444

11.2.2 Free Energy of Membrane Deformation445

There Is a Free-Energy Penalty Associated with Changing the Area of a Lipid Bilayer445

There Is a Free-Energy Penalty Associated with Bending a Lipid Bilayer446

There Is a Free-Energy Penalty for Changing the Thickness of a Lipid Bilayer446

There Is an Energy Cost Associated with the Gaussian Curvature447

11.3 STRUCTURE，ENERGETICS，AND FUNCTION OF VESICLES448

11.3.1 Measuring Membrane Stiffness448

Membrane Elastic Properties Can Be Measured by Stretching Vesicles448

11.3.2 Membrane Pulling450

11.3.3 Vesicles in Cells453

Vesicles Are Used for a Variety of Cellular Transport Processes453

There Is a Fixed Free-Energy Cost Associated with Spherical Vesicles of All Sizes455

Vesicle Formation Is Assisted by Budding Proteins456

There Is an Energy Cost to Disassemble Coated Vesicles458

11.4 FUSION AND FISSION458

11.4.1 Pinching Vesicles：The Story of Dynamin459

11.5 MEMBRANES AND SHAPE462

11.5.1 The Shapes of Organelles462

The Surface Area of Membranes Due to Pleating Is So Large That Organelles Can Have Far More Area than the Plasma Membrane463

11.5.2 The Shapes of Cells465

The Equilibrium Shapes of Red Blood Cells Can Be Found by Minimizing the Free Energy466

11.6 THE ACTIVE MEMBRANE467

11.6.1 Mechanosensitive Ion Channels and Membrane Elasticity467

Mechanosensitive Ion Channels Respond to Membrane Tension467

11.6.2 Elastic Deformations of Membranes Produced by Proteins468

Proteins Induce Elastic Deformations in the Surrounding Membrane468

Protein-Induced Membrane Bending Has an Associated Free-Energy Cost469

11.6.3 One-Dimensional Solution for MscL470

Membrane Deformations Can Be Obtained by Minimizing the Membrane Free Energy470

The Membrane Surrounding a Channel Protein Produces a Line Tension472

11.7 SUMMARY AND CONCLUSIONS475

11.8 PROBLEMS476

11.9 FURTHER READING479

11.10 REFERENCES479

PART 3 LIFE IN MOTION481

Chapter 12 The Mathematics of Water483

12.1 PUTTING WATER IN ITS PLACE483

12.2 HYDRODYNAMICS OF WATER AND OTHER FLUIDS484

12.2.1 Water as a Continuum484

Though Fluids Are Composed of Molecules It Is Possible to Treat Them as a Continuous Medium484

12.2.2 What Can Newton Tell Us？485

Gradients in Fluid Velocity Lead to Shear Forces485

12.2.3 F= ma for Fluids486

12.2.4 The Newtonian Fluid and the Navier-Stokes Equations490

The Velocity of Fluids at Surfaces Is Zero491

12.3 THE RIVER WITHIN：FLUID DYNAMICS OF BLOOD491

12.3.1 Boats in the River：Leukocyte Rolling and Adhesion493

12.4 THE LOW REYNOLDS NUMBER WORLD495

12.4.1 Stokes Flow：Consider a Spherical Bacterium495

12.4.2 Stokes Drag in Single-Molecule Experiments498

Stokes Drag Is Irrelevant for Optical Tweezers Experiments498

12.4.3 Dissipative Time Scales and the Reynolds Number499

12.4.4 Fish Gotta Swim，Birds Gotta Fly，and Bacteria Gotta Swim Too500

Reciprocal Deformation of the Swimmer’s Body Does Not Lead to Net Motion at Low Reynolds Number502

12.4.5 Centrifugation and Sedimentation：Spin It Down502

12.5 SUMMARY AND CONCLUSIONS504

12.6 PROBLEMS505

12.7 FURTHER READING507

12.8 REFERENCES507

Chapter 13 A Statistical View of Biological Dynamics509

13.1 DIFFUSION IN THE CELL509

13.1.1 Active versus Passive Transport510

13.1.2 Biological Distances Measured in Diffusion Times511

The Time It Takes a Diffusing Molecule to Travel a Distance L Grows as the Square of the Distance512

Diffusion Is Not Effective Over Large Cellular Distances512

13.1.3 Random Walk Redux514

13.2 CONCENTRATION FIELDS AND DIFFUSIVE DYNAMICS515

Fick’s Law Tells Us How Mass Transport Currents Arise as a Result of Concentration Gradients517

The Diffusion Equation Results from Fick’s Law and Conservation of Mass518

13.2.1 Diffusion by Summing Over Microtrajectories518

13.2.2 Solutions and Properties of the Diffusion Equation524

Concentration Profiles Broaden Over Time in a Very Precise Way524

13.2.3 FRAP and FCS525

13.2.4 Drunks on a Hill：The Smoluchowski Equation529

13.2.5 The Einstein Relation530

13.3 DIFFUSION TO CAPTURE532

13.3.1 Modeling the Cell Signaling Problem532

Perfect Receptors Result in a Rate of Uptake 4πDcoa533

A Distribution of Receptors Is Almost as Good as a Perfectly Absorbing Sphere534

Real Receptors Are Not Always Uniformly Distributed536

13.3.2 A “Universal” Rate for Diffusion-Limited Chemical Reactions537

13.4 SUMMARY AND CONCLUSIONS538

13.5 PROBLEMS539

13.6 FURTHER READING540

13.7 REFERENCES540

Chapter 14 Life in Crowded and Disordered Environments543

14.1 CROWDING，LINKAGE，AND ENTANGLEMENT543

14.1.1 The Cell Is Crowded544

14.1.2 Macromolecular Networks：The Cytoskeleton and Beyond545

14.1.3 Crowding on Membranes546

14.1.4 Consequences of Crowding547

Crowding Alters Biochemical Equilibria548

Crowding Alters the Kinetics within Cells548

14.2 EQUILIBRIA IN CROWDED ENVIRONMENTS550

14.2.1 Crowding and Binding550

Lattice Models of Solution Provide a Simple Picture of the Role of Crowding in Biochemical Equilibria550

14.2.2 Osmotic Pressures in Crowded Solutions552

Osmotic Pressure Reveals Crowding Effects552

14.2.3 Depletion Forces：Order from Disorder554

The Close Approach of Large Particles Excludes Smaller Particles Between Them，Resulting in an Entropic Force554

Depletion Forces Can Induce Entropic Ordering！559

14.2.4 Excluded Volume and Polymers559

Excluded Volume Leads to an Effective Repulsion Between Molecules559

Self-avoidance Between the Monomers of a Polymer Leads to Polymer Swelling561

14.2.5 Case Study in Crowding：How to Make a Helix563

14.2.6 Crowding at Membranes565

14.3 CROWDED DYNAMICS566

14.3.1 Crowding and Reaction Rates566

Enzymatic Reactions in Cells Can Proceed Faster than the Diffusion Limit Using Substrate Channeling566

Protein Folding Is Facilitated by Chaperones567

14.3.2 Diffusion in Crowded Environments567

14.4 SUMMARY AND CONCLUSIONS569

14.5 PROBLEMS569

14.6 FURTHER READING570

14.7 REFERENCES571

Chapter 15 Rate Equations and Dynamics in the Cell573

15.1 BIOLOGICAL STATISTICAL DYNAMICS：A FIRST LOOK573

15.1.1 Cells as Chemical Factories574

15.1.2 Dynamics of the Cytoskeleton575

15.2 A CHEMICAL PICTURE OF BIOLOGICAL DYNAMICS579

15.2.1 The Rate Equation Paradigm579

Chemical Concentrations Vary in Both Space and Time580

Rate Equations Describe the Time Evolution of Concentrations580

15.2.2 All Good Things Must End581

Macromolecular Decay Can Be Described by a Simple，First-Order Differential Equation581

15.2.3 A Single-Molecule View of Degradation：Statistical Mechanics Over Trajectories582

Molecules Fall Apart with a Characteristic Lifetime582

Decay Processes Can Be Described with Two-State Trajectories583

Decay of One Species Corresponds to Growth in the Number of a Second Species585

15.2.4 Bimolecular Reactions586

Chemical Reactions Can Increase the Concentration of a Given Species586

Equilibrium Constants Have a Dynamical Interpretation in Terms of Reaction Rates588

15.2.5 Dynamics of Ion Channels as a Case Study589

Rate Equations for Ion Channels Characterize the Time Evolution of the Open and Closed Probability590

15.2.6 Rapid Equilibrium591

15.2.7 Michaelis-Menten and Enzyme Kinetics596

15.3 THE CYTOSKELETON IS ALWAYS UNDER CONSTRUCTION599

15.3.1 The Eukaryotic Cytoskeleton599

The Cytoskeleton Is a Dynamical Structure That Is Always Under Construction599

15.3.2 The Curious Case of the Bacterial Cytoskeleton600

15.4 SIMPLE MODELS OF CYTOSKELETAL POLYMERIZATION602

The Dynamics of Polymerization Can Involve Many Distinct Physical and Chemical Effects603

15.4.1 The Equilibrium Polymer604

Equilibrium Models of Cytoskeletal Filaments Describe the Distribution of Polymer Lengths for Simple Polymers604

An Equilibrium Polymer Fluctuates in Time606

15.4.2 Rate Equation Description of Cytoskeletal Polymerization609

Polymerization Reactions Can Be Described by Rate Equations609

The Time Evolution of the Probability Distribution Pn（t） Can Be Written Using a Rate Equation610

Rates of Addition and Removal of Monomers Are Often Different on the Two Ends of Cytoskeletal Filaments612

15.4.3 Nucleotide Hydrolysis and Cytoskeletal Polymerization614

ATP Hydrolysis Sculpts the Molecular Interface，Resulting in Distinct Rates at the Ends of Cytoskeletal Filaments614

15.4.4 Dynamic Instability：A Toy Model of the Cap615

A Toy Model of Dynamic Instability Assumes That Catastrophe Occurs When Hydrolyzed Nucleotides Are Present at the Growth Front616

15.5 SUMMARY AND CONCLUSIONS618

15.6 PROBLEMS619

15.7 FURTHER READING621

15.8 REFERENCES621

Chapter 16 Dynamics of Molecular Motors623

16.1 THE DYNAMICS OF MOLECULAR MOTORS：LIFE IN THE NOISY LANE623

16.1.1 Translational Motors：Beating the Diffusive Speed Limit625

The Motion of Eukaryotic Cilia and Flagella Is Driven by Translational Motors628

Muscle Contraction Is Mediated by Myosin Motors630

16.1.2 Rotary Motors634

16.1.3 Polymerization Motors：Pushing by Growing637

16.1.4 Translocation Motors：Pushing by Pulling638

16.2 RECTIFIED BROWNIAN MOTION AND MOLECULAR MOTORS639

16.2.1 The Random Walk Yet Again640

Molecular Motors Can Be Thought of as Random Walkers640

16.2.2 The One-State Model641

The Dynamics of a Molecular Motor Can Be Written Using a Master Equation642

The Driven Diff usion Equation Can Be Transformed into an Ordinary Diffusion Equation644

16.2.3 Motor Stepping from a Free-Energy Perspective647

16.2.4 The Two-State Model651

The Dynamics of a Two-State Motor Is Described by Two Coupled Rate Equations651

Internal States Reveal Themselves in the Form of the Waiting Time Distribution654

16.2.5 More General Motor Models656

16.2.6 Coordination of Motor Protein Activity658

16.2.7 Rotary Motors660

16.3 POLYMERIZATION AND TRANSLOCATION AS MOTOR ACTION663

16.3.1 The Polymerization Ratchet663

The Polymerization Ratchet Is Based on a Polymerization Reaction That Is Maintained Out of Equilibrium666

The Polymerization Ratchet Force -Velocity Can Be Obtained by Solving a Driven Diffusion Equation668

16.3.2 Force Generation by Growth670

Polymerization Forces Can Be Measured Directly670

Polymerization Forces Are Used to Center Cellular Structures672

16.3.3 The Translocation Ratchet673

Protein Binding Can Speed Up Translocation through a Ratcheting Mechanism674

The Translocation Time Can Be Estimated by Solving a Driven Diffusion Equation676

16.4 SUMMARY AND CONCLUSIONS677

16.5 PROBLEMS677

16.6 FURTHER READING679

16.7 REFERENCES679

Chapter 17 Biological Electricity and the Hodgkin-Huxley Model681

17.1 THE ROLE OF ELECTRICITY IN CELLS681

17.2 THE CHARGE STATE OF THE CELL682

17.2.1 The Electrical Status of Cells and Their Membranes682

17.2.2 Electrochemical Equilibrium and the Nernst Equation683

Ion Concentration Differences Across Membranes Lead to Potential Differences683

17.3 MEMBRANE PERMEABILITY：PUMPS AND CHANNELS685

A Nonequilibrium Charge Distribution Is Set Up Between the Cell Interior and the External World685

Signals in Cells Are Often Mediated by the Presence of Electrical Spikes Called Action Potentials686

17.3.1 Ion Channels and Membrane Permeability688

Ion Permeability Across Membranes Is Mediated by Ion Channels688

A Simple Two-State Model Can Describe Many of the Features of Voltage Gating of Ion Channels689

17.3.2 Maintaining a Nonequilibrium Charge State691

Ions Are Pumped Across the Cell Membrane Against an Electrochemical Gradient691

17.4 THE ACTION POTENTIAL693

17.4.1 Membrane Depolarization：The Membrane as a Bistable Switch693

Coordinated Muscle Contraction Depends Upon Membrane Depolarization694

A Patch of Cell Membrane Can Be Modeled as an Electrical Circuit696

The Difference Between the Membrane Potential and the Nernst Potential Leads to an Ionic Current Across the Cell Membrane698

Voltage-Gated Channels Result in a Nonlinear Current-Voltage Relation for the Cell Membrane699

A Patch of Membrane Acts as a Bistable Switch700

The Dynamics of Voltage Relaxation Can Be Modeled Using an RC Circuit702

17.4.2 The Cable Equation703

17.4.3 Depolarization Waves705

Waves of Membrane Depolarization Rely on Sodium Channels Switching into the Open State705

17.4.4 Spikes710

17.4.5 Hodgkin-Huxley and Membrane Transport712

Inactivation of Sodium Channels Leads to Propagating Spikes712

17.5 SUMMARY AND CONCLUSIONS714

17.6 PROBLEMS714

17.7 FURTHER READING715

17.8 REFERENCES715

Chapter 18 Light and Life717

18.1 INTRODUCTION718

18.2 PHOTOSYNTHESIS719

Organisms From All Three of the Great Domains of Life Perform Photosynthesis720

18.2.1 Quantum Mechanics for Biology724

Quantum Mechanical Kinematics Describes States of the System in Terms of Wave Functions725

Quantum Mechanical Observables Are Represented by Operators728

The Time Evolution of Quantum States Can Be Determined Using the Schrodinger Equation729

18.2.2 The Particle-in-a-Box Model730

Solutions for the Box of Finite Depth Do Not Vanish at the Box Edges731

18.2.3 Exciting Electrons With Light733

Absorption Wavelengths Depend Upon Molecular Size and Shape735

18.2.4 Moving Electrons From Hither to Yon737

Excited Electrons Can Suffer Multiple Fates737

Electron Transfer in Photosynthesis Proceeds by Tunneling739

Electron Transfer Between Donor and Acceptor Is Gated by Fluctuations of the Environment745

Resonant Transfer Processes in the Antenna Complex Efficiently Deliver Energy to the Reaction Center747

18.2.5 Bioenergetics of Photosynthesis748

Electrons Are Transferred from Donors to Acceptors Within and Around the Cell Membrane748

Water，Water Everywhere，and Not an Electron to Drink750

Charge Separation across Membranes Results in a Proton-Motive Force751

18.2.6 Making Sugar752

18.2.7 Destroying Sugar757

18.2.8 Photosynthesis in Perspective758

18.3 THE VISION THING759

18.3.1 Bacterial “Vision”760

18.3.2 Microbial Phototaxis and Manipulating Cells with Light763

18.3.3 Animal Vision763

There Is a Simple Relationship between Eye Geometry and Resolution765

The Resolution of Insect Eyes Is Governed by Both the Number of Ommatidia and Diffraction Effects768

The Light-Driven Conformational Change of Retinal Underlies Animal Vision769

Information from Photon Detection Is Amplified by a Signal Transduction Cascade in the Photoreceptor Cell773

The Vertebrate Visual System Is Capable of Detecting Single Photons776

18.3.4 Sex，Death，and Quantum Mechanics781

Let There Be Light：Chemical Reactions Can Be Used to Make Light784

18.4 SUMMARY AND CONCLUSIONS785

18.5 APPENDIX：SIMPLE MODEL OF ELECTRON TUNNELING785

18.6 PROBLEMS793

18.7 FURTHER READING795

18.8 REFERENCES796

PART 4 THE MEANING OF LIFE799

Chapter 19 Organization of Biological Networks801

19.1 CHEMICAL AND INFORMATIONAL ORGANIZATION IN THE CELL801

Many Chemical Reactions in the Cell are Linked in Complex Networks801

Genetic Networks Describe the Linkages Between Different Genes and Their Products802

Developmental Decisions Are Made by Regulating Genes802

Gene Expression Is Measured Quantitatively in Terms of How Much，When，and Where804

19.2 GENETIC NETWORKS：DOING THE RIGHT THING AT THE RIGHT TIME807

Promoter Occupancy Is Dictated by the Presence of Regulatory Proteins Called Transcription Factors808

19.2.1 The Molecular Implementation of Regulation：Promoters，Activators，and Repressors808

Repressor Molecules Are the Proteins That Implement Negative Control808

Activators Are the Proteins That Implement Positive Control809

Genes Can Be Regulated During Processes Other Than Transcription809

19.2.2 The Mathematics of Recruitment and Rejection810

Recruitment of Proteins Reflects Cooperativity Between Different DNA-Binding Proteins810

The Regulation Factor Dictates How the Bare RNA Polymerase Binding Probability Is Altered by Transcription Factors812

Activator Bypass Experiments Show That Activators Work by Recruitment813

Repressor Molecules Reduce the Probability Polymerase Will Bind to the Promoter814

19.2.3 Transcriptional Regulation by the Numbers：Binding Energies and Equilibrium Constants819

Equilibrium Constants Can Be Used To Determine Regulation Factors819

19.2.4 A Simple Statistical Mechanical Model of Positive and Negative Regulation820

19.2.5 The Iac Operon822

The Iac Operon Has Features of Both Negative and Positive Regulation822

The Free Energy of DNA Looping Affects the Repression of the Iac Operon824

Inducers Tune the Level of Regulatory Response829

19.2.6 Other Regulatory Architectures829

The Fold-Change for Different Regulatory Motifs Depends Upon Experimentally Accessible Control Parameters830

Quantitative Analysis of Gene Expression in Eukaryotes Can Also Be Analyzed Using Thermodynamic Models832

19.3 REGULATORY DYNAMICS835

19.3.1 The Dynamics of RNA Polymerase and the Promoter835

The Concentrations of Both RNA and Protein Can Be Described Using Rate Equations835

19.3.2 Dynamics of mRNA Distributions838

Unregulated Promoters Can Be Described By a Poisson Distribution841

19.3.3 Dynamics of Regulated Promoters843

The Two-State Promoter Has a Fano Factor Greater Than One844

Different Regulatory Architectures Have Different Fano Factors849

19.3.4 Dynamics of Protein Translation854

19.3.5 Genetic Switches：Natural and Synthetic861

19.3.6 Genetic Networks That Oscillate870

19.4 CELLULAR FAST RESPONSE：SIGNALING872

19.4.1 Bacterial Chemotaxis873

The MWC Model Can Be Used to Describe Bacterial Chemotaxis878

Precise Adaptation Can Be Described by a Simple Balance Between Methylation and Demethylation881

19.4.2 Biochemistry on a Leash883

Tethering Increases the Local Concentration of a Ligand884

Signaling Networks Help Cells Decide When and Where to Grow Their Actin Filaments for Motility884

Synthetic Signaling Networks Permit a Dissection of Signaling Pathways885

19.5 SUMMARY AND CONCLUSIONS888

19.6 PROBLEMS889

19.7 FURTHER READING891

19.8 REFERENCES892

Chapter 20 Biological Patterns：Order in Space and Time893

20.1 INTRODUCTION：MAKING PATTERNS893

20.1.1 Patterns in Space and Time894

20.1.2 Rules for Pattern-Making895

20.2 MORPHOGEN GRADIENTS896

20.2.1 The French Flag Model896

20.2.2 How the Fly Got His Stripes898

Bicoid Exhibits an Exponential Concentration Gradient Along the Anterior-Posterior Axis of Fly Embryos898

A Reaction-Diffusion Mechanism Can Give Rise to an Exponential Concentration Gradient899

20.2.3 Precision and Scaling905

20.2.4 Morphogen Patterning with Growth in Anabaena912

20.3 REACTION-DIFFUSION AND SPATIAL PATTERNS914

20.3.1 Putting Chemistry and Diffusion Together：Turing Patterns914

20.3.2 How Bacteria Lay Down a Coordinate System920

20.3.3 Phyllotaxis：The Art of Flower Arrangement926

20.4 TURNING TIME INTO SPACE：TEMPORAL OSCILLATIONS IN CELL FATE SPECIFICATION931

20.4.1 Somitogenesis932

20.4.2 Seashells Forming Patterns in Space and Time935

20.5 PATTERN FORMATION AS A CONTACT SPORT939

20.5.1 The Notch-Delta Concept939

20.5.2 Drosophila Eyes944

20.6 SUMMARY AND CONCLUSIONS947

20.7 PROBLEMS948

20.8 FURTHER READING949

20.9 REFERENCES950

Chapter 21 Sequences，Specificity，and Evolution951

21.1 BIOLOGICAL INFORMATION952

21.1.1 Why Sequences？953

21.1.2 Genomes and Sequences by the Numbers957

21.2 SEQUENCE ALIGNMENT AND HOMOLOGY960

Sequence Comparison Can Sometimes Reveal Deep Functional and Evolutionary Relationships Between Genes，Proteins，and Organisms961

21.2.1 The HP Model as a Coarse-Grained Model for Bioinformatics964

21.2.2 Scoring Success966

A Score Can Be Assigned to Different Alignments Between Sequences966

Comparison of Full Amino Acid Sequences Requires a 20-by-20 Scoring Matrix968

Even Random Sequences Have a Nonzero Score970

The Extreme Value Distribution Determines the Probability That a Given Alignment Score Would Be Found by Chance971

False Positives Increase as the Threshold for Acceptable Expect Values （also Called E-Values） Is Made Less Stringent973

Structural and Functional Similarity Do Not Always Guarantee Sequence Similarity976

21.3 THE POWER OF SEQUENCE GAZING976

21.3.1 Binding Probabilities and Sequence977

Position Weight Matrices Provide a Map Between Sequence and Binding Affinity978

Frequencies of Nucleotides at Sites Within a Sequence Can Be Used to Construct Position Weight Matrices979

21.3.2 Using Sequence to Find Binding Sites983

21.3.3 Do Nucleosomes Care About Their Positions on Genomes？988

DNA Sequencing Reveals Patterns of Nucleosome Occupancy on Genomes989

A Simple Model Based Upon Self-Avoidance Leads to a Prediction for Nucleosome Positioning990

21.4 SEQUENCES AND EVOLUTION993

21.4.1 Evolution by the Numbers：Hemoglobin and Rhodopsin as Case Studies in Sequence Alignment994

Sequence Similarity Is Used as a Temporal Yardstick to Determine Evolutionary Distances994

Modern-Day Sequences Can Be Used to Reconstruct the Past996

21.4.2 Evolution and Drug Resistance998

21.4.3 Viruses and Evolution1000

The Study of Sequence Makes It Possible to Trace the Evolutionary History of HIV1001

The Luria-Delbruck Experiment Reveals the Mathematics of Resistance1002

21.4.4 Phylogenetic Trees1008

21.5 THE MOLECULAR BASIS OF FIDELITY1010

21.5.1 Keeping It Specific：Beating Thermodynamic Specificity1011

The Specificity of Biological Recognition Often Far Exceeds the Limit Dictated by Free-Energy Differences1011

High Specificity Costs Energy1015

21.6 SUMMARY AND CONCLUSIONS1016

21.7 PROBLEMS1017

21.8 FURTHER READING1020

21.9 REFERENCES1021

Chapter 22 Whither Physical Biology？1023

22.1 DRAWING THE MAP TO SCALE1023

22.2 NAVIGATING WHEN THE MAP IS WRONG1027

22.3 INCREASING THE MAP RESOLUTION1028

22.4 “DIFFICULTIES ON THEORY”1030

Modeler’ s Fantasy1031

Is It Biologically Interesting？1031

Uses and Abuses of Statistical Mechanics1032

Out-of-Equilibrium and Dynamic1032

Uses and Abuses of Continuum Mechanics1032

Too Many Parameters1033

Missing Facts1033

Too Much Stuff1033

Too Little Stuff1034

The Myth of “THE” Cell1034

Not Enough Thinking1035

22.5 THE RHYME AND REASON OF IT ALL1035

22.6 FURTHER READING1036

22.7 REFERENCES1037

Index1039