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E-raamat: Systems Biology of Cell Signaling: Recurring Themes and Quantitative Models

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(Stanford University, USA)
  • Formaat: 284 pages
  • Ilmumisaeg: 28-Sep-2021
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781000430783
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Systems Biology of Cell Signaling: Recurring Themes and Quantitative ModelsSuurem pilt
  • Formaat: 284 pages
  • Ilmumisaeg: 28-Sep-2021
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781000430783
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This textbook aims to provide advanced students with the tools and insights needed to carry out studies of signal transduction drawing upon modeling, theory and experimentation. It seeks to provide quantitatively inclined biologists and biologically inclined physicists with the tools needed to apply modeling/theory to biological processes.



How can we understand the complexity of genes, RNAs, and proteins and the associated regulatory networks? One approach is to look for recurring types of dynamical behavior. Mathematical models prove to be useful, especially models coming from theories of biochemical reactions such as ordinary differential equation models. Clever, careful experiments test these models and their basis in specific theories. This textbook aims to provide advanced students with the tools and insights needed to carry out studies of signal transduction drawing upon modeling, theory, and experimentation. Early chapters summarize the basic building blocks of signaling systems: binding/dissociation, synthesis/destruction, and activation/inactivation. Subsequent chapters introduce various basic circuit devices: amplifiers, stabilizers, pulse generators, switches, stochastic spike generators, and oscillators. All chapters consistently use approaches and concepts from chemical kinetics and non-linear dynamics, including rate-balance analysis, phase plane analysis, nullclines, linear stability analysis, stable nodes, saddles, unstable nodes, stable and unstable spirals, and bifurcations. This textbook seeks to provide quantitatively inclined biologists and biologically inclined physicists with the tools and insights needed to apply modeling and theory to interesting biological processes.

Key Features

· Full-color illustration program with diagrams to help illuminate the concepts

· Enables the reader to apply modeling and theory to the biological processes

· Further Reading for each chapter

· High-quality figures available for instructors to download

Chapter 1 Introduction
1(20)
1.1 Signal Transducers Are Cellular Components That Act Mainly By Regulating Other Cellular Components
3(1)
1.2 The Signal Transduction Parts List Is Long
3(1)
1.3 Signal Transduction In Bacteria Is Accomplished By Short, (Mostly) Linear, (Mostly) Non-Interconnected Pathways
4(2)
1.4 The EGFR System Is Deep, Interconnected, And Complicated
6(5)
1.5 Complicated Systems Can Be Simplified By Assuming Modularity
11(2)
1.6 Ordinary Differential Equations Provide A Powerful Framework For Understanding Many Signaling Processes
13(1)
1.7 Theory Can Help Highlight The Commonalities Of Diverse Biological Phenomena
14(1)
1.8 Six Basic Types Of Response Are Seen Over And Over Again In Cell Signaling
15(2)
1.9 Five Or Six Basic Circuit Motifs Are Seen Over And Over Again In Signaling Systems
17(4)
Summary
19(1)
Moving Forward
19(1)
Further Reading
20(1)
Chapter 2 Receptors 1: Monomeric Receptors And Ligands
21(18)
2.1 The β-Adrenergic Receptor Can Function As A Monomeric Receptor That Binds Monomeric Ligands
22(1)
2.2 Experiments Show The Receptor's Equilibrium And Dynamical Behaviors
23(1)
2.3 A Simple Binding-Dissociation Model Explains The Hyperbolic Equilibrium Response
24(3)
2.4 A Semilog Plot Expands The Range But Distorts The Graded Character Of The Response
27(1)
2.5 The System Approaches Equilibrium Exponentially
28(2)
2.6 Increasing The Association Rate Decreases F1/2; So Does Increasing The Dissociation Rate
30(1)
2.7 Going Up Is Faster Than Coming Down
31(1)
2.8 The Dissociation Rate Constant Determines The Half-Life And Mean Lifetime Of A Ligand-Receptor Complex
32(1)
2.9 Partial Agonists, Antagonists, And Inverse Agonists Can Be Explained By Assuming That Binding And Activation Occur In Distinct Steps
33(6)
Summary
37(1)
Further Reading
37(2)
Chapter 3 Receptors 2: Multimeric Receptors And Cooperativity
39(22)
Introduction
40(1)
3.1 The Hill Equation Is A Simple Expression For The Equilibrium Binding Of Ligand Molecules To An Oligomeric Receptor
41(1)
3.2 The Hill Exponent Is A Measure Of How Switch-Like A Sigmoidal Response Is
42(2)
3.3 The Hill Equation Accounts For Hemoglobin's Oxygen Binding Pretty Well, But The Assumptions Underpinning The Model Are Dubious
44(1)
3.4 The More-Plausible Monod-Wyman-Changeux (MWC) Model Yields Sigmoidal Binding Curves
44(6)
3.5 The MWC Model Accounts For The Binding Of Oxygen To Hemoglobin, But Not The Binding Of EGF To The EGFR
50(2)
3.6 The KNF Model Can Account For Either Ultrasensitive Or Subsensitive Binding
52(2)
3.7 Response Sensitivity Is Customarily Defined In Fold-Change Terms
54(2)
3.8 The Relationship Between Binding And Activation Yields A Variety Of Possible Responses
56(5)
Summary
59(1)
Further Reading
60(1)
Chapter 4 Downstream Signaling 1: Stoichiometric Regulation
61(14)
Stoichiometric Regulation Inside The Cell
63(1)
4.1 In The High-Affinity Limit, Does A Hyperbolic Response Make Intuitive Sense?
63(1)
4.2 The Equilibrium Response Changes From Hyperbolic To Linear When Depletion Of The Upstream Regulator Is Not Negligible
63(2)
4.3 The Dynamical Response Is Similar Even When The Depletion Of The Upstream Regulator Is Not Negligible
65(1)
4.4 Ligand Depletion Plus Negative Cooperativity Can Produce A Threshold
66(3)
4.5 Stoichiometric Regulators Must Sometimes Compete With Stoichiometric Inhibitors
69(6)
Summary
72(1)
Further Reading
72(3)
Chapter 5 Downstream Signaling 2: Covalent Modification
75(30)
5.1 A Mass Action Phosphorylation-Dephosphorylation Cycle Yields A Michaelian Steady-State Response With Exponential Approach To The Steady State
77(1)
5.2 The Steady-State Response Of A Phosphorylation-Dephosphorylation Reaction With Michaelis-Menten Kinetics Can Be Ultrasensitive
78(2)
5.3 Rate-Balance Plots Are Much Like The Economist's Supply-And-Demand Plots
80(1)
5.4 Rate-Balance Analysis Explains The Michaelian Steady-State Response
81(2)
5.5 The Dynamics Of The System Can Also Be Understood From The Rate-Balance Plot
83(1)
5.6 Rate-Balance Analysis Helps Explain Zero-Order Ultrasensitive
84(2)
5.7 Does Zero-Order Ultrasensitive Occur In Vivo?
86(1)
5.8 The Temporal Dynamics Of A Multistep Activation Process Tells You The Number Of Partially Rate-Determining Steps
87(2)
5.9 Assuming Mass Action Kinetics, Steady-State Multisite Phosphorylation Is Described By A Knf-Type Equation
89(3)
5.10 Priming Can Impart Positive Cooperativity On Multisite Phosphorylation
92(1)
5.11 Distributive Multisite Phosphorylation Improves Signaling Specificity
93(3)
5.12 Inessential Phosphorylation Sites Can Contribute To The Ultrasensitive
96(2)
5.13 Inessential Binding Sites Can Contribute To Ultrasensitive Receptor Activation
98(1)
5.14 Variation: Coherent Feed-Forward Regulation
99(2)
5.15 Variation: Reciprocal Regulation
101(4)
Summary
102(1)
Further Reading
103(2)
Chapter 6 Downstream Signaling 3: Regulated Production Or Destruction
105(8)
6.1 Stimulated Production Yields A Linear Steady-State Response With Exponential Approach To The Steady State
105(3)
6.2 The Stability Of The Steady State Can Be Quantified By The Exponent In The Exponential Approach Equation
108(1)
6.3 Saturating The Back Reaction Builds A Threshold Into The Steady-State Response
109(1)
6.4 Zero-Order Degradation Makes Drug Dosing Dicey
110(3)
Summary
112(1)
Further Reading
112(1)
Chapter 7 Cascades And Amplification
113(16)
Introduction
113(1)
7.1 Cascades Can Deliver Signals Faster Than Single Signal Transducers
114(3)
7.2 A Cascade Of Michaelian Responses Leads To Signal Degradation
117(2)
7.3 Fold-Sensitivity Decreases As A Signal Descends A Cascade Of Michaelian Responses
119(1)
7.4 Ultrasensitive Can Restore Or Increase The Decisiveness Of A Signal
120(5)
7.5 In Xenopus Oocyte Extracts, Responses Get More Ultrasensitive As The Mapk Cascade Is Descended
125(4)
Summary
126(1)
Further Reading
127(2)
Chapter 8 Bistability 1: Systems With One Time-Dependent Variable
129(18)
8.1 Cell Fate Induction Is Typically All-Or-None And Irreversible In Character
130(1)
8.2 Xenopus Oocyte Maturation Is An All-Or-None. Irreversible Process
131(1)
8.3 The Response Of Erk2 Is All-Or-None In Character
132(1)
8.4 There Is Positive Feedback In The Oocyte's Mapk Cascade
133(1)
8.5 The Response Of Erk2 To Progesterone Is Normally Irreversible
133(1)
8.6 The Mos/Erk2 System Can Be Reduced To A Model With A Single Time-Dependent Variable Because Of A Separation Of Time Scales
134(1)
8.7 Rate-Balance Analysis Shows What Is Required For A Bistable Response
135(2)
8.8 Increasing The Progesterone Concentration Pushes The System Through A Saddle-Node Bifurcation
137(3)
8.9 Tweaking The Model Can Change An Irreversible Response To A Hysteretic One
140(1)
8.10 The Dynamics Of The System Can Be Inferred From The Rate-Balance Plot
141(1)
8.11 The Velocity Vector Field Can Be Represented As A Potential Landscape
142(5)
Summary
144(1)
Further Reading
145(2)
Chapter 9 Bistability 2: Systems With Two Time-Dependent Variables
147(16)
9.1 Two-Variable Positive Feedback And Double-Negative Feedback Loops Can Function As Bistable Switches
148(2)
9.2 Linear Stability Analysis Explains The Dynamics Of The System Near Each Of The Steady States
150(2)
9.3 To Apply Linear Stability Analysis To A Two-Variable System, We Calculate Eigenvectors And Eigenvalues
152(3)
9.4 The System Can Change Between States Via A Saddle-Node Bifurcation
155(1)
9.5 Double-Negative Feedback Plus Ultrasensitive Can Yield Bistability
156(2)
9.6 Perfect Symmetry Can Produce A Pitchfork Bifurcation
158(2)
9.7 In The Absence Of Perfect Symmetry, A Pitchfork Bifurcation Morphs Into A Saddle-Node Bifurcation
160(3)
Summary
160(1)
Further Reading
161(2)
Chapter 10 Transcritical Bifurcations In Phase Separation And Infectious Disease
163(16)
Introduction
164(1)
10.1 Liquid-Liquid Phase Separation Can Produce Discrete Functional Domains That Lack Membranes
164(1)
10.2 Phase Separation Can Be Modeled By A Single Rate Equation With Positive Feedback And A Transcritical Bifurcation
165(3)
10.3 The Time Course Of Droplet Formation Is Sigmoidal
168(1)
10.4 The Same Principles Underpin The Formation Of Phospholipid Vesicles
169(1)
10.5 The Sir (Susceptible-Infected-Recovered) Model Explains Why Infectious Diseases Sometimes Spread Explosively
169(1)
10.6 The Sir Model Predicts Exponential Growth Followed By Exponential Decay
170(2)
10.7 The Basic Reproduction Number R0 Determines Whether An Infection Will Grow Exponentially
172(1)
10.8 The Proportion Of The Population That Will Ultimately Become Infected Depends On R0
173(2)
10.9 Manipulating R0 Can Delay An Epidemic, Decrease The Peak, And Diminish The Final Number Of Infected Individuals
175(4)
Summary
176(1)
Further Reading
177(2)
Chapter 11 Negative Feedback 1: Stability And Speed
179(6)
Introduction
179(1)
11.1 Negative Feedback Can Increase The Stability Of A Steady State
179(3)
11.2 Negative Feedback Can Allow A System To Respond More Quickly
182(3)
Further Reading
183(2)
Chapter 12 Negative
Feedback 2 Adaptation
185(1)
Introduction
185(1)
12.1 Bacteria Find Food Sources Through A Biased Random Walk
186(1)
12.2 Bacteria Suppress Tumbling In Response To Chemoattractants And Then Adapt Perfectly
187(1)
12.3 A Plausible Negative Feedback Model Can Account For Perfect Adaptation
188(4)
12.4 The Response Of The Erk Map Kinases To Mitogenic Signals Is Typically Transitory
192(1)
12.5 Delayed Negative Feedback Can Yield Near-Perfect Adaptation
193(2)
12.6 Ultrasensitive In The Feedback Loop Improves The System's Adaptation
195(1)
12.7 Induction Of Immediate-Early Gene Products Is Not Required For Erk Inactivation In Many Cell Types
196(3)
Summary
197(1)
Further Reading
198(1)
Chapter 13 Adaptation 2: Incoherent Feedforward Regulation And State-Dependent Inactivation
199(14)
Introduction
200(1)
13.1 Receptor Tyrosine Kinase Activation Is Followed By Transitory Ras Activation
200(1)
13.2 The Sequential Recruitment Of Sos And Gap To The Egfr Can Be Viewed As Incoherent Feedforward Regulation
201(1)
13.3 Incoherent Feedforward Systems Can Yield Perfect Adaptation
202(1)
13.4 Strict Ordering Of Sos And Gap Binding To The Egfr Is Not Required For Perfect Adaptation
203(2)
13.5 The Voltage-Sensitive Sodium Channel Also Undergoes Sequential Activation And Inactivation
205(3)
13.6 EGFR Internalization Can Be Viewed As State-Dependent Inactivation
208(1)
13.7 GPCR Signaling Is Switched From G-Proteins To (J-Arrestin Via A Mechanism Akin To State-Dependent Inactivation
209(4)
Summary
210(1)
Further Reading
211(2)
Chapter 14 Negative
Feedback 3 Oscillations
213(1)
Introduction
213(1)
14.1 Biological Oscillations Control Myriad Aspects Of Life And Operate Over A Ten-Billion-Fold Range Of Time Scales
214(1)
14.2 The Goodwin Oscillator Is Built Upon A Three-Tier Cascade With Highly Ultrasensitive Negative Feedback
214(3)
14.3 Linear Stability Analysis Yields A Pair Of Complex Eigenvalues
217(3)
14.4 Oscillations Are Born And Extinguished At HOPF Bifurcations
220(1)
14.5 Simple Harmonic Oscillators Are Not Limit Cycle Oscillators
221(4)
Summary
223(1)
Further Reading
224(1)
Chapter 15 Relaxation Oscillators
225(22)
Introduction
226(1)
15.1 The Xenopus Embryonic Cell Cycle Is Driven By A Reliable Biochemical Oscillator
226(2)
15.2 The Cell Cycle Oscillator Includes A Negative Feedback Loop And A Bistable Trigger
228(2)
15.3 A Simplified Model Captures The Basic Dynamics Of The Cell Cycle Oscillator
230(2)
15.4 The Cell Cycle Model Has A Single Unstable Steady State
232(3)
15.5 Tuning The Oscillator Changes The Period More Than The Amplitude
235(1)
15.6 Phase Plane Analysis Shows Why The HOPF Bifurcations Occur Where They Do
236(3)
15.7 Interlinked Positive And Double-Negative Feedback Loops Can Make The Mitotic Trigger More All-Or-None And More Robust
239(1)
15.8 The Fitzhugh-Nagumo Model Accounts For The Electrical Oscillations Of The Sinoatrial Node
240(1)
15.9 The Fitzhugh-Nagumo Model Consists Of A Quick Bistable Switch And A Slower Negative Feedback Loop
241(1)
15.10 The Cell Cycle Oscillator And The Fitzhugh-Nagumo Oscillator Share The Same Systems-Level Logic
242(1)
15.11 Depletion Can Take The Place Of Negative Feedback In A Relaxation Oscillator
243(4)
Summary
245(1)
Further Reading
246(1)
Chapter 16 Excitability
247(8)
Introduction
247(1)
16.1 The Receptor Tyrosine Kinase/Map Kinase System Includes Multiple Positive And Negative Feedback Loops
248(1)
16.2 Excitable Responses Can Be Generated By A Fast Positive Feedback Loop Coupled To A Slow Negative Feedback Loop
249(3)
16.3 Noise Can Cause An Excitable System To Fire Sporadically
252(3)
Summary
253(1)
Further Reading
253(2)
Chapter 17 Wrap-Up
255(2)
17.1 The Building Blocks
255(1)
17.2 Motifs
255(1)
17.3 Signal Processors
256(1)
17.4 Nonlinear Dynamics
256(1)
Glossary 257(6)
Index 263
James E. Ferrell, Jr., MD, PhD is Professor of Chemical and Systems Biology and Professor of Biochemistry at Stanford. His work, which makes use of quantitative experimental approaches, modeling and theory, looks to understand the design principles of biochemical switches, timers, and oscillators, especially those that control the cell cycle.
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