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Introduction to Systems Biology: Design Principles of Biological Circuits 2nd edition [Minkštas viršelis]

4.37/5 (173 ratings by Goodreads)
  • Formatas: Paperback / softback, 342 pages, aukštis x plotis: 254x178 mm, weight: 720 g, 5 Tables, black and white; 54 Line drawings, color; 193 Line drawings, black and white; 1 Halftones, color
  • Serija: Chapman & Hall/CRC Computational Biology Series
  • Išleidimo metai: 01-Aug-2019
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1439837171
  • ISBN-13: 9781439837177
Kitos knygos pagal šią temą:
Introduction to Systems Biology: Design Principles of Biological Circuits 2nd editionDidesnis paveikslėlis
  • Formatas: Paperback / softback, 342 pages, aukštis x plotis: 254x178 mm, weight: 720 g, 5 Tables, black and white; 54 Line drawings, color; 193 Line drawings, black and white; 1 Halftones, color
  • Serija: Chapman & Hall/CRC Computational Biology Series
  • Išleidimo metai: 01-Aug-2019
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1439837171
  • ISBN-13: 9781439837177
Kitos knygos pagal šią temą:
Kitos knygos iš šio išskirtinio pasiūlymo: Taylor & Francis siūlo daug technologijos knygų ISE (International Student Edition) kainomis
Pastovi nuoroda: https://www.kriso.lt/db/9781439837177.html
Raktažodžiai:
Praise for the first edition:

superb, beautifully written and organized work that takes an engineering approach to systems biology. Alon provides nicely written appendices to explain the basic mathematical and biological concepts clearly and succinctly without interfering with the main text. He starts with a mathematical description of transcriptional activation and then describes some basic transcription-network motifs (patterns) that can be combined to form larger networks. Nature

[ This text deserves] serious attention from any quantitative scientist who hopes to learn about modern biology It assumes no prior knowledge of or even interest in biology One final aspect that must be mentioned is the wonderful set of exercises that accompany each chapter. Alons book should become a standard part of the training of graduate students. Physics Today

Written for students and researchers, the second edition of this best-selling textbook continues to offer a clear presentation of design principles that govern the structure and behavior of biological systems. It highlights simple, recurring circuit elements that make up the regulation of cells and tissues. Rigorously classroom-tested, this edition includes new chapters on exciting advances made in the last decade.

Features:





Includes seven new chapters The new edition has 189 exercises, the previous edition had 66 Offers new examples relevant to human physiology and disease

The book website including course videos can be found here: https://www.weizmann.ac.il/mcb/UriAlon/introduction-systems-biology-design-principles-biological-circuits.

Recenzijos

"Systems biology is based on the idea that engineered and evolved systems share common principles. Here, Alon (Weizmann Inst. of Science, Rehovot) elucidates three of the major principles... This book is a compendium of many different experiments. Together, they show that biological systems do obey these design principles." --P. Cull, Oregon State University, CHOICE connect (57:5, Jan 2020)

"A very good book. Very well written, everything is clearly illustrated and presented. It makes a tough subject easy to follow." --Radu Angelescu, Senior Programmer at Ubisoft

"Alons book is the ideal counterargument to the idea that organisms are inherently human-opaque: it directly demonstrates the human-understandable structures which comprise real biological systems." --LessWrong.com

Praise for the First Edition

"[ This text deserves] serious attention from any quantitative scientist or physicist who hopes to learn about modern biology. the author succeeds in explaining in an intellectually exciting way what the cell does and what degrees of freedom enable it to function. He draws the detailed strands together into an appealing and inspiring overview of biology. Alons book should become a standard part of the training of graduate students in biological physics ." Nigel Goldenfeld, University of Illinois at Urbana-Champaign, Physics Today, June 2007

"a superb, beautifully written and organized work that takes an engineering approach to systems biology. He does an excellent job of explaining and motivating a useful toolbox of engineering models and methods using network-based controls. a valuable and non-overlapping addition to a systems-biology curriculum." Eric Werner, University of Oxford, Nature, Vol. 446, No. 29, March 2007

"I read Uri Alons elegant book almost without stopping for breath. He perceives and explains so many simple regularities, so clearly, that the novice reading this book can move on immediately to research literature, armed with a grasp of the many connections between diverse phenomena." Philip Nelson, University of Pennsylvania, Philadelphia, USA

" Beyond simply recounting recent results, Alon boldly articulates the basic principles underlying biological circuitry at different levels and shows how powerful they can be in understanding the complexity of living cells. For anyone who wants to understand how a living cell works, but thought they never would, this book is essential." Michael B. Elowitz, California Institute of Technology, Pasadena, USA

"Uri Alon offers a highly original perspective on systems biology, emphasizing the function of certain simple networks that appear as ubiquitous building blocks of living matter. " Boris Shraiman, University of California, Santa Barbara, USA

"This is a remarkable book that introduces not only a field but a way of thinking. Uri Alon describes in an elegant, simple way how principles such as stability, robustness and optimal design can be used to analyze and understand the evolution and behavior of living organisms. Alons clear intuitive language and helpful examples offer even to a mathematically naive reader deep mathematical insights into biology. The community has been waiting for this book; it was worth the wait." Galit Lahav, Harvard Medical School, Boston, Massachusetts, USA

Introduction xv
Part 1 Network Motifs
Chapter 1 Transcription Networks: Basic Concepts
3(18)
1.1 Introduction
3(1)
1.2 The Cognitive Problem of the Cell
3(2)
1.3 Elements of Transcription Networks
5(8)
1.3.1 Separation of Timescales
7(1)
1.3.2 The Signs on the Arrows: Activators and Repressors
8(1)
1.3.3 The Numbers on the Arrows: Input Functions
9(1)
1.3.4 Logic Input Functions: A Simple Framework for Understanding Network Dynamics
10(1)
1.3.5 Multi-Dimensional Input Functions Govern Genes with Several Inputs
11(2)
1.4 Dynamics and Response Time of Simple Regulation
13(2)
1.4.1 The Response Time of Stable Proteins Is One Cell Generation
15(1)
Further Reading
15(1)
Exercises
16(2)
Bibliography
18(3)
Chapter 2 Autoregulation: A Network Motif
21(16)
2.1 Introduction
21(1)
2.2 Patterns, Randomized Networks and Network Motifs
21(2)
2.2.1 Detecting Network Motifs by Comparison to Randomized Networks
23(1)
2.3 Autoregulation Is a Network Motif
23(1)
2.4 Negative Autoregulation Speeds the Response Time of Gene Circuits
24(5)
2.4.1 Rate Analysis Shows Speedup for Any Repressive Input Function f(X)
28(1)
2.5 Negative Autoregulation Promotes Robustness to Fluctuations in Production Rate
29(1)
2.6 Summary: Evolution as an Engineer
30(1)
Further Reading
31(1)
Exercises
31(5)
Bibliography
36(1)
Chapter 3 The Feedforward Loop Network Motif
37(24)
3.1 Introduction
37(1)
3.2 The Feedforward Loop Is a Network Motif
37(2)
3.3 The Structure of the Feedforward Loop Gene Circuit
39(2)
3.4 Dynamics of the Coherent Type-1 FFL with AND Logic
41(1)
3.5 The C1-FFL Is a Sign-Sensitive Delay Element
42(4)
3.5.1 Delay Following an ON Step of Sx
43(1)
3.5.2 No Delay Following an OFF Step of Sx
44(1)
3.5.3 The C1-FFL Is a Sign-Sensitive Delay Element
44(1)
3.5.4 Sign-Sensitive Delay Can Protect against Brief Input Fluctuations
44(1)
3.5.5 Sign-Sensitive Delay in the Arabinose System of E. coil
45(1)
3.6 OR-Gate C1-FFL Is a Sign-Sensitive Delay for OFF Steps
46(1)
3.7 The Incoherent Type-1 FFL Generates Pulses of Output
47(4)
3.7.1 The Incoherent FFL Can Speed Response Times
48(1)
3.7.2 Interim Summary: Three Ways to Speed Your Response Time
49(1)
3.7.3 The I1-FFL Can Provide Biphasic Steady-State Response Curves
50(1)
3.8 The Other Six FFL Types Can Also Act as Filters and Pulse Generators
51(1)
3.9 Convergent Evolution of FFLs
51(1)
3.10 Summary
52(1)
Further Reading
52(1)
Exercises
53(6)
Bibliography
59(2)
Chapter 4 Temporal Programs and the Global Structure of Transcription Networks
61(16)
4.1 Introduction
61(1)
4.2 The Single-Input Module (SIM) Network Motif
61(1)
4.3 The SIM Can Generate Temporal Gene Expression Programs
62(2)
4.4 The Multi-Output Feedforward Loop
64(2)
4.5 The Multi-Output FFL Can Generate FIFO Temporal Programs
66(2)
4.5.1 The Multi-Output FFL Also Acts as a Persistence Detector for Each Output
68(1)
4.6 Signal Integration by Bi-Fans and Dense-Overlapping Regulons
68(2)
4.7 Network Motifs and the Global Structure of Sensory Transcription Networks
70(1)
4.8 Interlocked Feedforward Loops in the B. subtilis Sporulation Network
70(3)
Further Reading
73(1)
Exercises
73(2)
Bibliography
75(2)
Chapter 5 Positive Feedback, Bistability and Memory
77(20)
5.1 Network Motifs in Developmental Transcription Networks
77(8)
5.1.1 Positive Autoregulation Slows Responses and Can Lead to Bistability
78(2)
5.1.2 Two-Node Positive Feedback Loops for Decision-Making
80(3)
5.1.3 Regulating Feedback and Regulated Feedback
83(1)
5.1.4 Long Transcription Cascades and Developmental Timing
84(1)
5.2 Network Motifs in Protein-Protein Interaction Networks
85(3)
5.2.1 Hybrid Network Motifs Include a Two-Node Negative Feedback Loop
85(2)
5.2.2 Hybrid FFL Motifs Can Provide Transient Memory
87(1)
5.2.3 Feedforward Loops Show a Milder Version of the Functions of Feedback Loops
87(1)
5.3 Network Motifs in Neuronal Networks
88(3)
5.3.1 Multi-Input FFLs in Neuronal Networks
89(2)
5.4 Reflection
91(1)
Further Reading
91(1)
Exercises
92(2)
Bibliography
94(3)
Chapter 6 How to Build a Biological Oscillator
97(20)
6.1 Oscillations Require Negative Feedback and Delay
97(4)
6.1.1 In Order to Oscillate, You Need to Add a Sizable Delay to the Negative Feedback Loop
97(4)
6.2 Noise Can Induce Oscillations in Systems That Have Only Damped Oscillations on Paper
101(1)
6.3 Delay Oscillators
102(1)
6.4 Many Biological Oscillators Have a Coupled Positive and Negative Feedback Loop Motif
103(4)
6.5 Robust Bistability Using Two Positive Feedback Loops
107(3)
Further Reading
110(1)
Exercises
110(2)
Bibliography
112(5)
Part 2 Robustness
Chapter 7 Kinetic Proofreading and Conformational Proofreading
117(20)
7.1 Introduction
117(1)
7.2 Kinetic Proofreading of the Genetic Code Can Reduce Error Rates
118(5)
7.2.1 Equilibrium Binding Cannot Explain the Precision of Translation
119(2)
7.2.2 Kinetic Proofreading Can Dramatically Reduce the Error Rate
121(2)
7.3 Recognition of Self and Non-Self by the Immune System
123(4)
7.3.1 Equilibrium Binding Cannot Explain the Low Error Rate of Immune Recognition
124(1)
7.3.2 Kinetic Proofreading Increases Fidelity of T-Cell Recognition
125(2)
7.4 Kinetic Proofreading Occurs in Diverse Processes in the Cell
127(1)
7.5 Conformational Proofreading Provides Specificity without Consuming Energy
128(1)
7.6 Demand Rules for Gene Regulation Can Minimize Errors
129(1)
Further Reading
130(1)
Exercises
131(5)
Bibliography
136(1)
Chapter 8 Robust Signaling by Bifunctional Components
137(16)
8.1 Robust Input-Output Curves
137(1)
8.2 Simple Signaling Circuits Are Not Robust
138(2)
8.3 Bacterial Two-Component Systems Can Achieve Robustness
140(4)
8.3.1 Limits of Robustness
143(1)
8.3.2 Remarks on the Black-Box Approach
143(1)
8.3.3 Bifunctional Components Provide Robustness in Diverse Circuits
144(1)
Further Reading
144(1)
Exercises
145(5)
Bibliography
150(3)
Chapter 9 Robustness in Bacterial Chemotaxis
153(22)
9.1 Introduction
153(1)
9.2 Bacterial Chemotaxis, or How Bacteria Think
153(3)
9.2.1 Chemotaxis Behavior
153(2)
9.2.2 Response and Exact Adaptation
155(1)
9.3 The Chemotaxis Protein Circuit
156(3)
9.3.1 Attractants Lower the Activity of X
157(1)
9.3.2 Adaptation Is Due to Slow Modification of X That Increases Its Activity
158(1)
9.4 The Barkai-Leibler Model of Exact Adaptation
159(6)
9.4.1 Robust Adaptation and Integral Feedback
162(2)
9.4.2 Experiments Show That Exact Adaptation Is Robust, Whereas Steady-State Activity and Adaptation Times Are Fine-Tuned
164(1)
9.5 Individuality and Robustness in Bacterial Chemotaxis
165(1)
Further Reading
166(1)
Exercises
166(6)
Bibliography
172(3)
Chapter 10 Fold-Change Detection
175(16)
10.1 Universal Features of Sensory Systems
175(1)
10.2 Fold-Change Detection in Bacterial Chemotaxis
176(4)
10.2.1 Definition of Fold-Change Detection (FCD)
177(1)
10.2.2 The Chemotaxis Circuit Provides FCD by Means of a Nonlinear Integral-Feedback Loop
178(2)
10.3 FCD and Exact Adaptation
180(1)
10.4 The Incoherent Feedforward Loop Can Show FCD
180(2)
10.5 A General Condition for FCD
182(1)
10.6 Identifying FCD Circuits from Dynamic Measurements
183(1)
10.7 FCD Provides Robustness to Input Noise and Allows Scale- Invariant Searches
184(2)
Further Reading
186(1)
Exercises
186(2)
References
188(3)
Chapter 11 Dynamical Compensation and Mutant Resistance in Tissues
191(18)
11.1 The Insulin-Glucose Feedback Loop
191(2)
11.2 The Minimal Model Is Not Robust to Changes in Insulin Sensitivity
193(1)
11.3 A Slow Feedback Loop on Beta-Cell Numbers Provides Compensation
194(3)
11.4 Dynamical Compensation Allows the Circuit to Buffer Parameter Variations
197(3)
11.5 Type 2 Diabetes Is Linked with Instability Due to a U-Shaped Death Curve
200(1)
11.6 Tissue-Level Feedback Loops Are Fragile to Invasion by Mutants That Misread the Signal
201(1)
11.7 Biphasic (U-Shaped) Response Curves Can Protect against Mutant Takeover
202(1)
11.8 Summary
203(1)
Further Reading
204(1)
Exercises
204(3)
Bibliography
207(2)
Chapter 12 Robust Spatial Patterning in Development
209(18)
12.1 The French Flag Model Is Not Robust
210(2)
12.2 Increased Robustness by Self-Enhanced Morphogen Degradation
212(2)
12.3 Network Motifs That Provide Degradation Feedback for Robust Patterning
214(1)
12.4 The Robustness Principle Can Distinguish between Mechanisms of Fruit Fly Patterning
215(5)
Further Reading
220(1)
Exercises
220(3)
Bibliography
223(4)
Part 3 Optimality
Chapter 13 Optimal Gene Circuit Design
227(22)
13.1 Introduction
227(1)
13.2 Optimal Expression Level of a Protein under Constant Conditions
228(6)
13.2.1 Cost of the LacZ Protein
229(1)
13.2.2 The Benefit of the LacZ Protein
230(1)
13.2.3 Fitness Function and the Optimal Expression Level
231(1)
13.2.4 Cells Reach Optimal LacZ Levels in a Few Hundred Generations in Laboratory Evolution Experiments
232(2)
13.3 To Regulate or Not to Regulate? Optimal Regulation in Changing Environments
234(2)
13.4 Environmental Selection of the Feedforward Loop Network Motif
236(2)
13.5 Inverse Ecology
238(1)
Further Reading
239(1)
Exercises
239(8)
Bibliography
247(2)
Chapter 14 Multi-Objective Optimality in Biology
249(24)
14.1 Introduction
249(1)
14.2 The Fitness Landscape Picture for a Single Task
249(1)
14.3 Multiple Tasks Are Characterized by Performance Functions
250(1)
14.4 Pareto Optimality in Performance Space
251(1)
14.5 Pareto Optimality in Trait Space Leads to Simple Patterns
252(1)
14.6 Two Tasks Lead to a Line Segment, Three Tasks to a Triangle, Four to a Tetrahedron
253(1)
14.7 Trade-Offs in Morphology
254(2)
14.8 Archetypes Can Last over Geological Timescales
256(1)
14.9 Trade-Offs for Proteins
257(1)
14.10 Trade-Offs in Gene Expression
258(1)
14.11 Division of Labor in the Individual Cells That Make Up an Organ
259(1)
14.12 Variation within a Species Lies on the Pareto Front
260(3)
Further Reading
263(1)
Exercises
263(8)
Bibliography
271(2)
Chapter 15 Modularity
273(14)
15.1 The Astounding Speed of Evolution
273(1)
15.2 Modularity Is a Common Feature of Engineered and Evolved Systems
273(1)
15.3 Modularity Is Found at All Levels of Biological Organization
274(1)
15.4 Modularity Is Not Found in Simple Computer Simulations of Evolution
275(1)
15.5 Simulated Evolution of Circuits Made of Logic Gates
275(3)
15.6 Randomly Varying Goals Cause Confusion
278(1)
15.7 Modularly Varying Goals Lead to Spontaneous Evolution of Modularity
278(2)
15.8 The More Complex the Goal, the More MVG Speeds Up Evolution
280(1)
15.9 Modular Goals and Biological Evolution
281(2)
Further Reading
283(1)
Exercises
283(1)
Bibliography
284(3)
Appendix A The Input Functions of Genes: Michaelis-Menten and Hill Equations 287(12)
A.1 Binding of a Repressor to a Promoter
287(2)
A.2 Binding of an Inducer to a Repressor Protein: The Michaelis-Menten Equation
289(2)
A.3 Cooperativity of Inducer Binding and the Hill Equation
291(1)
A.4 The Monod-Changeux-Wyman Model
292(1)
A.5 The Input Function of a Gene Regulated by a Repressor
293(1)
A.6 Binding of an Activator to Its DNA Site
294(1)
A.6.1 Comparison of Dynamics with Logic and Hill Input Functions
295(1)
A.7 Michaelis-Menten Enzyme Kinetics
295(2)
Further Reading
297(1)
Exercises
297(1)
Bibliography
298(1)
Appendix B Multi-Dimensional Input Functions 299(4)
B.1 Input Function That Integrates an Activator and a Repressor
299(2)
Exercise
301(1)
Bibliography
301(2)
Appendix C Graph Properties of Transcription Networks 303(4)
C.1 Transcription Networks Are Sparse
303(1)
C.2 Transcription Networks Have Long-Tailed Out-Degree Sequences and Compact In-Degree Sequences
303(2)
C.3 Clustering Coefficients
305(1)
C.4 Quantitative Measure of Network Modularity
305(1)
Bibliography
306(1)
Appendix D Noise in Gene Expression 307(6)
D.1 Introduction
307(1)
D.2 Extrinsic and Intrinsic Noise
307(1)
D.3 Distribution of Protein Levels
308(1)
D.4 Network Motifs Affect Noise
309(1)
D.5 Position of Noisiest Step
310(1)
Further Reading
311(1)
Bibliography
311(2)
Words Of Thanks 313(2)
Index 315
Uri Alon is the Abisch-Frenkel professor of systems biology at the Weizmann Institute of Science; https://www.weizmann.ac.il/mcb/UriAlon/homepage