Introduction to Systems Biology Design Principles of Biological Circuits

ISBN-10: 1584886420

ISBN-13: 9781584886426

Edition: 2006

Authors: Uri Alon

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Thorough and accessible, this book presents the design principles of biological systems, and highlights the recurring circuit elements that make up biological networks. It provides a simple mathematical framework which can be used to understand and even design biological circuits. The text avoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles.An Introduction to Systems Biology: Design Principles of Biological Circuits builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.
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Book details

List price: $68.95
Copyright year: 2006
Publisher: CRC Press LLC
Publication date: 7/7/2006
Binding: Paperback
Pages: 320
Size: 7.00" wide x 9.75" long x 0.75" tall
Weight: 1.298

Transcription Networks: Basic Concepts
The Cognitive Problem of the Cell
Elements of Transcription Networks
Separation of Timescales
The Signs on the Edges: Activators and Repressors
The Numbers on the Edges: The Input Function
Logic Input Functions: A Simple Framework for Understanding Network Dynamics
Multi-Dimensional Input Functions Govern Genes with Several Inputs
Interim Summary
Dynamics and Response Time of Simple Gene Regulation
The Response Time of Stable Proteins Is One Cell Generation
Further Reading
Autoregulation: A Network Motif
Patterns, Randomized Networks, and Network Motifs
Detecting Network Motifs by Comparison to Randomized Networks
Autoregulation: A Network Motif
Negative Autoregulation Speeds the Response Time of Gene Circuits
Negative Autoregulation Promotes Robustness to Fluctuations in Production Rate
Positive Autoregulation Slows Responses and Can Lead to Bi-Stability
Further Reading
The Feed-Forward Loop Network Motif
The Number of Appearances of a Subgraph in Random Networks
The Feed-Forward Loop Is a Network Motif
The Structure of the Feed-Forward Loop Gene Circuit
Dynamics of the Coherent Type-1 FFL with AND Logic
The C1-FFL Is a Sign-Sensitive Delay Element
Delay Following an ON Step of S[subscript x]
No Delay Following an OFF Step of S[subscript x]
The C1-FFL Is a Sign-Sensitive Delay Element
Sign-Sensitive Delay Can Protect against Brief Input Fluctuations
sign-Sensitive Delay in the Arabinose System of E. coli
The OR Gate C1-FFL Is a Sign-Sensitive Delay for OFF Steps of S[subscript x]
Interim Summary
The Incoherent Type-1 FFL
The Structure of the Incoherent FFL
Dynamics of the I1-FFL: A Pulse Generator
The I1-FFL Speeds the Response Time
Response Acceleration Is Sign Sensitive
Experimental Study of the Dynamics of an I1-FFL
Three Ways to Speed Your Responses (An Interim Summary)
Why Are Some FFL Types Rare?
Steady-State Logic of the I1-FFL: S[subscript y] Can Turn on High Expression
I4-FFL, a Rarely Selected Circuit, Has Reduced Functionality
Convergent Evolution of FFLs
Further Reading
Temporal Programs and the Global Structure of Transcription Networks
The Single-Input Module (SIM) Network Motif
SIMs Can Generate Temporal Expression Programs
Topological Generalizations of Network Motifs
The Multi-Output FFL Can Generate FIFO Temporal Order
The Multi-Output FFL Can Also Act as a Persistence Detector for Each Output
Signal Integration and Combinatorial Control: Bi-Fans and Dense Overlapping Regulons
Network Motifs and the Global Structure of Sensory Transcription Networks
Further Reading
Network Motifs in Developmental, Signal Transduction, and Neuronal Networks
Network Motifs in Developmental Transcription Networks
Two-Node Positive Feedback Loops for Decision Making
Regulating Feedback and Regulated Feedback
Long Transcription Cascades and Developmental Timing
Interlocked Feed-Forward Loops in the B. subtilis Sporulation Network
Network Motifs inSignal Transduction Networks
Information Processing Using Multi-Layer Perceptrons
Toy Model for Protein Kinase Perceptrons
Multi-Layer Perceptrons Can Perform Detailed Computations
Composite Network Motifs: Negative Feedback and Oscillator Motifs
Network Motifs in the Neuronal Network of C elegans
The Multi-Input FFL in Neuronal Networks
Multi-Layer Perceptrons in the C. elegans Neuronal Network
Further Reading
Robustness of Protein Circuits: The Example of Bacterial Chemotaxis
The Robustness Principle
Bacterial Chemotaxis, or How Bacteria Think
Chemotaxis Behavior
Response and Exact Adaptation
The Chemotaxis Protein Circuit of E. coli
Attractants Lower the Activity of X
Adaptation Is Due to Slow Modification of X That Increases Its Activity
Two Models Can Explain Exact Adaptation: Robust and Fine-Tuned
Fine-Tuned Model
The Barkai-Leibler Robust Mechanism for Exact Adaptation
Robust Adaptation and Inegral Feedback
Experiments Show That Exact Adaptation Is Robust, Whereas Steady-State Activity and Adaptation Times Are Fine-Tuned
Individuality and Robustness in Bacterial Chemotaxis
Further Reading
Robust Patterning in Development
Exponential Morphogen Profiles Are Not Robust
Increased Robustness by Self-Enhanced Morphogen Degradation
Network Motifs That Provide Degradation Feedback for Robust Patterning
The Robustness Principle Can Distinguish between Mechanisms of Fruit Fly Patterning
Further Reading
Kinetic Proofreading
Kinetic Proofreading of the Genetic Code Can Reduce Error Rates of Molecular Recognition
Equilibrium Binding Cannot Explain the Precision of Translation
Kinetic Proofreading Can Dramatically Reduce the Error Rate
Recognizing Self and Non-Self by the Immune System
Equilibrium Binding Cannot Explain the Low Error Rate of Immune Recognition
Kinetic Proofreading Increases Fidelity of T-Cell Recognition
Kinetic Proofreading May Occur in Diverse Recognition Processes in the Cell
Further Reading
Optimal Gene Circuit Design
Optimal Expression Level of a Protein under Constant Conditions
The Benefit of the LacZ Protein
The Cost of the LacZ Protein
The Fitness Function and the Optimal Expression Level
Laboratory Evolution Experiment Shows That Cells Reach Optimal LacZ Levels in a Few Hundred Generations
To Regulate or Not to Regulate: Optimal Regulation in Variable Environments
Environmental Selection of the Feed-Forward Loop Network Motif
Further Reading
Demand Rules for Gene Regulation
The Savageau Demand Rule
Evidence for the Demand Rule in E. coli
Mutational Explanation of the Demand Rule
The Problem with Mutant-Selection Arguments
Rules for Gene Regulation Based on Minimal Error Load
The Selection Pressure for Optimal Regulation
Demand Rules for Multi-Regulator Systems
Further Reading
Epilogue: Simplicity in Biology
The Input Function of a Gene: Michaelis-Menten and Hill Equations
Binding of a Repressor to a Promoter
Binding of a Repressor Protein to an Inducer: Michaelis-Menten Equation
Cooperativity of Inducer Binding and the Hill Equation
The Monod, Changeux, and Wymann Model
The Input Function of a Gene Regulated by a Repressor
Binding of an Activator to Its DNA Site
Comparison of Dynamics with Logic and Hill Input Functions
Michaelis-Menten Enzyme Kinetics
Further Reading
Multi-Dimensional Input Functions
Input Function That Integrates an Activator and a Repressor
Graph Properties of Transcription Networks
Transcription Networks Are Sparse
Transcription Networks Have Long-Tailed Output Degree Sequences and Compact Input Degree Sequences
Clustering Coefficients of Transcription Networks
Quantitative Measures of Network Modularity
Cell-Cell Variability in Gene Expression
Further Reading
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