Introduction to Systems Biology Design Principles of Biological Circuits

Name: Introduction to Systems Biology Design Principles of Biological Circuits
Price: 28.37 USD
Availability: InStock
ISBN: 9781584886426

ISBN-10: 1584886420

ISBN-13: 9781584886426

Edition: 2006

Authors: Uri Alon

List price: $73.95

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Description:

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…

Book details

List price: $73.95
Copyright year: 2006
Publisher: CRC Press LLC
Publication date: 7/7/2006
Binding: Paperback
Pages: 320
Size: 7.01" wide x 10.00" long x 0.63" tall
Weight: 1.298
Language: English




Introduction



Transcription Networks: Basic Concepts



Introduction



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


Exercises



Autoregulation: A Network Motif



Introduction



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



Summary


Further Reading


Exercises



The Feed-Forward Loop Network Motif



Introduction



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



Summary


Further Reading


Exercises



Temporal Programs and the Global Structure of Transcription Networks



Introduction



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


Exercises



Network Motifs in Developmental, Signal Transduction, and Neuronal Networks



Introduction



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



Summary


Further Reading


Exercises



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


Exercises



Robust Patterning in Development



Introduction



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


Exercises



Kinetic Proofreading



Introduction



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


Exercises



Optimal Gene Circuit Design



Introduction



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



Summary


Further Reading


Exercises



Demand Rules for Gene Regulation



Introduction



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



Summary


Further Reading


Exercises



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


Exercises



Multi-Dimensional Input Functions



Input Function That Integrates an Activator and a Repressor


Exercises



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


Glossary


Bibliography


Index