Engineering Genetic Circuits

by Chris J. Myers

Overview

Focusing on genetic regulatory networks, Engineering Genetic Circuits presents the modeling, analysis, and design methods for systems biology. It discusses how to examine experimental data to learn about mathematical models, develop efficient abstraction and simulation methods to analyze these models, and use analytical methods to guide the design of new circuits. The text clearly shows how the success of systems biology depends on collaborations between engineers and biologists.

Features

Introduces relevant biology and biochemistry for readers with an engineering background

Covers key methods for modeling and analyzing genetic circuits, such as differential equations and stochastic analysis

Describes abstraction methods, which can substantially improve the efficiency of analyses

Uses the lysis/lysogeny decision circuit of phage λ as an example throughout to help illustrate the various methods

Presents an introduction to the emerging area of synthetic biology

Offers iBioSim software and other material on the author's website

Product Details

ISBN-13:	9781420083255
Publisher:	CRC Press
Publication date:	04/19/2016
Series:	Chapman & Hall/CRC Mathematical and Computational Biology , #26
Sold by:	Barnes & Noble
Format:	eBook
Pages:	306
File size:	4 MB

About the Author

Chris J. Myers is a professor in the Department of Electrical and Computer Engineering at the University of Utah. A co-inventor on four patents and author of more than 80 technical papers and the textbook Asynchronous Circuit Design, Dr. Myers received an NSF Fellowship in 1991 and an NSF CAREER award in 1996. His research interests include formal verification, asynchronous circuit design, and the analysis and design of genetic regulatory circuits.

Table of Contents

List of Figures xiii

List of Tables xvii

Foreword xix

Preface xxiii

Acknowledgments xxvii

1 An Engineer's Guide to Genetic Circuits

1.1 Chemical Reactions 1

1.2 Macromolecules 4

1.3 Genomes 8

1.4 Cells and Their Structure 9

1.5 Genetic Circuits 13

1.6 Viruses 16

1.7 Phage λ: A Simple Genetic Circuit 17

1.7.1 A Genetic Switch 19

1.7.2 Recognition of Operators and Promoters 26

1.7.3 The Complete Circuit 29

1.7.4 Genetic Circuit Models 35

1.7.5 Why Study Phage λ? 36

1.8 Sources 39

Problems 40

Appendix 43

2 Learning Models 51

2.1 Experimental Methods 52

2.2 Experimental Data 57

2.3 Cluster Analysis 59

2.4 Learning Bayesian Networks 62

2.5 Learning Causal Networks 68

2.6 Experimental Design 79

2.7 Sources 80

Problems 81

3 Differential Equation Analysis 85

3.1 A Classical Chemical Kinetic Model 86

3.2 Differential Equation Simulation 88

3.3 Qualitative ODE Analysis 92

3.4 Spatial Methods 97

3.5 Sources 98

Problems 99

4 Stochastic Analysis 103

4.1 A Stochastic Chemical Kinetic Model 104

4.2 The Chemical Master Equation 106

4.3 Gillespie's Stochastic Simulation Algorithm 107

4.4 Gibson/Bruck's Next Reaction Method 111

4.5 Tau-Leaping 115

4.6 Relationship to Reaction Rate Equations 117

4.7 Stochastic Petri-Nets 119

4.8 Phage λ Decision Circuit Example 120

4.9 Spatial Gillespie 124

4.10 Sources 127

Problems 127

5 Reaction-Based Abstraction 131

5.1 Irrelevant Node Elimination 132

5.2 Enzymatic Approximations 133

5.3 Operator Site Reduction 136

5.4 Statistical Thermodynamical Model 145

5.5 Dimerization Reduction 151

5.6 Phage λ Decision Circuit Example153

5.7 Stoichiometry Amplification 154

5.8 Sources 154

Problems 157

6 Logical Abstraction 161

6.1 Logical Encoding 163

6.2 Piecewise Models 165

6.3 Stochastic Finite-State Machines 170

6.4 Markov Chain Analysis 174

6.5 Qualitative Logical Models 180

6.6 Sources 184

Problems 185

7 Genetic Circuit Design 187

7.1 Assembly of Genetic Circuits 188

7.2 Combinational Logic Gates 189

7.3 PoPS Gates 198

7.4 Sequential Logic Circuits 198

7.5 Future Challenges 211

7.6 Sources 212

Problems 213

Solutions to Selected Problems 215

References 237

Glossary 247

Index 273