Engineering genetic circuits

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.

After reviewing the basic molecular biology and biochemistry principles needed to understand genetic circuits, the book describes modern experimental techniques and methods for discovering genetic circuit models from the data generated by experiments. The next four chapters present state-of-the-art methods for analyzing these genetic circuit models. The final chapter explores how researchers are beginning to use analytical methods to design synthetic genetic circuits.

This text clearly shows how the success of systems biology depends on collaborations between engineers and biologists. From biomolecular observations to mathematical models to circuit design, it provides essential information on genetic circuits and engineering techniques that can be used to study biological systems.

Author Notes

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.

List of Figures	p. xiii
List of Tables	p. xvii
Foreword	p. xix
Preface	p. xxiii
Acknowledgments	p. xxvii
1 An Engineer's Guide to Genetic Circuits
1.1 Chemical Reactions	p. 1
1.2 Macromolecules	p. 4
1.3 Genomes	p. 8
1.4 Cells and Their Structure	p. 9
1.5 Genetic Circuits	p. 13
1.6 Viruses	p. 16
1.7 Phage ¿: A Simple Genetic Circuit	p. 17
1.7.1 A Genetic Switch	p. 19
1.7.2 Recognition of Operators and Promoters	p. 26
1.7.3 The Complete Circuit	p. 29
1.7.4 Genetic Circuit Models	p. 35
1.7.5 Why Study Phage ¿?	p. 36
1.8 Sources	p. 39
Problems	p. 40
Appendix	p. 43
2 Learning Models	p. 51
2.1 Experimental Methods	p. 52
2.2 Experimental Data	p. 57
2.3 Cluster Analysis	p. 59
2.4 Learning Bayesian Networks	p. 62
2.5 Learning Causal Networks	p. 68
2.6 Experimental Design	p. 79
2.7 Sources	p. 80
Problems	p. 81
3 Differential Equation Analysis	p. 85
3.1 A Classical Chemical Kinetic Model	p. 86
3.2 Differential Equation Simulation	p. 88
3.3 Qualitative ODE Analysis	p. 92
3.4 Spatial Methods	p. 97
3.5 Sources	p. 98
Problems	p. 99
4 Stochastic Analysis	p. 103
4.1 A Stochastic Chemical Kinetic Model	p. 104
4.2 The Chemical Master Equation	p. 106
4.3 Gillespie's Stochastic Simulation Algorithm	p. 107
4.4 Gibson/Bruck's Next Reaction Method	p. 111
4.5 Tau-Leaping	p. 115
4.6 Relationship to Reaction Rate Equations	p. 117
4.7 Stochastic Petri-Nets	p. 119
4.8 Phage ¿ Decision Circuit Example	p. 120
4.9 Spatial Gillespie	p. 124
4.10 Sources	p. 127
Problems	p. 127
5 Reaction-Based Abstraction	p. 131
5.1 Irrelevant Node Elimination	p. 132
5.2 Enzymatic Approximations	p. 133
5.3 Operator Site Reduction	p. 136
5.4 Statistical Thermodynamical Model	p. 145
5.5 Dimerization Reduction	p. 151
5.6 Phage ¿ Decision Circuit Example	p. 153
5.7 Stoichiometry Amplification	p. 154
5.8 Sources	p. 154
Problems	p. 157
6 Logical Abstraction	p. 161
6.1 Logical Encoding	p. 163
6.2 Piecewise Models	p. 165
6.3 Stochastic Finite-State Machines	p. 170
6.4 Markov Chain Analysis	p. 174
6.5 Qualitative Logical Models	p. 180
6.6 Sources	p. 184
Problems	p. 185
7 Genetic Circuit Design	p. 187
7.1 Assembly of Genetic Circuits	p. 188
7.2 Combinational Logic Gates	p. 189
7.3 PoPS Gates	p. 198
7.4 Sequential Logic Circuits	p. 198
7.5 Future Challenges	p. 211
7.6 Sources	p. 212
Problems	p. 213
Solutions to Selected Problems	p. 215
References	p. 237
Glossary	p. 247
Index	p. 273

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