Engineering Genetic Circuits
Purchasing Options
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 lambda as an example throughout to help illustrate the various methods
- Presents an introduction to the emerging area of synthetic biology
- Includes exercises and self-study problem sets in every chapter
- Offers iBioSim software, lecture slides, and a password-protected solutions manual on the author’s website
Summary
An Introduction to Systems Bioengineering
Takes a Clear and Systematic Engineering Approach to Systems Biology
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.
Table of Contents
An Engineer’s Guide to Genetic Circuits
Chemical Reactions
Macromolecules
Genomes
Cells and Their Structure
Genetic Circuits
Viruses
Phage lambda: A Simple Genetic Circuit
Learning Models
Experimental Methods
Experimental Data
Cluster Analysis
Learning Bayesian Networks
Learning Causal Networks
Experimental Design
Differential Equation Analysis
A Classical Chemical Kinetic Model
Differential Equation Simulation
Qualitative ODE Analysis
Spatial Methods
Stochastic Analysis
A Stochastic Chemical Kinetic Model
The Chemical Master Equation
Gillespie’s Stochastic Simulation Algorithm
Gibson/Bruck’s Next Reaction Method
Tau-Leaping
Relationship to Reaction Rate Equations
Stochastic Petri-Nets
Phage lambda Decision Circuit Example
Spatial Gillespie
Reaction-Based Abstraction
Irrelevant Node Elimination
Enzymatic Approximations
Operator Site Reduction
Statistical Thermodynamical Model
Dimerization Reduction
Phage lambda Decision Circuit Example
Stoichiometry Amplification
Logical Abstraction
Logical Encoding
Piecewise Models
Stochastic Finite-State Machines
Markov Chain Analysis
Qualitative Logical Models
Genetic Circuit Design
Assembly of Genetic Circuits
Combinational Logic Gates
PoPS Gates
Sequential Logic Circuits
Future Challenges
Solutions to Selected Problems
References
Glossary
Index
Sources and Problems appear at the end of each chapter.
Author Bio(s)
Editorial Reviews
"This book by Professor Myers is one of the few texts in the area that gently brings the uninitiated to these edges. I congratulate him for his achievement—Engineering Genetic Circuits admirably touches on much of the ‘required’ knowledge while creating a minimal toolset with which beginning students can confidently venture into this exciting new territory of systems biology."
—From the Foreword, Adam Arkin, University of California, Berkeley, USA
"Prof. Myers’ book will be an excellent reference for any course in systems biology … . I find the many illustrations (worked-out examples and ample number of figures) and exercises at the end of each chapter quite useful and important."
—Baltazar Aguda, The Ohio State University, Columbus, USA
