• 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

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.

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&nbs