Synthetic Gene Circuits: Methods and Protocols

Preface
Contents
Contributors
Chapter 1: Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits
	1 Introduction
	2 Examples of Synthetic Regulatory Circuits
	3 Boolean Models
		3.1 The Toggle Switch
		3.2 The Oscillator with Positive Feedback
		3.3 The IRMA Circuit
	4 Piecewise-Linear Differential Equation Models
		4.1 The Toggle Switch
		4.2 The Oscillator with Positive Feedback
		4.3 The IRMA Circuit
	5 Analysis of Network Dynamics
		5.1 Analysis of Attractors and Their Stability
		5.2 Reduction of State Transition Graphs
		5.3 Formal Verification of Network Properties Using Model Checking
		5.4 Modular Analysis of Network Dynamics
		5.5 Probabilistic Analysis of State Transition Graphs
	6 Control of Network Dynamics
		6.1 Control Strategies
		6.2 Control for Boolean Models
		6.3 Control of Synthetic Circuits
	7 Concluding Remarks
	8 Notes
		8.1 Reviews on Qualitative Modeling
		8.2 Dynamic Properties of Positive and Negative Feedback Loops
		8.3 Updating Schedules for Boolean Models
		8.4 Discontinuities in Piecewise-Linear Differential Equation Models
	References
Chapter 2: Stochastic Differential Equations for Practical Simulation of Gene Circuits
	1 Introduction
	2 Materials
		2.1 Getting the Model in Proper Form
		2.2 Accounting for Noise and Computing Long-Term Statistics
		2.3 Software
	3 Methods
		3.1 Define the OpenFPM Client Program main.cpp
		3.2 Compilation
		3.3 Simulation
	4 Notes
	References
Chapter 3: Using Models to (Re-)Design Synthetic Circuits
	1 Introduction
	2 Materials
	3 Methods
		3.1 Writing Down a Model
			3.1.1 Abstracting the Circuit: Sketching Its Diagram
			3.1.2 Mass Action Equations
			3.1.3 Parameter Estimation
		3.2 Deterministic Solution
			3.2.1 Writing Ordinary Differential Equations (ODEs)
			3.2.2 Solving the System of ODEs
			3.2.3 Parameter Space Analysis and Bifurcation Diagrams
		3.3 Stochastic Simulations
			3.3.1 Stochastic Notation
			3.3.2 Simulating a Time-Trace: The Gillespie Algorithm
			3.3.3 Gillespie Algorithm: Time to Next Reaction
			3.3.4 Gillespie: Choosing a Reaction
			3.3.5 Gillespie: Updating the State of the System
			3.3.6 Gillespie: Iterating the Algorithm
			3.3.7 Characterization of Results
			3.3.8 Parameter Scan
		3.4 Using Models to Redesign the Circuit: An Example
	4 Notes
	References
Chapter 4: Automated Biocircuit Design with SYNBADm
	1 Introduction
	2 Methods
		2.1 The Modeling Framework
		2.2 Design as an Optimization Problem
		2.3 Optimization Solvers
		2.4 Practical Examples
	3 SYNBADm Installation and Initialization
	4 Design of an Oscillator with SYNBADm
		4.1 Definition of the Problem
		4.2 Preparing the Library of Components
		4.3 Defining the Objective Function
		4.4 Solving the Single Objective Optimization Problem
		4.5 Simulating the Dynamics of a Circuit
	5 Design of a Switch-Like Circuit with SYNBADm
		5.1 Definition of the Problem
		5.2 Preparing the Library of Components
		5.3 Defining the Objective Functions
		5.4 Solving the Multi-Objective Optimization Problem
	6 Notes
	References
Chapter 5: Setting Up an Automated Biomanufacturing Laboratory
	1 Introduction
	2 Identifying the Need for Automation
		2.1 Design
		2.2 Build
		2.3 Test
		2.4 Learn
	3 Strategy
	4 Business Plan
		4.1 Funding: Government
		4.2 Funding: Fee-for-Service Model and Project Partnerships
		4.3 Partnerships
		4.4 Education
		4.5 System Maintenance and Personnel
	5 Exciting Ventures in the Automation Field: Enabling Technologies
		5.1 Machine Learning
		5.2 Microfluidics and Cell-Free Systems
		5.3 DNA Assembly and Strain Development
		5.4 Open Science
		5.5 Metrology and Standardization
	6 Conclusion
	References
Chapter 6: Computer-Aided Design and Pre-validation of Large Batches of DNA Assemblies
	1 Introduction
	2 Batch Part Standardization
		2.1 Manual Standardization of a Genetic Part (Outline)
		2.2 Preparing the Necessary Data Files
		2.3 Protecting some Part Regions against Modifications
		2.4 Using the Web Application
		2.5 Output
	3 Batch Type-2S Assembly Pre-validation Via Cloning Simulation
		3.1 Preparing the Necessary Data Files
		3.2 Using the Web Application
		3.3 Output
	References
Chapter 7: Computer-Aided Planning for the Verification of Large Batches of DNA Constructs
	1 Introduction
	2 Automated Enzyme Selection for Restriction Digest
		2.1 Using the Web Application
	3 Automated Primer Selection and Design for Sanger Sequencing
		3.1 Preparing the Necessary Data Files
		3.2 Indicating Regions to Cover and Primer-Free Regions
		3.3 Using the Web Application
		3.4 Output
	References
Chapter 8: Characterizing Genetic Parts and Devices Using RNA Sequencing
	1 Introduction
	2 Materials
		2.1 Software Dependencies
		2.2 Installation of the Genetic Analyzer
		2.3 Sequencing Data
	3 Methods
		3.1 Initial Workflow Setup
		3.2 Data Preprocessing
		3.3 Generating Transcription Profiles
		3.4 Analyzing Differential Gene Expression to Understand the Host Response
		3.5 Characterizing Promoters and Terminators
		3.6 Quantifying the Response Function of Genetic Devices
		3.7 Removing Temporary Files and Logs
	4 Notes
	References
Chapter 9: Steady-State Cell-Free Gene Expression with Microfluidic Chemostats
	1 Introduction
	2 Materials
		2.1 Photolithography Machines
		2.2 Photolithography Consumables
		2.3 Soft Lithography Machines
		2.4 Soft Lithography Consumables
		2.5 Microfluidic Hardware
		2.6 Microfluidic Connectors
		2.7 Microscope Hardware
		2.8 Software
		2.9 Experimental Reagents
	3 Methods
		3.1 Design of Microfluidic Devices
		3.2 Photolithography for Mask and Wafer Fabrication
			3.2.1 Mask Fabrication
			3.2.2 Flow Mold Fabrication
			3.2.3 Control Mold Fabrication
		3.3 Soft Lithography for Device Fabrication
			3.3.1 Silanization of Wafers
			3.3.2 Casting and Curing of PDMS Devices
			3.3.3 Bonding of PDMS Devices to a Glass Slide
		3.4 Hardware Setup
			3.4.1 Regulation of Control Layer Pressure
			3.4.2 Regulation of Flow Layer Pressure
		3.5 Device Operation
			3.5.1 Filling Control Lines
			3.5.2 Filling Flow Lines
			3.5.3 Cell-Free Expression
	4 Notes
	References
Chapter 10: A Microfluidic/Microscopy-Based Platform for on-Chip Controlled Gene Expression in Mammalian Cells
	1 Introduction
	2 Materials
		2.1 Chip Fabrication
		2.2 Chip Loading
		2.3 Microfluidic-Based Time-lapse
	3 Methods
		3.1 Fabrication of PDMS Replica Molding
			3.1.1 Silanization of Master Mold Wafer
			3.1.2 PDMS Microfluidic Device Preparation
			3.1.3 Cleaning and Bonding of PDMS Chips to Glass Coverslips
		3.2 Chip Loading
			3.2.1 Pins Preparation and Wetting of the Device
			3.2.2 Shear-Free Cell Loading Via on-Chip Vacuum
			3.2.3 Preculture of Cells in the Microfluidic Device
		3.3 Microfluidics/Microscopy-Based Time-lapse
			3.3.1 Tubes
			3.3.2 Chip Positioning
			3.3.3 Actuation System
			3.3.4 Microscope Specs
			3.3.5 Time-lapse Settings
		3.4 Computational Algorithms
			3.4.1 Cell Segmentation
		3.5 Feedback Control Algorithms
			3.5.1 Relay Controller
			3.5.2 Proportional-Integral (PI) Controller
			3.5.3 Model Predictive Control (MPC)
	4 Notes
	References
Chapter 11: Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2
	1 Introduction
	2 Materials
		2.1 Toolbox Download and License
		2.2 Toolbox Requirements and Installation Guide
		2.3 Code Structure
		2.4 Basics on the Use of AMIGO2
	3 Methods
		3.1 Illustrative Example: Modeling of an Inducible Promoter
		3.2 Optimal Experimental Design for Model Selection in AMIGO2
			3.2.1 Definition of the Objective Functional
			3.2.2 Definition of the Optimization Problem
			3.2.3 Definition of the Numerical Methods
			3.2.4 Running the Code
		3.3 Optimal Experimental Design for Parameter Estimation in AMIGO2
			3.3.1 Definition of the Objective Functional
			3.3.2 Definition of the Optimization Problem
			3.3.3 Definition of the Numerical Methods
			3.3.4 Running the Code
	References
Chapter 12: A Cyber-Physical Platform for Model Calibration
	1 Introduction
	2 Materials
		2.1 Computational Tools
		2.2 Microfluidic Device Fabrication
		2.3 Microfluidic Experimental Setup
	3 Methods
		3.1 Structural Identifiability
		3.2 Sensitivity Analysis
		3.3 Practical Identifiability
		3.4 Microfluidic Experiments
			3.4.1 Microfluidic Device Fabrication
			3.4.2 Overnight Culture
			3.4.3 Syringe Preparation
			3.4.4 Wetting the Microfluidic Chip
			3.4.5 Connecting Syringes to the Chip
			3.4.6 Calibration of the Microfluidic Device
			3.4.7 Loading the Cells
			3.4.8 Microscope Setup
		3.5 Image Processing
			3.5.1 Fine-Tuning of the Weights of the Convolutional Neural Network
			3.5.2 Image Segmentation
			3.5.3 Cell-Tracking and Extraction of Fluorescence Time-Series
		3.6 Parameter Estimation
		3.7 Optimal Experimental Design for Model Calibration
	4 Notes
	References
Chapter 13: Prediction of Cellular Burden with Host-Circuit Models
	1 Introduction
	2 Coarse-Grained Models for Bacterial Growth
		2.1 Bacterial Growth Laws
		2.2 A Mechanistic Model of Bacterial Growth
	3 Modeling Gene Circuits Coupled with Their Host
		3.1 Extending the Model with Heterologous Genes
		3.2 Simulation of an Inducible Gene
	4 Simulation of Transcriptional Logic Gates
		4.1 Host-Aware NOT Gate
		4.2 Host-Aware AND Gate
		4.3 Host-Aware NAND Gate
		4.4 Impact of Design Parameters on Circuit Function
			4.4.1 Ribosomal Binding Sites (RBS)
			4.4.2 Nutrient Quality
	5 Discussion
	References
Chapter 14: A Practical Step-by-Step Guide for Quantifying Retroactivity in Gene Networks
	1 Introduction
	2 Materials
		2.1 Biochemical Reactions
		2.2 Model Order Reduction via Time-Scale Separation
		2.3 Contraction Theory
	3 Methods
		3.1 Step 1: Mathematical Model of Modules
		3.2 Step 2: Internal Retroactivity
		3.3 Step 3: External Retroactivity
		3.4 Step 4: Scaling and Mixing Retroactivity
		3.5 Step 5: Error Due to Retroactivity
		3.6 Illustrating the Effects of Intermodular Connections
	4 Notes
	References
Chapter 15: Engineering Sensors for Gene Expression Burden
	1 Introduction
	2 Materials
		2.1 Strains
		2.2 Molecular Cloning
		2.3 Medium
		2.4 RNA-Seq Library Preparation
			2.4.1 Consumables
			2.4.2 Equipment
	3 Methods
		3.1 Identify How the Host Responds to Burden Using RNA-Seq
			3.1.1 Transformation with Synthetic Construct Causing Burden
			3.1.2 Time-Course Assay
			3.1.3 RNA-Seq Sample Preparation
			3.1.4 RNA-Seq Library Sequencing
			3.1.5 Sequencing Quality Control and Alignments
			3.1.6 Transcription Profiles and Promoter Characterization
			3.1.7 Analyze the Plate-Reader Data to Evaluate Burden
		3.2 Select the Best Burden-Responsive Promoter to Build a Burden Biosensor
			3.2.1 Interpret the RNA-Seq Results to Identify Promoter Upregulated by Burden
			3.2.2 Test the Burden-Responsive Promoters Out of Their Genomic Context
			3.2.3 Select the Best Promoter to Use as Burden Biosensor
		3.3 Build the Burden-Driven Feedback Loop
			3.3.1 Build the Feedback Plasmid
			3.3.2 Tune the Burden-Driven Feedback
	4 Notes
	References
Chapter 16: Engineering Protein-Based Parts for Genetic Devices in Mammalian Cells
	1 Introduction
		1.1 Synthetic Devices that Sense Intracellular Protein and Regulate Cellular Fate
		1.2 A Protein-Based Strategy to Regulate RNA Translation and Protein Activity
	2 Material
		2.1 Intracellular Protein-Sensor Devices to Regulate Cell Fate
			2.1.1 DNA Cloning and Plasmid Construction
			2.1.2 Mammalian Cells Culture and Transfection/Electroporation
			2.1.3 Flow Cytometry Staining, Acquisition, and Analyses
			2.1.4 RNA Extraction, cDNA Synthesis, and qPCR
		2.2 Protein-Based Devices to Regulate RNA and Protein Activity
			2.2.1 Cell Culture, Transient Transfection Cell Imaging and Flow Cytometry
			2.2.2 PCR and Plasmid Cloning
			2.2.3 In Silico Protein Engineering
	3 Methods
		3.1 Intracellular Protein-Sensor Devices to Regulate Cell Fate
			3.1.1 DNA Cloning and Plasmid Construction
			3.1.2 Transfection of HEK 293FT Cells and Fluorescence Imaging
			3.1.3 Electroporation of TZM-bl and Jurkat Cells
			3.1.4 Flow Cytometry and Data Analysis
			3.1.5 Determination of HLA-I Surface Expression by Flow Cytometry
			3.1.6 HIV Production and Infection
			3.1.7 Apoptosis Assays
			3.1.8 RNA Extraction, cDNA Synthesis, and qPCR
		3.2 Protein-Based Devices to Regulate RNA and Protein Activity
			3.2.1 Protein Structural Analysis and Plasmid Cloning
			3.2.2 Protein-Protein Devices Testing
	4 Notes
	References
Index