21 Synthetic Biology

21.1 Learning Objectives

By the end of this chapter, you should be able to:

Define synthetic biology and distinguish it from genetic engineering
Describe the key principles of engineering applied to biological systems
Explain the design-build-test-learn cycle in synthetic biology
Analyze common genetic circuits and their functions
Evaluate applications of synthetic biology in medicine, industry, and environment
Discuss ethical, safety, and regulatory considerations in synthetic biology
Compare different chassis organisms and their uses
Propose a synthetic biology approach to solve a specific problem

21.2 Introduction

Synthetic biology represents a fundamental shift in how we approach living systems. Rather than simply studying biology as it exists, synthetic biology applies engineering principles to design and construct new biological parts, devices, and systems, or to redesign existing natural biological systems for useful purposes. This emerging field sits at the intersection of biology, engineering, computer science, and chemistry, creating tools to program living cells much as we program computers. From producing life-saving medicines to cleaning up environmental pollutants, synthetic biology promises to revolutionize how we interact with the biological world.

21.3 Foundations of Synthetic Biology

21.3.1 Definition and Scope

Synthetic biology: The design and construction of new biological entities or systems, and the redesign of existing biological systems, for useful purposes.

Key distinctions from genetic engineering:

Scale: Genetic engineering typically modifies single genes; synthetic biology often engineers entire pathways or genomes
Approach: Genetic engineering is often ad hoc; synthetic biology applies systematic engineering principles
Goals: Genetic engineering aims to modify existing traits; synthetic biology aims to create novel functions
Standardization: Synthetic biology emphasizes standardized parts and assembly methods

21.3.2 Core Principles

Abstraction: Hiding complexity through well-defined interfaces
Standardization: Creating interchangeable biological parts
Modularity: Designing systems from functional modules
Characterization: Quantifying part performance under different conditions
Decoupling: Separating design from fabrication

21.3.3 Engineering Paradigms

Top-down approach: Simplifying existing biological systems (minimal genomes)

Bottom-up approach: Constructing synthetic systems from basic components (protocells)

Middle-out approach: Combining natural and synthetic components

21.4 The Synthetic Biology Toolbox

21.4.1 DNA Synthesis and Assembly

Gene synthesis: Chemical production of DNA sequences from digital files

Cost: Dropped from ~$10 per base pair (2000) to ~$0.05 per base pair (2020)
Length: Up to hundreds of kilobases for chromosome-scale synthesis

DNA assembly methods:

Restriction enzyme-based: BioBrick, Golden Gate, MoClo
Homology-based: Gibson assembly, yeast homologous recombination
In vivo assembly: Transformation-associated recombination (TAR) in yeast

Genome editing tools:

CRISPR-Cas9: Precision editing with guide RNAs
Base editors: Convert one DNA base to another without double-strand breaks
Prime editing: “Search-and-replace” genome editing

21.4.2 Genetic Parts and Devices

Parts: Basic biological components with defined functions

Promoters: Control transcription initiation
Ribosome binding sites (RBS): Control translation initiation
Protein coding sequences: Encode functional proteins
Terminators: Signal transcription termination

Devices: Combinations of parts that perform specific functions

Sensors: Detect environmental signals
Actuators: Produce outputs (proteins, metabolites)
Logic gates: Perform Boolean operations

Systems: Networks of devices that perform complex functions

21.4.3 Characterization and Measurement

Characterization: Quantifying part performance

Transfer functions: Input-output relationships
Parameters: Strength, leakage, dynamic range
Context dependence: Performance in different genetic backgrounds

Measurement technologies:

Flow cytometry: Single-cell measurements
Fluorescence microscopy: Spatial and temporal resolution
RNA-seq, proteomics, metabolomics: Multi-omics characterization

21.5 Genetic Circuit Design

21.5.1 Basic Circuit Elements

Constitutive expression: Constant production regardless of conditions

Inducible systems: Expression controlled by external signals

Chemical inducers: IPTG, arabinose, tetracycline
Physical signals: Light, temperature
Biological signals: Quorum sensing molecules

Repressible systems: Expression turned off by signals

21.5.2 Logic and Computation

Boolean logic in cells:

AND gate: Output only if both inputs present
OR gate: Output if either input present
NOT gate: Output if input absent
NAND, NOR, XOR gates: More complex logic functions

Examples:

Toggle switch: Bistable system with two stable states
Repressilator: Synthetic oscillatory network
Band-detection circuit: Responds to specific concentration ranges

21.5.3 Dynamic Control

Feedback loops:

Negative feedback: Stabilizes systems, reduces noise
Positive feedback: Creates bistability, amplifies signals

Feedforward loops: Anticipatory control

Proportional, integral, derivative (PID) control: Engineering control theory applied to cells

21.5.4 Memory and State

Biological memory: Cells “remember” past states

Synthetic memory circuits:

Transcriptional memory: Self-sustaining expression states
Recombinase-based memory: DNA rearrangement creates stable states
CRISPR-based memory: DNA writing records events

21.6 Chassis Organisms

21.6.1 Bacterial Chassis

E. coli: Most common synthetic biology chassis

Advantages: Well-characterized, fast growth, many tools
Strains: K-12 (lab), B (industry), W (metabolism)

B. subtilis: Gram-positive, industrial applications Pseudomonas spp.: Environmental applications, biofilm formation Cyanobacteria: Photosynthetic, CO₂ fixation

21.6.2 Eukaryotic Chassis

S. cerevisiae (yeast):

Advantages: Eukaryotic systems, post-translational modifications, GRAS status
Applications: Protein production, metabolic engineering

Mammalian cells:

HEK293, CHO cells: Protein therapeutics
Stem cells: Tissue engineering, regenerative medicine

Plant chassis:

Arabidopsis: Model plant
Tobacco, lettuce: Bioproduction platforms

21.6.3 Minimal and Synthetic Genomes

Minimal genomes: Smallest set of genes for life

Mycoplasma genitalium: 525 genes (natural minimal)
JCVI-syn3.0: 473 genes (synthetic minimal)

Synthetic genomes:

Yeast 2.0: Synthetic yeast genome project
Genome recoding: Reassigning codons for new functions

Xenobiology: Creating organisms with alternative biochemistries

XNA: Xenonucleic acids (alternative genetic polymers)
Non-canonical amino acids: Expanding the genetic code

21.7 Applications of Synthetic Biology

21.7.1 Medical Applications

Therapeutics production:

Artemisinin: Anti-malarial produced in yeast
Insulin, growth hormone: Recombinant proteins
Monoclonal antibodies: Engineered for specific targets

Diagnostics and sensing:

Paper-based diagnostics: Freeze-dried cell-free systems
Gut microbiome sensors: Detecting disease markers
Living therapeutics: Engineered bacteria for disease treatment

Cell and gene therapy:

CAR-T cells: Engineered immune cells for cancer
Gene circuits for safety: Suicide switches, dependency circuits

21.7.2 Industrial Biotechnology

Biofuels and chemicals:

Isobutanol, butanol: Advanced biofuels
1,4-butanediol, succinic acid: Platform chemicals
Vanillin, saffron: High-value natural products

Materials production:

Spider silk: Strong, lightweight biomaterial
Bacterial cellulose: Wound dressings, textiles
Biocement, bioplastics: Sustainable materials

Agriculture:

Nitrogen fixation: Engineering crops to fix atmospheric nitrogen
Disease resistance: Engineering pathogen resistance
Nutritional enhancement: Biofortified crops

21.7.3 Environmental Applications

Bioremediation:

Heavy metal capture: Engineering metal-binding proteins
Pollutant degradation: Pathways for breaking down contaminants
Oil spill cleanup: Engineering oil-degrading bacteria

Biosensing:

Environmental monitoring: Detecting pollutants, pathogens
Landmine detection: Engineering plants that change color near explosives

Conservation:

Gene drives: Controlling invasive species or disease vectors
De-extinction: Potential to revive extinct species

21.8 Challenges and Limitations

21.8.1 Technical Challenges

Context dependence: Parts behave differently in different genetic backgrounds

Noise and variability: Stochastic fluctuations in biological systems

Metabolic burden: Engineering imposes fitness costs on host cells

Orthogonality: Ensuring engineered systems don’t interfere with host functions

Scale-up: Moving from lab scale to industrial production

21.8.2 Design Challenges

Predictive modeling: Current models have limited predictive power

Standardization: Biological parts are not truly standardized

Characterization: Incomplete characterization of parts and devices

Evolution: Engineered systems evolve away from designed functions

21.8.3 Safety and Security

Containment strategies:

Physical containment: Lab facilities, fermentation systems
Biological containment: Auxotrophy, toxin-antitoxin systems
Genetic firewalls: Recoded genomes, XNA organisms

Dual-use concerns: Technologies with both beneficial and harmful applications

Environmental risk: Potential ecological impacts of engineered organisms

21.10 Future Directions

21.10.1 Emerging Technologies

Cell-free synthetic biology: Using cellular components without intact cells

Advantages: No viability constraints, direct access to reactions
Applications: Diagnostics, biomanufacturing, education

DNA data storage: Using DNA as a high-density, long-term storage medium

Density: Theoretical limit of 1 exabyte/mm³
Longevity: Thousands of years with proper storage

Biological computing: Using cells as living computers

Distributed computation: Microbial consortia performing complex calculations
Pattern recognition: Engineering cells to recognize complex patterns

21.10.2 Integration with Other Fields

AI and machine learning: Designing, modeling, and optimizing biological systems

Nanotechnology: Interface between biological and nanoscale systems

Materials science: Biohybrid materials with novel properties

Space biology: Supporting human exploration and settlement of space

21.10.3 Long-Term Vision

Programmable cells: Cells that can be reliably programmed like computers

Synthetic ecosystems: Engineered microbial communities with defined functions

Biofoundries: Automated facilities for designing, building, and testing biological systems

Distributed biology: Open-source, accessible synthetic biology tools and knowledge

21.11 Chapter Summary

21.11.1 Key Concepts

Synthetic biology applies engineering principles to biological systems
The design-build-test-learn cycle drives iterative improvement
Genetic circuits implement computation and control in cells
Standardization and abstraction enable complex system design
Applications span medicine, industry, environment, and basic science
Technical challenges include context dependence, noise, and evolution
Ethical and social considerations are integral to responsible development
Emerging directions include cell-free systems, DNA data storage, and biological computing

21.11.2 The DBTL Cycle

Phase	Activities	Tools & Methods
Design	Specification, modeling, part selection	CAD software, repositories, modeling tools
Build	DNA assembly, transformation, verification	Gene synthesis, assembly methods, sequencing
Test	Characterization, measurement, data collection	Flow cytometry, microscopy, omics technologies
Learn	Analysis, modeling refinement, redesign	Statistical analysis, machine learning, model updating

21.11.3 Common Genetic Circuits

Circuit Type	Function	Key Components	Applications
Toggle switch	Bistable memory	Two repressors, mutual inhibition	Cellular memory, state switching
Repressilator	Oscillation	Three repressors in cycle	Biological clocks, rhythmic processes
Band detector	Concentration range detection	Multiple promoters with different affinities	Precision sensing, threshold responses
Logic gates	Boolean computation	Promoters, operators, transcription factors	Decision-making, pattern recognition
Feedback controller	Regulation to setpoint	Sensor, comparator, actuator	Homeostasis, metabolic control

21.11.4 Major Chassis Organisms

Organism	Type	Advantages	Applications
E. coli	Bacterium	Well-characterized, fast growth, many tools	Protein production, metabolic engineering, basic research
S. cerevisiae	Yeast	Eukaryotic, GRAS status, genetic tools	Protein production, metabolic engineering, synthetic genomics
B. subtilis	Bacterium	Secretion capability, industrial use	Enzyme production, fermentation
CHO cells	Mammalian	Proper protein folding, glycosylation	Therapeutic protein production
Cyanobacteria	Bacterium	Photosynthetic, CO₂ fixation	Biofuels, chemicals from CO₂

21.11.5 Applications by Sector

Sector	Examples	Impact
Medicine	Artemisinin production, CAR-T cells, diagnostics	Improved treatments, lower costs, new therapies
Industry	Biofuels, bioplastics, flavors/fragrances	Sustainable production, reduced environmental impact
Agriculture	Nitrogen fixation, disease resistance, biofortification	Increased yield, reduced fertilizer/pesticide use
Environment	Bioremediation, biosensing, carbon capture	Pollution cleanup, monitoring, climate mitigation
Basic Science	Minimal genomes, synthetic cells, origin of life	Understanding fundamental principles of life

21.11.6 Safety Strategies

Strategy Type	Methods	Purpose
Physical containment	Closed systems, HEPA filters, negative pressure	Prevent escape of engineered organisms
Biological containment	Auxotrophy, toxin-antitoxin, kill switches	Limit survival outside lab/fermenter
Genetic firewalls	Recoded genomes, XNA, orthogonal systems	Prevent horizontal gene transfer
Environmental	Nutrient limitations, temperature sensitivity	Limit persistence in environment
Reversibility	Gene drives with reversal mechanisms	Ability to undo modifications

21.12 Review Questions

21.12.1 Level 1: Recall and Understanding

Define synthetic biology and explain how it differs from traditional genetic engineering.
Describe the four phases of the design-build-test-learn cycle.
What are the three main types of chassis organisms used in synthetic biology, and what are their advantages?
List three applications of synthetic biology in medicine and three in environmental remediation.
Explain the concept of a genetic circuit and give two examples.

21.12.2 Level 2: Application and Analysis

Design a simple genetic circuit that turns green only in the presence of chemical A AND chemical B, but not in the presence of either alone. What components would you need?
Why might a genetic part work well in one organism but poorly in another, even if both are bacteria?
A company wants to engineer yeast to produce an expensive plant-derived compound. What steps would they follow using synthetic biology approaches?
Compare and contrast the ethical considerations for releasing a genetically engineered bacterium for bioremediation versus a gene drive for mosquito control.
How does the standardization of biological parts enable more complex engineering projects?

21.12.3 Level 3: Synthesis and Evaluation

Evaluate the claim that synthetic biology will enable us to “program life like we program computers” within the next 20 years.
Design a safety strategy for a genetically engineered microbe intended for large-scale environmental release to clean up oil spills.
How might synthetic biology contribute to addressing climate change? Consider both mitigation and adaptation strategies.
Propose a framework for international governance of synthetic biology that balances innovation, safety, and equity.

21.13 Key Terms

Synthetic biology: Design and construction of new biological systems
Genetic circuit: Engineered network of genes that performs a specific function
Chassis: Host organism used to implement synthetic biological systems
BioBrick: Standardized DNA part with defined interfaces
Design-build-test-learn (DBTL): Iterative engineering cycle
Minimal genome: Smallest set of genes necessary for life
Orthogonality: Biological components that function independently of host systems
Xenobiology: Study and engineering of biological systems with alternative biochemistries
Gene drive: Genetic system that biases inheritance to spread through populations
Cell-free system: Biological reactions without intact cells
Biological containment: Strategies to limit survival of engineered organisms
Standard biological parts: DNA sequences with defined functions and standardized interfaces

21.14 Further Reading

21.14.1 Books

Church, G. M., & Regis, E. (2012). Regenesis: How Synthetic Biology Will Reinvent Nature and Ourselves. Basic Books.
Endy, D. (2005). Foundations for engineering biology. Nature, 438(7067), 449-453.
Benner, S. A., & Sismour, A. M. (2005). Synthetic biology. Nature Reviews Genetics, 6(7), 533-543.

21.14.2 Scientific Articles

Gibson, D. G., et al. (2010). Creation of a bacterial cell controlled by a chemically synthesized genome. Science, 329(5987), 52-56.
Purnick, P. E., & Weiss, R. (2009). The second wave of synthetic biology: from modules to systems. Nature Reviews Molecular Cell Biology, 10(6), 410-422.
Cameron, D. E., Bashor, C. J., & Collins, J. J. (2014). A brief history of synthetic biology. Nature Reviews Microbiology, 12(5), 381-390.

21.14.3 Online Resources

iGEM (International Genetically Engineered Machine): https://igem.org
Synthetic Biology Open Language (SBOL): https://sbolstandard.org
JBEI-ICE (Inventory of Composable Elements): https://public-registry.jbei.org
Synthetic Biology Project (Woodrow Wilson Center): https://www.synbioproject.org

21.15 Quantitative Problems

Genetic Circuit Modeling: Consider a simple toggle switch with two repressors A and B.
- Production rate of A = α₁/(1 + [B]ⁿ) - δ₁[A]
- Production rate of B = α₂/(1 + [A]ⁿ) - δ₂[B] Where α₁ = α₂ = 10, δ₁ = δ₂ = 1, n = 2 (Hill coefficient)
1. Solve for steady states ([A], [B]) where d[A]/dt = d[B]/dt = 0
2. Plot the nullclines and identify stable states
3. How does changing α₁ to 15 affect the system?
Metabolic Burden Calculation: A synthetic pathway adds 5 proteins to a cell, each 300 amino acids. Transcription/translation costs: ~4 ATP per amino acid Protein degradation: ~0.1 per hour per protein Cell division: Every 30 minutes
1. Calculate ATP cost per cell cycle for the pathway
2. If cell has 10⁸ ATP per cycle for protein synthesis, what fraction is used?
3. How might this burden affect growth rate?
Containment Probability: An engineered bacterium has:
- Probability of escape from fermenter: 10⁻⁶ per day
- Probability of survival in environment: 10⁻⁴
- Probability of horizontal gene transfer: 10⁻⁷ per encounter
- Wild type encounter rate: 10⁴ per day in environment
1. What’s the probability of gene transfer per day of operation?
2. If operated for 100 days, what’s the overall probability?
3. How do these probabilities change with additional containment strategies?

21.16 Case Study: Artemisinin Production

Background: Artemisinin is a potent anti-malarial drug derived from the sweet wormwood plant. Traditional production was limited by plant cultivation, leading to price volatility and supply shortages.

Synthetic Biology Solution: Researchers engineered yeast to produce artemisinic acid, a precursor that can be chemically converted to artemisinin.

Questions:

What metabolic pathway modifications were needed in yeast?
How did synthetic biology approaches differ from traditional plant breeding?
What were the economic and health impacts of this achievement?
What challenges were encountered in scaling up production?
How does this case illustrate the design-build-test-learn cycle?

Data for analysis:

Pathway: Introduced genes from Artemisia annua and other species
Titers: From 0 to >25 g/L artemisinic acid
Cost reduction: Artemisinin combination therapy cost reduced by ~70%
Production time: From ~14 months (plant) to ~1 week (fermentation)
Partners: Academic labs, Bill & Melinda Gates Foundation, Sanofi

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