Week 7 HW: Gene Synthesis & Genome Engineering

Part 1. Intracellular Artificial Neural Networks (IANNs)

Question 1: Advantages of IANNs over Traditional Boolean Genetic Circuits

Traditional genetic circuits implement Boolean logic — genes are essentially ON or OFF, and circuits are built by wiring together AND, OR, and NOT gates. While powerful for simple decisions, this approach has fundamental limitations that IANNs overcome:

In summary, IANNs exploit the inherently analog nature of biology (continuous protein concentrations, graded promoter responses) rather than fighting it. A single perceptron equation replaces layers of logic gates, making circuits simpler to design, more robust to noise, and far more flexible in the computations they can perform.

Question 2: Application: Multi-Biomarker Cancer Diagnostic IANN

System overview

An IANN-based diagnostic circuit inside engineered immune cells (e.g., CAR-T cells) that detects circulating tumor markers and classifies cancer risk by integrating multiple biomarker signals simultaneously — something a Boolean circuit would struggle to do with graded, noisy biological inputs.

Input/output behavior

Inputs (analog biomarker signals):

Computation: The perceptron computes a weighted sum: if (w₁·X₁ + w₂·X₂ + w₃·X₃ − threshold) exceeds a decision boundary, classify as “high cancer risk.” Unlike a Boolean AND gate that requires all markers above a sharp threshold, the IANN performs soft classification — one strong marker plus two moderate ones can still trigger a positive result, better reflecting clinical reality.

Output: Graded GFP fluorescence proportional to cancer risk score (low = healthy, high = danger). Above a critical threshold, a second output activates: synthesis of a therapeutic cytokine (e.g., IL-2) to recruit immune cells to the tumor site.

Limitations

Biological noise: Stochastic fluctuations in mRNA/protein levels can cause the perceptron output to oscillate around the decision boundary, leading to false positives/negatives. Robust threshold setting and temporal integration would be needed.

Weight tuning: The weights w₁, w₂, w₃ must be calibrated to clinically relevant biomarker ranges. Weights optimized for one patient population may not generalize to others.

Metabolic burden: Expressing multiple sensor proteins, the computational circuitry, and therapeutic outputs creates significant metabolic load on the host cell, potentially affecting viability and computational fidelity.

Scalability: Expanding to 10+ biomarkers requires wider input layers or multilayer architectures, increasing complexity and potential crosstalk between genetic components.

Leakiness: Genetic components are never perfectly switch-like — leaky transcription and variable Hill coefficients introduce nonlinearities not perfectly captured by the idealized perceptron model.

Question 3: Multilayer Perceptron Circuit Architecture

Multilayer Perceptron Diagram

Below is my perceptron diagram for the intracellular multilayer perceptron. Layer 1 integrates two transcription factor inputs and produces the endoribonuclease Csy4. Layer 2 uses Csy4 to regulate GFP output through cleavage of the GFP mRNA.

How information flows

Layer 1 (Hidden Layer): Two upstream input signals (e.g., promoter activities driven by small molecules like IPTG and aTc) are integrated through weighted regulation. The “weights” are implemented biologically as promoter strengths and ribosome binding site (RBS) efficiencies — stronger promoters or optimized RBS sequences correspond to higher weights (w₁, w₂). The summed transcriptional output drives expression of the endoribonuclease Csy4. The bias term (θ) corresponds to basal promoter leakiness.

Layer 2 (Output Layer): A constitutive promoter drives GFP mRNA that contains a Csy4 recognition hairpin (28-nt sequence) in its 5’ UTR. When Csy4 is present, it specifically cleaves this hairpin, destabilizing the GFP mRNA and reducing fluorescent output. This creates an inhibitory (inverting) connection — shown with a blunt-end bar in the diagram:

• High Layer 1 activation → high Csy4 concentration → extensive GFP mRNA cleavage → low GFP fluorescence
• Low Layer 1 activation → low Csy4 concentration → intact GFP mRNA → high GFP fluorescence

Key advantage over single-layer: The hidden layer performs a weighted, nonlinear transformation of the raw inputs before passing the result to the output layer. This enables the circuit to compute functions (like XOR) that a single-layer perceptron fundamentally cannot.

Part 2. Fungal Materials

Question 1: Existing Fungal Materials: Examples, Uses, Advantages & Disadvantages

Advantages over traditional counterparts

Biodegradable and compostable: Mycelium materials decompose in weeks and enrich soil, versus Styrofoam’s 500+ year persistence in landfills.

Grows on waste: Mycelium feeds on abundant agricultural waste (hemp hurd, straw, sawdust), turning waste streams into valuable materials — a circular economy in action.

Carbon-neutral production: No petroleum extraction or energy-intensive manufacturing. The fungus metabolizes substrate carbohydrates at ambient temperature.

Customizable properties: By varying fungal species, substrate, and growth conditions, material properties (density, strength, flexibility) can be tuned for specific applications.

Fire resistance: Mycelium composites are naturally self-extinguishing with low smoke production — superior to many synthetic foams.

Fast production: Packaging materials grow in 5–10 days; leather in 4–9 days.

Disadvantages

Moisture sensitivity: Mycelium composites can absorb >200% of their weight in water, causing mechanical weakening. This limits outdoor applications.

Low mechanical strength: Compressive strength of 0.05–0.18 MPa restricts use to non-structural, non-load-bearing applications.

Cost: Currently more expensive than petroleum-based alternatives (polystyrene costs a few cents per cubic foot).

Scalability: Manufacturing processes are still being standardized. Batch-to-batch consistency remains a challenge.

Short lifespan for packaging: The very biodegradability that makes mycelium appealing also limits shelf life for long-distance shipping.

Consumer perception: “Grown” materials can face skepticism about durability and quality compared to established synthetics.

Question 2: Genetic Engineering of Fungi & Advantages Over Bacteria

What would I engineer fungi to do?

1. Enhanced material properties: Engineer mycelium to produce hydrophobic surface compounds (reducing water absorption from >200% to <30%), express structural proteins that reinforce hyphal networks (increasing compressive strength 10-fold), or deposit mineral compounds during growth for fire resistance. This would address the main disadvantages of current mycelium materials.

2. Bioremediation of plastics: Fungi naturally secrete powerful extracellular enzymes (laccases, peroxidases, hydrolases) that can break polymer bonds. Engineering Aspergillus or Penicillium species to overexpress plastic-degrading enzymes and broaden their substrate specificity could accelerate degradation of polyethylene, polystyrene, and polyurethane from months to days.

3. Complex protein production: Engineer filamentous fungi to produce recombinant spider silk (284.9 kDa, similar to natural), collagen for medical implants (avoiding BSE/prion risk from animal sources), or therapeutic antibodies. Fungi can properly fold these complex proteins in ways bacteria cannot.

4. Living materials with embedded function: Engineer mycelium to produce pigments, antimicrobial compounds, or even conductive polymers as it grows — creating materials with built-in functionality (self-coloring, self-sterilizing, or electrically conductive composites).

Advantages of synthetic biology in fungi vs. bacteria

Part 3. First DNA Twist Order — EZH2 Y726D Expression Cassette

Step 1 — Choose Your Protein

Step 2 — Amino Acid Sequence (EZH2 Y726D)

Full-length EZH2 protein sequence from UniProt Q15910, with the Y726D mutation (position 726: Y → D, highlighted below):

The mutation site is in the SET domain catalytic pocket. Wild-type has Y726 (Tyrosine), which participates in SAM cofactor positioning. Replacing it with D (Aspartic acid) disrupts methyltransferase activity while maintaining overall protein folding.

Step 3 — Reverse Translation & Codon Optimization

The EZH2 Y726D protein sequence was reverse-translated to DNA and codon-optimized for human (mammalian) expression using the most frequently used codons from the Homo sapiens codon usage table (Kazusa database). Key optimization metrics:

Step 4 — Expression Cassette Design

The insert is designed for cloning into the pTwist CMV mammalian expression backbone from Twist Bioscience. Since the backbone already provides the CMV promoter and bGH polyA terminator, the insert contains:

Step 5 — Backbone Vector

Step 6 — Complete Insert DNA Sequence (2,265 bp)

This is the full insert to be ordered from Twist Bioscience as a clonal gene in pTwist CMV:

Step 7 — Twist Bioscience Order Summary

Design Rationale Summary

The EZH2 Y726D catalytically dead mutant is the cornerstone construct for our final project on non-canonical PRC2 functions in partial reprogramming. By expressing a version of EZH2 that can still assemble into PRC2 and bind chromatin but cannot methylate H3K27, we can decouple the enzymatic activity from the structural scaffolding role. This allows us to ask: does PRC2 contribute to reprogramming through histone methylation, or through physical chromatin organization and transcription factor sequestration?

The mammalian codon optimization ensures high expression levels in HEK293T or iPSC-derived cells. The C-terminal 6×His tag enables purification and western blot detection without disrupting the N-terminal domains critical for PRC2 complex assembly (EED and SUZ12 binding interfaces).