Week 7 HW: Genettic Circuits: Part II

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

1. What advantages do IANNs have over traditional genetic circuits?

In my research, I found that IANNs offer a much more flexible way to handle biological data compared to standard Boolean (ON/OFF) circuits. Here are the main benefits:

• Graded vs. Binary Responses: While Boolean gates force a sharp decision, IANNs treat molecules as continuous inputs. This allows the cell to compute a proportional response (like partial induction) rather than just being fully “on” or “off.”
• Multivariate Integration: IANNs can sum up multiple weighted inputs at once. This lets them perform pattern recognition and complex classification that simple logic gates can’t handle.
• Noise & Fault Tolerance: IANNs are much more robust. In a Boolean gate, a single signal crossing a threshold can flip the whole output (brittleness). In an IANN, since it’s an analog sum, noise in one specific regulator has a limited effect on the final result.
• Adaptability: The “weights” and thresholds of these networks can be tuned or trained through directed evolution, making them easier to optimize than rigid digital circuits.

2. Application: Sentinel Implant Probiotic

I propose using an IANN to create a “Guardian” probiotic (using Lactobacillus reuteri) designed to prevent infections around dental implants.

• Input Behavior: The bacteria would monitor two specific analog cues:
  1. AHL concentration (pathogen quorum-sensing molecules).
  2. Local pH levels (which drop when acid-producing pathogens are present).
• Output Behavior: These inputs are weighted within the cell. If the weighted sum of “AHL + Low pH” hits a certain threshold, the probiotic expresses an antimicrobial peptide at a proportional level.
• The Goal: This creates a “soft AND” gate, ensuring the antimicrobial is only produced during actual dysbiosis, which protects the healthy oral flora.
• Limitations: Setting precise biological weights is difficult. We also have to worry about signal leakage (AHL diffusing away) and the long-term stability of the engineered strain in a competitive biofilm.

3. Multilayer Perceptron Diagram

Below is a conceptual layout for a two-layer IANN:

• Layer 1: Takes inputs $X_1$ and $X_2$ (DNA-encoded regulators) and a bias. Their weighted sum drives the production of an endoribonuclease (ERN-A).
• Layer 2: Uses the ERN-A from Layer 1 as a negative weight (it cleaves the mRNA of the output) and integrates it with a third input, $X_3$.
• Output: The final fluorescent protein reflects a cascaded computation, allowing for a more complex “decision boundary” than a single-layer model.

Assignment Part 2: Fungal Materials

1. Existing Fungal Materials: Use Cases, Pros, and Cons

Fungi provide a versatile range of materials that can replace traditional plastics and leathers.

2. Genetic Engineering in Fungi

What I would engineer: I’d want to engineer filamentous fungi (like Aspergillus) to secrete human growth factors (e.g., BMP-2) for bone and dental tissue regeneration.

Why Fungi over Bacteria?

• Eukaryotic Processing: Fungi have the Golgi and ER needed to perform post-translational modifications (like glycosylation and disulfide bonding). Bacteria like E. coli often fail at this, leaving proteins unfolded or inactive.
• High Secretion Capacity: Industrial fungi are powerhouses; they can secrete up to 100 g/L of protein, which is far beyond what most bacterial systems can do.
• Safety: Fungi don’t produce endotoxins, making the purification process for medical-grade human proteins much simpler and safer.

Final Project: DNA Design & Backbone Documentation

Backbone Vector Details

The insert sequence will be synthesized and cloned into the pET28a expression vector, obtained from Addgene.

Key Features of pET28a:

• Promoter: Carries a T7 promoter for high-level, IPTG-inducible expression.
• Selection Marker: Includes a kanamycin resistance cassette for reliable bacterial selection.
• Purification Tag: Features an N-terminal His-tag, allowing for efficient protein purification via IMAC (Immobilized Metal Affinity Chromatography).

Experimental Context:

This backbone is widely validated for recombinant protein production in E. coli BL21(DE3) and is directly compatible with the final project’s experimental aim. By using this standardized vector, I ensure that the synthesized DNA can be expressed and verified using established laboratory protocols.

Progress Checklist for March 20 Deadline:

• Draft Aim 1 and Project Summary.
• Select HTGAA Industry Council members.
• Shared Benchling/Kernel folder created.
• Insert sequence designed and uploaded to shared folder.
• Backbone vector documented (above).