Week 07: Genetic Circuits Part-II

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

Q1. Advantages of IANNs over traditional Boolean genetic circuits

Traditional genetic circuits are limited to discrete ON/OFF outputs — they can only compute simple logic like AND, OR, NOT. IANNs go beyond this by processing continuous, graded inputs and computing weighted sums across multiple signals simultaneously, just like neurons. This means a single cell can integrate many environmental signals at once and produce nuanced, analog responses rather than just a binary switch. IANNs can also be trained — their weights (gene expression levels) can be tuned to classify complex input patterns. This makes them far more powerful for tasks like disease detection inside a cell, where multiple biomarkers need to be weighed together rather than evaluated individually.

Q2. Useful application for an IANN — Cancer Detection and Response

Application

An IANN engineered into immune cells (like T cells) that detects cancerous cells based on multiple surface protein markers and triggers a therapeutic response.

Input behavior

The IANN receives multiple molecular inputs simultaneously — for example, the presence of tumor antigens (e.g., HER2, PD-L1, EGFR) at varying concentrations on a target cell’s surface. Each input signal is weighted differently depending on its relevance to cancer identity.

Output behavior

If the weighted sum of inputs exceeds a threshold, the T cell activates and releases cytotoxins to kill the target cell. If the threshold is not met (i.e., a healthy cell expressing only one marker at low levels), no response is triggered.

Why IANN is better than Boolean here

A Boolean circuit might trigger on HER2 alone, which is also expressed on some healthy cells — causing toxicity. The IANN integrates all markers with learned weights, making the decision far more precise.

Limitations

• Slow response time — gene expression and protein production takes hours, unlike electronic neural networks
• Difficult to “retrain” weights inside a living cell once deployed
• Cell-to-cell variability in gene expression can cause inconsistent behavior
• Limited number of orthogonal molecular parts (RNases, promoters) available for building complex layers

Q3. Multilayer perceptron diagram description

Layer 1 (Input layer)

• Input X1: DNA encoding Promoter A → Csy4 endoribonuclease (transcription Tx → translation Tl → Csy4 protein)
• Input X2: DNA encoding Promoter B → a second regulatory RNA/protein (e.g., another RNase or transcription factor)
• Both are weighted by their respective promoter strengths (w1, w2)
• Layer 1 output: Csy4 protein concentration (and optionally a second regulator)

Layer 2 (Output layer)

• Input to Layer 2: Csy4 from Layer 1 acts on the mRNA of the fluorescent protein (cleaving or stabilizing it depending on circuit design)
• A second weight (w3) is applied via the RBS strength controlling translation of the fluorescent protein
• Layer 2 output: Fluorescent protein expression level — a continuous analog output proportional to the combined weighted inputs from Layer 1

In plain words: X1 and X2 are transcribed and translated in Layer 1 → their protein products regulate mRNA processing in Layer 2 → fluorescent protein output is produced in proportion to the weighted sum of both inputs.

Assignment Part 2: Fungal Materials

Q1. Examples of existing fungal materials

Overall advantages over traditional materials: fully biodegradable, carbon-neutral production, grown using waste substrates, no petrochemicals. Disadvantages: currently higher cost, variable mechanical properties, limited scalability.

Q2. What to genetically engineer fungi to do, and why

I would engineer fungi to produce BDNF (Brain-Derived Neurotrophic Factor) or other neuroprotective proteins as a sustainable bioproduction platform. Fungi like Aspergillus niger or Pichia pastoris are already established as industrial protein secretion hosts.

What to engineer

• Insert the codon-optimized BDNF gene under a strong inducible fungal promoter
• Add a secretion signal peptide so BDNF is secreted directly into the growth medium for easy harvesting
• Engineer glycosylation patterns to match human BDNF for therapeutic use

Why fungi over bacteria