Week 7 HW: genetic circuits part ii
Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)
1. Advantages of IANNs over traditional Boolean genetic circuits
Intracellular Artificial Neural Networks (IANNs) offer several advantages compared to traditional genetic circuits that rely on Boolean logic:
First, IANNs enable graded and continuous responses rather than strictly binary outputs (ON/OFF). This allows cells to process signals in a more nuanced way, similar to natural biological systems, where gene expression levels vary continuously.
Second, IANNs can integrate multiple inputs simultaneously with weighted contributions, instead of relying on rigid logical gates (e.g., AND, OR). This makes them more flexible and scalable for complex decision-making tasks.
Third, IANNs are capable of learning-like behavior and adaptability (at least in design or through tunable parameters), which allows for optimization of responses without redesigning the entire circuit.
Finally, IANNs support higher computational complexity within a single cell, enabling implementation of functions that would require many layers of traditional Boolean circuits.
2. Example application of an IANN
A useful application of an IANN is in smart disease diagnostics and targeted therapy, particularly in detecting cancer-specific cellular states.
Input/Output behavior:
Inputs (X₁, X₂, X₃, etc.): expression of multiple biomarkers, such as microRNAs, transcription factors, or metabolites associated with cancer.
Each input is assigned a weight, reflecting its importance in identifying the disease state.
The IANN processes these inputs through a weighted sum and activation function.
Output: a therapeutic response, such as expression of a cytotoxic protein or activation of apoptosis, only when the combined signal exceeds a defined threshold.
This allows the system to distinguish between healthy and diseased cells with greater precision than simple Boolean logic.
Limitations:
Biological noise: Variability in gene expression can affect the reliability of weighted computations.
Limited scalability: Increasing the number of inputs may lead to metabolic burden and circuit instability.
Tuning challenges: Precisely adjusting weights and thresholds in living cells is difficult.
Response time: Cellular processes (transcription/translation) are slower than electronic systems.
3. Explanation of the intracellular single-layer perceptron
A perceptron is the simplest type of artificial neural network, consisting of inputs, weights, a weighted sum, and an activation function that produces an output.
In the intracellular system described:
X₁ (Csy4 endoribonuclease DNA): Acts as an input that produces the Csy4 protein after transcription (Tx) and translation (Tl).
X₂ (fluorescent protein DNA): Produces mRNA that encodes a fluorescent protein, but this mRNA is regulated by Csy4.
Mechanism:
The presence of Csy4 (from X₁) affects the stability or processing of the mRNA from X₂.
This interaction effectively assigns a weight to X₁’s influence over the output.
The fluorescent protein level represents the output (Y).
Perceptron analogy:
Inputs: X₁ and X₂
Weight: Regulatory effect of Csy4 on the fluorescent protein mRNA
Summation: Combined effect of transcription and regulation
Activation function: Threshold-like behavior in protein expression (e.g., fluorescence only above a certain level)
Thus, this system behaves like a biological perceptron, where gene expression and RNA regulation implement the mathematical operations of weighted input summation and activation.
Assignment Part 2: Fungal Materials
1. Examples of existing fungal materials, their uses, advantages, and disadvantages
Several fungal (mycelium-based) materials already exist and are being used in different industries:
Examples and uses:
Mycelium-based packaging: Used as a biodegradable alternative to plastic foams (e.g., Styrofoam).
Construction materials: Mycelium composites can be used as insulation panels or lightweight building blocks.
Textiles and leather-like materials: Flexible fungal materials are used as sustainable alternatives to animal leather.
Acoustic and thermal insulation: Due to their structure, mycelium materials are effective at sound absorption and heat resistance.
Advantages over traditional materials:
Sustainability: They are biodegradable, renewable, and can be grown on agricultural waste.
Low energy production: They require less energy compared to plastics or synthetic materials.
Environmental benefits: They reduce pollution and can even sequester carbon during growth.
Fire resistance and non-toxicity: They do not contain harmful chemicals and can resist heat.
Disadvantages:
Lower mechanical strength: They are often weaker than traditional materials like plastics or concrete.
High moisture sensitivity (hygroscopicity): They can absorb water easily, affecting durability.
Biological degradation: They can be susceptible to microbial decay if not properly treated.
Production challenges: Growth requires controlled conditions and consistency can be an issue.
2. Genetic engineering of fungi and advantages over bacteria
One interesting goal for genetically engineering fungi would be to create living materials that can self-heal or respond to environmental stimuli.
Example application:
Engineer fungi to produce self-repairing building materials that can detect cracks and regrow to repair structural damage.
Inputs: Environmental signals such as mechanical stress, cracks, or humidity.
Outputs: إنتاج enzymes or new mycelial growth that reinforces the damaged area.
Another application could be:
- Bioremediation fungi engineered to degrade plastics, toxins, or pollutants more efficiently.
Why fungi? Advantages over bacteria:
Ability to form complex structures: Fungi naturally grow as multicellular networks (mycelium), which is ideal for building materials, unlike bacteria that are mostly unicellular.
Secretion of enzymes: Fungi efficiently secrete enzymes to break down complex substrates (e.g., lignin, plastics), making them powerful for industrial and environmental applications.
Growth on low-cost substrates: They can grow on agricultural waste (e.g., sawdust, straw), reducing production costs.
Higher metabolic complexity: Fungi can produce more complex molecules (e.g., secondary metabolites) than many bacteria.
Greater tolerance to contamination: Some fungi are more robust in non-sterile conditions compared to bacteria.
Limitations:
Genetic engineering in fungi is often more complex and slower than in bacteria.
Growth rates are generally slower than bacterial systems.
Regulatory and scalability challenges still exist.