Week 7: Genetic Circuits 2

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions?

The main advvantage I see of an IANN over a traditional genetic circuit (Boolean in nature) is that the IANN seems better suited for biological situations that require a more sophisticated estimation than a sharp TRUE/FALSE. We would need more ‘sensitive tools’ if we are to deal with many inputs, gradients, dynamic thresholds and intermidiate states - for all mentioned, binary logic is insufficient. I’m interested in cultivated meat, and in this course I will try to demonstrate control over marbeling of fat and meat tissues. This concept matters here because fat distribution is not a binary problem but a morphogentic spatial multi-input issue. IANN’s can seem more relevant when the goal is to account for several factors and generate a site-specific nuanced output.

Describe a useful application for an IANN; include a detailed description of input/output behavior, as well as any limitations an IANN might face to achieve your goal.

A useful application for an IANN, in the context of my meat-fat marbling catalogue direction, would be a system that helps decide where cells in a growing tissue should become more fat-like, more muscle-like, or remain in an intermediate state based on several inputs at once. The inputs could include things like oxygen level, nutrient availability, local signaling molecules, and maybe cell density or position within the scaffold, and the output would be a graded patterning decision rather than a single differentiation switch. What interests me is that this could eventually help produce different marbling motifs or classes instead of just maximizing fat everywhere. A limitation, though, is that a system like this would probably be very sensitive to diffusion, timing, signal decay, and spatial arrangement, so even if the logic works conceptually, getting a stable and precise pattern in real tissue would be difficult. Some of the papers I’ve been reading make that feel plausible as a direction, but also make clear how quickly these systems become messy once spatial biology is involved.

Draw a diagram for an intracellular multilayer perceptron where layer 1 outputs an endoribonuclease that regulates a fluorescent protein output in layer 2.

Assignment Part 2: Fungal Materials

What are some examples of existing fungal materials and what are they used for? What are their advantages and disadvantages over traditional counterparts?

One example of an existing fungal material is mycelium-based packaging or building material, where the fungal network grows through agricultural waste and binds it into a lightweight solid. I find that interesting because it can replace things like foam packaging or some synthetic insulation materials while using low-value waste as feedstock. The main advantages are that it is more biodegradable, potentially lower-emission, and can be grown rather than heavily manufactured. At the same time, the disadvantages are that it is usually less standardized and sometimes less strong, water-resistant, or durable than conventional materials, so it can be harder to use in situations where reliability and repeatability matter a lot.

What might you want to genetically engineer fungi to do and why? What are the advantages of doing synthetic biology in fungi as opposed to bacteria?

If I were engineering fungi, I would be interested in improving the mechanical properties of fungal materials, for example making them stronger or more consistent so they become more useful as design or construction materials. That feels meaningful to me because a lot of the promise of mycelium materials depends not just on being sustainable, but on actually performing well enough to compete with existing options. An advantage of working in fungi instead of bacteria is that fungi already naturally grow as large material-forming networks, so they are better suited for building structural biomaterials rather than just producing molecules in liquid culture. The downside is that fungi are usually slower and more complex to engineer than bacteria, but for material applications they may still be the more relevant organism.

References:

Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H., & Weiss, R. (2005). A synthetic multicellular system for programmed pattern formation. Nature, 434(7037), 1130–1134.

Mordvintsev, A., Randazzo, E., Niklasson, E., & Levin, M. (2020, February 11). Growing neural cellular automata. Distill, 5(2), e23. https://distill.pub/2020/growing-ca/

GeeksforGeeks. (n.d.). The perceptron, the basis of artificial neural networks. https://www.geeksforgeeks.org/deep-learning/what-is-perceptron-the-simplest-artificial-neural-network

Vasle, A. H., & Moškon, M. (2024). Synthetic biological neural networks: From current implementations to future perspectives. BioSystems, 245, 105164. https://doi.org/10.1016/j.biosystems.2024.105164