Week 7 HW: Genetic Circuits Part II

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

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

One of the core advantages of IANNs over traditional genetic circuits is that IANNs can be used for complex computations whereas traditional genetic circuits are restricted to simpler digital operations. Moreover, IANNs have a higher predictability than traditional genetic circuits and can be modified to perform precise therapeutic functions.

Traditional genetic circuits also struggle with scalability, as stochastic fluctuation of genetic expression and molecular concentrations cause noise. Similar to ANNs, IANNs smooth out input perturbations through by distributing these stochastic fluctuations across nodes and pathways,

Sources:

Ameen Eetemadi, Ilias Tagkopoulos, Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships, Bioinformatics, Volume 35, Issue 13, July 2019, Pages 2226–2234, https://doi.org/10.1093/bioinformatics/bty945

Next-generation biocomputing: mimicking artificial neural network with genetic circuits Leo Chi U Seak, Owen Lok In Lo, Wade Chun-Wai Suen, Ming-Tsung Wu bioRxiv 2021.03.12.435120; doi: https://doi.org/10.1101/2021.03.12.435120

2. 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.

For my final project aim 1 I intend to develop a genetic circuit implemented in e.coli: a

For aim 1 of my final project, I propose implementing an intracellular artificial neural network (IANN) as a genetic circuit in E. coli chassis cells applied to real-world ermine moth silk, which serves as the substrate source for sericin-2; BmCoc enzymatic degradation of this silk-derived sericin-2 triggers GFP fluorescence readout.

Inputs are spatially localized BmCoc activity levels on the silk (sensed via cleavage products or substrate modifications contacting the chassis), feeding into layer 1 sensor modules that compute a weighted sum through regulatory proteins or RNA processors, with nonlinearity from thresholded promoter logic.

The layer 1 output (e.g., an endoribonuclease) regulates a layer 2 promoter driving GFP, yielding low fluorescence without degradation and high GFP above a tunable threshold when silk sericin-2 is broken down, enabling binary or analog reporting via microscopy or flow cytometry of silk-colonizing cells. This allows live quantification of enzymatic silk processing directly on the natural substrate.

Limitations include poor specificity (crosstalk from moth silk contaminants or host pathways), gene expression noise blurring single-cell outputs on heterogeneous silk fibers, fixed genetic "weights" limiting adaptability, metabolic burden under silk-adherent growth conditions, and kinetic mismatches between rapid proteolysis and slow transcription/translation. Despite these, the IANN provides a modular platform for screening BmCoc variants on authentic ermine moth silk and multi-input enzymatic classification in a substrate-embedded context.

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

In the first layer the transcription rate of the Cys4 DNA forms the weighted value of the Cys4 translation rate, then in layer two the transcription of the GFP DNA sequence as well as the output of Cys4 translation inform the weighted value that dictates the translation of the GFP protein

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?

Over the past decade, a growing interest in fungal materials as a sustainable substitute for traditional, often plastic-based, materials. One example of fungal materials is mycelium packaging as a replacement for polystyrene packaging. This replacement material is completely biodegradable, shock absorbent and has a negative carbon footprint. However it is much more costly to produce and it takes a lot more time to develop.

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?

Fungi are increasingly becoming a domain of interest in regards to bioremediation strategies, showing promising potential in the remediation of heavy metals, synthetic dyes, hydrocarbons and more. (Dinakarkumar, et al., 2024).

The reality of bioremediative initiatives however, is the possibility that these efforts are not a one-time solution. As long as pollutive industries continue to operate, it is not possible to say with full certainty that bioremediative efforts can be stopped, still leaving local communities vulnerable to the ramifications of exposure to these pollutants. Quick, accessible communication of ecological safety is therefore an important public health concern. I propose to genetically engineer fungi to produce differently coloured fruiting bodies when degrading pollutants in the soil. This would give a direct indicator of soil health and safety.

When comparing the application of synthetic biology to fungi to that of bacteria, a key advantage in favour of fungi is the fact that filamentous fungi excel in protein secretion and are particularly suited for producing complex enzymes due to their ability to perform post-translational modifications. (Garg, 2026)

References

Yuvaraj Dinakarkumar, Gnanasekaran Ramakrishnan, Koteswara Reddy Gujjula, Vishali Vasu, Priyadharishini Balamurugan, Gayathri Murali,

Fungal bioremediation: An overview of the mechanisms, applications and future perspectives, Environmental Chemistry and Ecotoxicology, Volume 6, 2024, Pages 293-302,

ISSN 2590-1826, https://doi.org/10.1016/j.enceco.2024.07.002.

Shilpa Garg, The importance of fungal biotechnology for sustainable applications, Trends in Biotechnology, Volume 44, Issue 1, 2026, Pages 79-91, ISSN 0167-7799, https://doi.org/10.1016/j.tibtech.2025.06.010.