Hi, I am Isobel, a multidisciplinary artist from the UK. I create objects and spaces inspired by particular ecologies.
At the core of my work is material sensation and embodied experience, using the qualities of materials, movement and interaction to encourage new ways of seeing and feeling our complex relationship with the natural world. In 2025, I graduated from Fabricademy at the Waag Futurelab in Amsterdam. There, I was introduced to amazing ways of working at the intersection of textiles, bio and digital fabrication and became enamored with the ethical, poetic and aesthetic potential of bio and living materials for the visual arts and design. I am looking forward to throwing myself into the deep end and being challenged to think in different and deeper ways. I am particularly interested in researching bio-engineering applications in speculative bio-design and how it can be used as a tool to explore, critique and reimagine possible futures and relationships with other species.
Important Resources
Week 1 Lecture Recording
Week 1 Recitation Recording
Week 1 Recitation Slides
Engineered bacterial cellulose to make self-pigmenting, living textiles. I have chosen to explore a bioengineering application for creating more sustainable systems in the textile industry and more personally, an alternative material for use in design and the visual arts. Specifically, I am analysing the proposal of engineered bacterial cellulose to produce self-pigmenting textiles. In this analysis, I focus on preventing environmental harm, ensuring bio-safety, and equitable production in the deployment of this technology. I also briefly extend the discussion to the ethical implications of engineered bacterial cellulose for other bio-fabrication applications, such as increasing material yield, creating sensing and responsive materials, or affecting its mechanical structure, all applications that I am intrigued by and are seeds for potential final projects. Walker, K. T., Li, I. S., Keane, J., Goosens, V. J., Song, W., Lee, K.-Y., & Ellis, T. (2025). Self-pigmenting textiles grown from cellulose-producing bacteria with engineered tyrosinase expression. Nature Biotechnology, 43(3), 345–354. https://doi.org/10.1038/s41587-024-02194-3. Image from online article: https://www.nature.com/articles/s41587-024-02194-3
Important Resources
George Church lecture
Joe Jacobson lecture
Emily Leproust Lecture
Part 1: Benchling & In-silico Gel Art Using Benchling, I imported the Lambda DNA and began simulating the Restriction Enzyme Digestion with the following enzymes: EcoRI-HF, HindIII-HF, BamHI-HF, KpnI-HF, EcoRV-HF, SacI-HF, SalI-HF.
Important Resources
Recitation Recording
Recitation Slides
Lab Overview
FINAL PROJECT IDEAS: 3 Initial Proposals. Final Project Ideas by Isobel Leonard Homework Part 1: Python Script for Opentrons Artwork This week we are creating a Python file to run on an Opentrons OT-2 liquid handling robot to create flourescent designs. This is achieved by depositing E.coli genetically engineered to express different fluorescent proteins onto black agar plates. The flouresence will be visible under UV light and so will our designs!
Engineered bacterial cellulose to make self-pigmenting, living textiles.
I have chosen to explore a bioengineering application for creating more sustainable systems in the textile industry and more personally, an alternative material for use in design and the visual arts. Specifically, I am analysing the proposal of engineered bacterial cellulose to produce self-pigmenting textiles. In this analysis, I focus on preventing environmental harm, ensuring bio-safety, and equitable production in the deployment of this technology. I also briefly extend the discussion to the ethical implications of engineered bacterial cellulose for other bio-fabrication applications, such as increasing material yield, creating sensing and responsive materials, or affecting its mechanical structure, all applications that I am intrigued by and are seeds for potential final projects.
Walker, K. T., Li, I. S., Keane, J., Goosens, V. J., Song, W., Lee, K.-Y., & Ellis, T. (2025). Self-pigmenting textiles grown from cellulose-producing bacteria with engineered tyrosinase expression. Nature Biotechnology, 43(3), 345–354. https://doi.org/10.1038/s41587-024-02194-3. Image from online article: https://www.nature.com/articles/s41587-024-02194-3
1. Describe a biological engineering application or tool you want to develop and why.
I am inspired by the bio-engineered, self- pigmenting Bacterial Cellulous developed by the Future Materials Group (FMG) at Imperial College London in collaboration with Modern Synthesis. Their research responds to the environmental impact of textile production, largely through uncontrolled microplastic pollution from synthetic fabrics, water pollution from toxic, chemical dyes and its heavy contribution to greenhouse gas emissions. It is responsible for 10% of global carbon emissions,20% of global waste water, 35% of marine microplastic pollution and is expected to account for 25% of the global carbon budget by 2050. The question that interests me here is rather than manufacturing synthetic materials and colours, can we engineer materials to grow with the aesthetic properties we want?
Self-pigmenting bacterial cellulose offers a bio-degradable, non-toxic and grown alternative to synthetic fabrics and chemical dyeing. It builds upon the development of microbial leathers and dyes in the alternative textile field, pioneered by the likes of Suzanne Lee (Biocoutre), Laura Luchtman and Ilfa Siebenhaar (Living Colour) by adopting a synthetic biology approach. The Future Materials Group genetically engineering the bacterium Komagataeibacter rhaeticus to grow bacterial cellulose that is self pigmenting.
In carbon-rich growth media, Komagataeibacter metabolize sugars and converts them into cellulose chains, which are secreted outside the cell and self assemble into a fiber network called a pellicle. This pellicle is pure bacterial cellulose and forms the base material for the BC textile.
Next, the FMG engineered the biosynthesis of a pigment producing gene, in this case black melanin pigment, Eumelanin. To do this FMG inserted the tyrosinase gene (Tyr1) from Bacillus megaterium. Tyrosinase is an enzyme that converts tyrosine into dopachrome which eventually polymerises and aggregates to make black pigment. Using a modular Golden Gate cloning system, the Tyr1 gene is inserted into K. rhaeticus either on a plasmid or directly into the bacterial chromosome. As the engineered bacteria grow, they produce cellulose while also generating pigment, which becomes embedded throughout the pellicle. Instead of dyeing the textile afterward, the colour is produced from within the engineered living material itself. Additionally, research into engineered bacterial cellulose is opening up possibilities for creating living, programmable biomaterials with a wide range of functionalities. These include increased yield for scalability, embedded bio-sensing and responsive behaviours and the ability to effect its structure.
Such capabilities makes bacterial cellulose an exciting and versatile for sustainable applications in fashion, design and the visual arts, highlighting the importance of carefully considering the ethics guiding its development and broader adoption across these industries.
Walker, K. T., Li, I. S., Keane, J., Goosens, V. J., Song, W., Lee, K.-Y., & Ellis, T. (2025).
Self-pigmenting textiles grown from cellulose-producing bacteria with engineered tyrosinase expression.
Nature Biotechnology, 43(3), 345–354. Image: Eumelanin production from K. rhaeticus tyrosinase expression.
From: Self-pigmenting textiles grown from cellulose-producing bacteria with engineered tyrosinase expression.
Available at: https://www.nature.com/articles/s41587-024-02194-3/figures/1
2. Describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future.
1. Ensuring biosafety and laboratory containment:
The World Health Organization defines “Biosafety” in its Laboratory Biosafety Manual as “containment principles, technologies and practices that are implemented to prevent unintentional exposure to biological agents or their inadvertent release (World Health Organization, 2004).”
Genetically engineering Komagataeibacter rhaeticus to produce self-pigmenting bacterial cellulose introduces modified microbes into research and production environments. While K. rhaeticus is non-pathogenic and requires strict growth conditions to survive, the insertion of the tyrosinase gene (Tyr1) creates new biological capabilities which carry some risks of producing unintended, harmful effects to the health of humans and the environment if released.
It is particularly important to protect laboratory personell, scientists, designers, consumers and neighbouring communities from exposure to toxic, infectious or dangerous pathogens and to prevent their accidental release outside the laboratory with strict containment protocol.
2. Preventing harm to the environment:
Self-pigmenting BC textiles offer an environmentally friendly alternative to synthetic fabrics and chemical dyes, but the long-term ecological impact of introducing genetically engineered bacteria must be considered. It is important to ensure that the development, production and onward life of the Bacterial Cellulose Textile does not negatively affect the environment.
Genetic modifications and decisions made for scalability should not compromise biodegradability. Production systems should avoid petroleum-based plastics or toxic media. End-of-life disposal, industrial cultivation and potential escape into natural ecosystems should be monitored to prevent unintended ecological consequences.
3. Ensuring equity:
Access to self-pigmenting BC textiles should extend beyond large corporations and well-funded labs to designers, artisans, and educational institutions.
Development of this technology should ensure low-cost, modular production systems to enable the equitable participation of non-traditional or underprivileged communities in sustainable material creation.
Training in biosafety and handling of engineered microbes should accompany access, ensuring that communities can safely experiment with living textiles while benefiting from environmentally responsible materials.
4. Transparency and education:
Clear communication of the science, benefits and limitations of self-pigmenting BC is critical for stakeholder trust, informed consent and wider uptake of engineered living materials.
Designers, consumers, and regulators should understand how BC reduces reliance on petroleum based textiles and avoids toxic dyes. Transparency should include how the bacteria are engineered, the pigment synthesis process, and any potential environmental or health considerations.
Engaging the public through workshops, open-access resources and participatory design initiatives encourages adoption while supporting ethical decisions about integrating engineered living textiles into daily life.
5. Respect for life:
Self Pigmenting Bacterial cellulose is a living material produced by bacteria and ethical considerations should extend to the organisms themselves.
Scaling self-pigmenting BC production for textiles must balance functionality with respect for life, acknowledging the bacteria as active contributors to the material.
This perspective highlights a distinction between large-scale industrial production and smaller, artisanal or community-based approaches, emphasising that sustainability and innovation should be pursued without disregarding the living organisms at the core of bio-fabrication.
3. Describe at least three different potential governance “actions”, consider: Purpose, Design, Assumptions, Risks of Failure & Success.
I have outlined 3 categories of actors:
Researchers:
Those involved in the research and development of Self pigmenting Bacterial cellulose e.g Scientists, Universities, Research Centers, Community Bio-labs.
Producers:
Those involved in turning SPBC into actual products and for use in design and manufacturing e.g. Designers, artists, fashion designers, companies, bio-material start ups.
Regulators:
Those involved in control of safely deploying this technology on a local, national and international level e.g Local Authority Environmental Health Department, Waste Management Boards, Research Ethics Committees, Department of Environment, Food and Rural Affairs, The Health and Safety Executive.
1. Ensuring biosafety and laboratory containment
ACTION: Implement strict biosafety protocols and containment standards for research and production facilities handling engineered K. rhaeticus.
PURPOSE: Prevent accidental exposure or environmental release of genetically modified bacteria.
DESIGN:
Researchers should ensure that genetically engineered K. rhaeticus strains are safe and non-pathogenic.
Regulators should require Biosafety Level 1+ facilities, PPE, and biosafety training for all SPBC work.
Researchers should test bio-containment strategies such as kill-switches linked to the Tyr1 pigment pathway.
Producers should gain approval from institutional or local biosafety boards before scaling production.
Regulators should enforce sterilisation of spent cultures prior to disposal and include periodic risk and life-cycle assessments in SPBC workflows.
ASSUMPTION: I assume that risk to biosafety from K. rhaeticus is low and that it cannot survive in open environments. I assume that containment technologies and protocols are sufficient to prevent accidental release. I assume that all researchers and designers would adhere to training and protocol.
RISK OF FAILURE: Failure risks accidental release beyond the laboratory that could lead to unintended effect on the environment or humans.
RISK OF SUCCESS: Safe research and production is wide spread, increasing demand for DIY genetic engineering and biohacking to make SPBC, which may require more wide spread biosafety training and stricter protocol.
2. Preventing harm to the environment
ACTION: Require lifecycle assessment and contained environmental monitoring for large-scale production of self pigmenting bacterial cellulose.
PURPOSE: Ensure that self-pigmenting BC textiles remain a sustainable alternative to synthetical coloured fabrics and monitor its life cycle to ensure that use and disposal of genetic engineering materials has no unintended impact on the environment.
DESIGN:
Researchers should test large-scale cultivation and end-of-life treatment to prevent ecological pollution.
Producers should be required to submit life-cycle assessments covering carbon footprint, biodegradability, toxicity, and disposal.
Regulators should enforce compliance with local environmental and waste-management regulations for genetically engineered materials.
ASSUMPTION: I assume that engineered self-pigmented bacterial cellulose behaves consistently at different scales and with different production methods, when individual producers may need more tailored environmental restrictions. I assume that all environmental monitoring can accurately capture all possible environmental impacts long term. I assume that with scalability and commercialisation, all producers will maintain sustainability as a priority in producing SPBC.
RISK OF FAILURE: Genetic engineering and scalability effects the biodegradability of the material. Unforeseen environmental impact is created by the use and disposal of SPBC at scale.
RISK OF SUCCESS: Wide spread commercial uptake of SPBC creates challenges for scaled manufacturing and efficient cellulose yield, increasing environmental impact.
3. Ensuring equity, transparency and education
ACTION: Develop technology should also fund low-cost and accessible production available to smaller bio-production spaces. Researchers and producers would hold engagement and education initatives so that use of the sustainable material is understood and available to a wide range of stakeholders.
PURPOSE: Allow equitable access to bio-engineered, living material and ensure transparency and consent from the public.
DESIGN:
Researchers should provide biosafety training and open educational resources for community labs and schools.
Researchers should establish governed strain-sharing programs for qualified smaller labs.
Researchers should partner with artists, designers and under-resourced communities to explore local applications.
Producers should be required to provide clear labelling and marketing explaining pigment production, genetic engineering and environmental benefits.
ASSUMPTION: I assume there would be engagement from non-traditional users. I assume there would be resources and initatives in place to support community engagement and educational initatives. I assume that some smaller labs would be able to maintain proper biosafety practices to accomodate the use of genetically engineered bacterial cellulose.
RISK OF FAILURE: Risk the misuse of genetically engineered material. Risk that underfunding may limit accessibility to use of the material and its development remains expensive and highly centralised. Miscommunication could cause fear or misinformation and create an aversion to the uptake of the material.
RISK OF SUCCESS: Success expands access to sustainable textiles and gentic engineering but poses complexities with a higher demand for DIY biohacking and public access to genetic engineering.
4. Respect for life
ACTION: Establish and enforce ethical guidelines for scaling SPBC production.
PURPOSE: To recognise bacteria as living contributors and discourage unnecessary cultivation, waste or harmful treatment.
DESIGN:
Regulators should create industry standards for responsible growth volumes (avoiding over-production of Komagataeibacter rhaeticus).
Open forum for discussion between researchers and producers to establish conduct for most humane termination and sterilisation of cultures after use.
ASSUMPTION: I assume stakeholders value ethical treatment of bacteria. I assume that ethics will be prioritised over industrial production. I assume ethical guidelines influence industrial practices.
RISK OF FAILURE: Industrial production may prioritise efficiency over respect for living organisms.
RISK OF SUCCESS: The inherent conflict of the use of other living beings still exists.
4. Score (from 1-3 with, 1 as the best) each of your governance actions against your rubric of policy goals.
Rubric by Isobel Jo Leonard
5. Describe which governance option or combination of options, you would prioritise, and why.
Priorities
From the analysis in my table, the governance goals I would prioritise for the ethical development of self-pigmenting bacterial cellulose would be bio-security, prevnting environmental harm and transparency. This would require a hybrid strategy that triangulates researcher responsibility, producer accountability and high regulatory standards on a local, national and international level.
First, I prioritise actions that embed biosafety at the research stage. Researchers engineering Komagataeibacter rhaeticus should be responsible for ensuring non-pathogenic behaviour, testing containment strategies such as Tyr1-linked kill-switches, evaluating large scale cultivation strategies and end-of-life sterilisation. Designing safety into self-pigmenting bacterial cellulose production early is the most effective way of reducing harm when the materials enter production or circulation.
Second, environmental regulation becomes critical as SPBC scales beyond the lab. Producer-level actions such as life-cycle assessment, biodegradability standards and regulated waste management ensure that genetic modification and increased scale and access do not undermine the sustainability goals of replacing petroleum-based textiles, nor does SPBC’s use or disposal disrupting natural ecosystems.
Finally, these technical controls must be supported by transparency and public engagement. Clear labelling and education initiatives surrounding how genetic engineering operate in SPBC allow designers, consumers, and regulators to make informed decisions. Transparency builds trust and supports adoption, ensuring that living, engineered biomaterials can be widely adopted safely.
Trade offs
The rubric highlights a clear trade-off between biosecurity and equity. The stricter regulatory measures for researchers and producers increase the costs and barriers to working with these materials. While it is important that DIY bio-fabrication spaces and small-scale industries maintain access, this access cannot compromise biosafety regulations or the requirement for ethical research training.
Another inherent trade-off involves respect for life. Efforts to expand the development, accessibility and scalability of this technology raise ethical questions regarding the extractavist use of more than human life in the service of human ends.
Assumptions and uncertainties
I think an assumption underpinning my analysis is that producers and regulators will prioritise sustainability over the efficiency and profit of scaling this highly functional material. Although the fundamental process of producing self-pigmenting bacterial cellulose is sustainable, I am uncertain whether its development for applications in industries such as fashion and design, would create pressures for increasing yield, durability and aesthetic that compromise its sustainability. For this reason, my proposed actions may have over stated the influence and enthusiasm of regulatory bodies to prevent harm.
Florea, M., Hagemann, H., Santosa, G., Abbott, J., Micklem, C.N., Spencer-Milnes, X., de Arroyo Garcia, L., Paschou, D., Lazenbatt, C., Kong, D., Chughtai, H., Jensen, K., Freemont, P.S., Kitney, R., Reeve, B. and Ellis, T. (2016) ‘Engineering control of bacterial cellulose production using a genetic toolkit and a new cellulose-producing strain’, _Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.1522985113
Goosens, V.J., Coussement, P., De Paepe, B., De Maeseneire, S., De Mey, M. and Soetaert, W. (2021) ‘Komagataeibacter tool kit (KTK): a modular cloning system for multigene constructs and programmed protein secretion from cellulose producing bacteria’, ACS Synthetic Biology. https://doi.org/10.1021/acssynbio.1c00365
Hunckler, M.D. and Levine, A.D. (2022) ‘Navigating ethical challenges in the development and translation of biomaterials research’, Frontiers in Bioengineering and Biotechnology. https://doi.org/10.3389/fbioe.2022.899428
Malcı, K., Li, I.S., Kisseroudis, N. and Ellis, T. (2024) ‘Modulating microbial materials: Engineering bacterial cellulose with synthetic biology’, ACS Synthetic Biology. https://doi.org/10.1021/acssynbio.4c00615
Ou, Y. and Guo, S. (2023) ‘Safety risks and ethical governance of biomedical applications of synthetic biology’, Frontiers in Bioengineering and Biotechnology. https://doi.org/10.3389/fbioe.2023.1292029
Quijano, L., Fischer, D., Ferrero-Regis, T. et al. (2025) ‘Exploring bacterial cellulose as an engineered living and programmable biomaterial across disciplines through qualitative thematic analysis’, Scientific Reports. https://doi.org/10.1038/s41598-025-01931-1
Schiros, T.N. et al. (2022) ‘Microbial nanocellulose biotextiles for a circular materials economy’, Environmental Science: Advances. https://doi.org/10.1039/D1VA00044E
Shuster, V. and Fishman, A. (2009) ‘Isolation, cloning and characterization of a tyrosinase with improved activity in organic solvents from Bacillus megaterium’, Journal of Molecular Microbiology and Biotechnology. https://doi.org/10.1159/000243640
Singhania, R.R., Patel, A.K., Tsai, M.L., Chen, C.W. and Dong, C.D. (2021) ‘Genetic modification for enhancing bacterial cellulose production and its applications’, Bioengineered. https://doi.org/10.1080/21655979.2021.1968989
Tkaczyk, A., Mitrowska, K. and Posyniak, A. (2020) ‘Synthetic organic dyes as contaminants of the aquatic environment and their implications for ecosystems’, Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2020.137222
Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?
AI prompts: ChatGPT accessed 2026: “Can you explain Error Correcting Polymerase to me in the stages of extension, extension error and proofreading? “What would the error rate of polymerase be copying the human genome, error rate of polymerase is 1:10⁶”
The Error Rate of polymerase is 1:10⁶. Human genome length is ~3 × 10⁹ bp. Without proofreading, the error rate for copying the human genome would be large (approx. error rate is ~3,000 mistakes per replication.)
Biology deal with this discrepency with Error Correcting Polymerase which works in stages:
Extension: The polymerase adds nucleotides (nucleotide triphosphates) one by one to the growing DNA strand.
Extension error: Occasionally, the polymerase incorporates an incorrect, non-complementary nucleotide and stalls.
Proofreading: The polymerase detects and removes misincorporated nucleotides via 3′→5′ exonuclease activity, drastically reducing errors.
This all allows for accurate replication of the large human genome despite the high error rate of the polymerase.
How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?
Due to codon degeneracy, an average human protein (~345 amino acids) can be coded by an astronomically large number of different DNA sequences.
Even though they code for the same amino acids, many sequences are unusable due to:
Codon bias: different organisms prefer different codons due to tRNA abundance. If an organism uses a rare codons it can slow or stall translation because trna is scarce causing the protien to misfold.
mRNA stability and folding: some DNA nucleotide sequences produce mRNA that folds into secondary structures, which can prevent efficient translation even though the amino acid sequence is correct.
Homework Questions from Dr. LeProust:
What’s the most commonly used method for oligo synthesis currently?
Solid-phase phosphoramidite synthesis.
Why is it difficult to make oligos longer than 200nt via direct synthesis?
AI prompts: ChatGPT accessed 2026: Explain this section to me simply: “A key challenge in DNA synthesis is the generation of >300- nucleotide DNA, which is limited by the elongation cycle efficiency, that is, the efficiency with which each nucleotide is incorporated in the sequence. For example, with the elongation cycle efficiency of 99%, the theoretical yield for an oligonucleotide comprising 120 nucleotides is ~30%(0.99 120 × 100%). However,for a 200 bp polymer/ oligonucleotide,this is reducedtojust 13%. Attempts to over come this issue have focused on improving the accuracy and speed of DNA assembly processes.”
It is difficult to make oligonucleotides longer than 200 nt via direct chemical synthesis because of the elongation cycle efficiency (the efficiency with which each nucleotide is added during synthesis). Even if the per-step efficiency is high (e.g. 99%), the overall yield of full-length oligos drops exponentially with length. For example, a 120 nt oligo would have ~30% full-length product, while a 200-nt oligo drops to ~13%. This cumulative effect of imperfect nucleotide addition makes direct synthesis of long oligos impractical.
Why can’t you make a 2000bp gene via direct oligo synthesis?
AI prompts: “What would be the difference to synthesise a 2000bp gene via direct synthesis?”
Essentially for the same reason. Oligos are synthesised with solid phase phosphormidite one nucleotide at a time. A 2000bp gene is double stranded DNA meaning 1bp is 2 nucleotides, that is 4000 nt. The yield of full-length product declines rapidly as the length exceeds 150-200 nucleotides. 200 nt is already low (~13% full-length at 99% efficiency per step). For a 2000 bp, the theoretical yield of the correct molecule becomes negligibly small, often less than 1%.
Homework Question from George Church:
What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?
By “Lysine Contingency” I assume this is refering to Jurassic Park, where scientists genetically engineer the Dinosaurs DNA so they cannot synthesise lysine. This creates a dependency on the lysine supplements provided by the park staff. Given that lysine is an essential amino acid for metabolising proteins and the dinasaurs would not be able to survive without it, the “Lysine Contingency” is effectively a kill switch, a clever containment strategy which prevents the survival of the dinosaurs if they escaped the island and ensuring they could not harm the outside world or global ecosystems.
Using Benchling, I imported the Lambda DNA and began simulating the Restriction Enzyme Digestion with the following enzymes: EcoRI-HF, HindIII-HF, BamHI-HF, KpnI-HF, EcoRV-HF, SacI-HF, SalI-HF.
Here is the Virtual Digest (left) and Linear Sequence Map (right) I produced in Benchling:
Next, I create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks using this Ronan’s iterating tool. This is preparation for a Gel Electrophoresis lab, a fundamental laboratory technique used to separate and analyze DNA but with the added fun of using the process to make DNA Gel art.
I made this funky design:
Part 3: DNA Design Challenge
Choose your protein:
I have been thinking a lot about Chronobiology. I have an interest in our connection to the temporalities and rhythms of more than human species. Brainstorming for my final project I am thinking about ways that I could use speculative bio-design to explore the patterns of presence and absence of other species and make tangible specific patterns of activity that are less visible, illusive or little known.
So I chose two proteins for the bio-design challenge. Below are the details, structure and DNA sequence from Uniprot
The circadian clock oscillator protein KaiC) in Synechococcus elongatus (cyanobnacteria).
I have chosen the KaiC protein from Synechococcus elongatus PCC 7942 because it is the core component of the cyanobacterial circadian clock (The KaiABC). KaiC undergoes a precise phosphorylation and dephosphorylation cycle that repeats approximately every 24 hours, creating a reliable biological oscillator.
Firefly Luciferase from the North American firefly Photinus pyralis that catalyzes bioluminescence.
It produces the firefly’s characteristic yellow-green light through oxidation of the small molecule luciferin. I also chose to study the Firefly Luciferase sequence from Photinus pyralis because it is a well-characterized bioluminescent enzyme used extensively as a reporter in gene expression studies.
By fusing Luciferase to KaiC, I can convert these molecular oscillations into a measurable bioluminescent signal. This fusion allows real-time monitoring of the circadian rhythm, with peaks in light output corresponding to high KaiC activity and troughs corresponding to low activity. I wanted to explore both proteins today because together KaiC provides the biological timing mechanism and Luciferase provides a visible readout, making them an ideal pair for a circadian biological clock.
Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.
The central dogma of molecular biology, proposed by Francis Crick, describes the flow of genetic information: DNA is transcribed into RNA, which is translated into protein (DNA>RNA>PROTEINS).
In Transcription DNA sequences are converted to RNA. This occurs in the nucleus and is mediated by RNA polymerase.
In Translation messenger RNA is decoded to synthesize a specific polypeptide chain and occurs in the cytoplasm where ribosomes read mRNA instructions to make proteins.
The Central Dogma gives us the framework to work backwards from a given protein sequence and infer the DNA sequence that the protein is derived from.
Below are the nucleotide sequences (reverse translation) for KaiC (above) and Luciferase (below). I used the online tool Sequence Manipulation Tool from Bioinformatics by entering the raw amino acid sequence for each protein.
Codon optimization: Describe why you need to optimize codon usage?
Codon optimisation is a process used to make translation more efficient and improve gene expression.
Different organisms have codon usage bias, meaning they prefer to use certain codons to specify each amino acid based on their abundance of matching tRNAs.
Codon optimisation replaces codons rare to the host organism with codons it prefers, allowing the ribosome to translate the mRNA more efficiently. This is especially important when expressing a gene from one organism in a different host, because unoptimised codons can slow translation, reduce protein yield, or cause errors in protein production.
Codon optimization: _Which organism have you chosen to optimize the codon sequence for and why?
It is a standard laboratory organism that is widely available, cheap, has a well understood codon usage bias and established protocols for isolating plasmids and cloning.
Its fast growing with high protein expression efficiency.
Specifically, for a possible project where I would like to fuse Luciferase to KaiC to act as a bioluminescent reporter of circadian rhythms, codon optimisation would be important. Codon optimisation of both KaiC (from cyanobacteria) and firefly Luciferase (from eukaryotes) ensures that E. coli can efficiently translate the fusion protein using its tRNA pool, allowing high-level expression suitable for detecting and reporting circadian rhythms. The KaiC portion reliably maintains its role in the circadian oscillator, while the Luciferase portion generates strong bioluminescence that creates a good visualisation of the circadian rhythmns.
Codon optimised nucleotide sequence for KaiC.
KaiC comes from Synechococcus a prokaryotic cyanobacterium, but its codon usage bias is different from E. coli. Optimising KaiC for E. coli ensures efficient translation and more abundant protein production. Firefly Luciferase is eukaryotic meaning its codons are rare in bacteria, so without optimisation translation could be inefficient. Optimising each gene independently for E. coli ensures that translation of one protein does not impact the other. Optimising both genes for the same host ensures coordinated and efficient expression within a single engineered system, allowing it to accurately report the dynamics of the circadian clock.
Codon optimised nucleotide sequence for Firefly Luciferase.
Cell-dependent production of Luciferase in _E. coli
LIGATION:
Plasmids are a small circular piece of DNA used that replicate independantly fromthe hosts chromosomal DNA. They are used to intriduce foreign DNA into another cell. To produce the protein Luciferase by cell dependant production in Ecoli, first the plasmid (designed for expression in E.coli) and the Luciferase gene are cut using restriction enzymes to make compatible ends. The gene is then inserted into the the plasmid through ligation. This is where DNA ligase enzyme binds the compatible ends of plasmid and Luciferase gene. The plasmid contains a promoter to drive transcription in the E.coli and an antibiotic resistance gene for selection.
TRANSFORMATION:
This plasmid is then introduced into E. coli through transformation, where heat shock encourages some bacteria to take up the plasmid. The shock makes the bacterial membrane more permeable to the plasmids.
SELECTION:
The bacteria are grown on an antibiotic plate. The plasmids contain a antibiotic gene so only bacteria that successfully took up the plasmid survive and form bacterial cultures.
SCREENING
Colonies are screened using PCR, restriction digest or sequencing to identify a plasmid with the Luciferase gene in the correct direction relative to the promoter and to ensure the plasmid hasn’t closed back up without taking in the gene.
PROTEIN PRODUCTION
Once a colony with the correct plasmid is identified, it is grown in bulk, and protein expression is induced by adding a chemical signal. Inside the bacteria, the codon-optimised Luciferase DNA is transcribed into mRNA and then translated by bacterial ribosomes into luciferase protein. The codon optimisation ensuring that translation is efficient, even though Luciferase is originally a eukaryotic protein.The bacteria can then be lysed (split open) to release the protein.
PURIFICATION
Finally, the Luciferase protein can either be used directly in bacterial cells or purified. Purification techniques include affinity chromatography, where antibodies specific to luciferase bind and isolate the protein.
Part 4: Prepare a Twist DNA Synthesis Order
Finally, I’ve done a test order from Twist inserting Bioscience the codon optimised sequence for Luciferase.
(i) What DNA would you want to sequence (e.g., read) and why? This could be DNA related to human health (e.g. genes related to disease research), environmental monitoring (e.g., sewage waste water, biodiversity analysis), and beyond (e.g. DNA data storage, biobank).**
Sequencing ELF3 to track impact of climate changing on circadianflowering in plants.
I would sequence the ELF3 (EARLY FLOWERING 3) gene, which is a key player in the circadian clock of plants such as Arabidopsis thaliana’s. It’s function includes the regulation of flowering time in response to light and temperature variations, therefore mutations within the ELF3 DNA can lead to the loss of photoperiod sensitivity and shifts in seasonal flowering.
Zhu X, Wang H. Revisiting the role and mechanism of ELF3 in circadian clock modulation. Gene. 2024 Jun 30;913:148378. doi: 10.1016/j.gene.2024.148378. Epub 2024 Mar 13. PMID: 38490512.
Sequencing ELF3 allows us to identify genetic variations or mutations such as single nucleotide polymorphisms (SNPs) or insertions/deletions that could shift flowering schedules, which is particularly important for studying how climate change may disrupt plant phenology. Changes in flowering time can misalign plant/pollinator interactions, affecting ecosystems and crops so it is a crucial thing to understand.
ELF3 would be interesting to sequence because its variations make invisible molecular changes visible and can show illusive shifts in the rhythms of plant cycles. Through comparative sequencing of ELF3 across different plant populations, climates or even historical samples, we could track shifts in flowering schedules in different conditions and relate gene variations to observable plant traits that respond to climate (e.g., flowering date). This would be interesting to infer how plants are responding to environmental pressures or predicting how future climate conditions may effect flowering cycles.
(ii) In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why?
Illumina Sequencing. Next Generation Sequencing (2nd gen).
It has a similar principle to Sanger sequencing which was historically used in combination with positional cloning to first isolate the ELF3 gene. Both methods use fluorescently-labelled nucleotides and identifies each nucleotide by its fluorescent tag. However, while Sanger sequencing handles one DNA fragment at a time, Illumina allows millions of fragments to be sequenced simultaneously in a single run. For my project, I would use Illumina sequencing to read ELF3 DNA because:
High accuracy for detecting variants, single nucleotide polymorphisms (SNPs) and small insertions/deletions in ELF3 (approx 99.9% short reads).
Widely used in circadian biology studies and with plant DNA samples
High throughput which is suitable for single-gene sequencing across many plant samples.
Illumina sequencing works on the principle of sequencing by synthesis (SBS). It identifies DNA bases as they are added to a DNA strand. Each of the four DNA bases is labelled with a unique fluorescent dye, allowing the sequencing system to detect which nucleotide has been added during each cycle. The system captures images of these signals which are then used to determine the exact sequence of the DNA fragment.
Essential Steps:
DNA extraction: The input is genomic DNA isolated from plant tissue in the leaf or seeds. It is important the nucleic acids are of high quality so there will be a quality control check in preparation.
Fragmentation: The DNA is broken down into smaller pieces suitable for Illumina sequencing (typically 200–500 bp) using processes such as mechanical shearing or enzyme digestion.
Adapter ligation: Sequencing adapters are attached to both ends of each fragment to ensure they bind to the sequencing flow cell. They can also be used for barcoding multiple samples.
Library amplification: Bridge PCR is used to bend each DNA strand to form a bridge over a chip. This amplifies the bridge creating clusters at each spot and generates a strong, clear signal.
5. Sequencing by Synthesis: Fluorescently labeled nucleotides are added one by one to the growing DNA strand. Each nucleotide emits a fluorescence as it attaches, specifying the base (base calling) and allowing the sequence to be determined over cycles of synthesis.
6. Analysis: Images collected from each cycle are converted into base sequences by analysing fluorescent signals. The sequences are compared to a reference genome of ELF3 to identify variants.
By sequencing ELF3 in this way, we can generate a detailed map of how plants’ circadian clock genes vary with environmental conditions and over time, providing insights into the elusive rhythms of plants.
https://microbenotes.com/illumina-sequencing/
5.2 DNA Write – Fibroin for Bio-printing
(i) What DNA would you want to synthesize (e.g., write) and why?
Image left: Fibrinogen-Based Bioink for Application in Skin Equivalent 3D Bioprinting. Image right: silkworm
The DNA I would like to synthesise is the fibroin gene (FibH) from silkworms (Bombyx mori) codon optimised for an Ecoli host. This gene encodes the structural protein fibroin, the main component of silk.
Fibroin has amazing mechanical properties that would be interesting for experimentation for biomaterials and bio-printing to create transparent, complex or bioactive structures. Fibroin is non-toxic and biodegradable, however, unlike its partnering gene Sericin it cannot be extracted from waste product of the silk production industry. By synthesising fibroin DNA, I could produce recombinant fibroin in an Ecoli host for sustainable and cruelty free bio-fabrication.
Some of the qualities of Fibroin that make it interesting are:
High mechanical strength: β-sheet crystalline regions give stiffness and shape retention
Transparency: can form clear films and scaffolds
Compatible with composites: can be mixed with water, polymers, hydrogels, nonparticles.
Biocompatible: widely used in tissue engineering, non toxic, biodegradable
Tuneable properties: could be engineered for responsiveness or added functionality. Recombinant production allows precise control over sequence, length etc. enabling tuneable transparency, stiffness, and degradation rates in printed structures.
Scarlett Yang Sericin Bioplastics
(ii) What technology or technologies would you use to perform this DNA synthesis and why?
I would use oligonucleotide synthesis via Twist Bioscience. This is because unlike past methods of DNA synthesis Twist writes DNA:
Has very high throughput and extremely accurate oligo synthesis. Twist uses silicon-based DNA synthesis which allows them to make 1 million oligos per chip and up to 9,600 genes. This is perfect for assembling long or repetitive genes like FibH.
Its high fidelity reduces errors in the gene sequence to an error rate of 1:5000 bases which allows for longer sequences to be synthesised. FibH is a very long and repetitive gene which is tricky to synthesise accurately. They also handle codon optimization and cloning into plasmids for insertion into ecoli host, saving me a lot of time.
It is a process which requires them to synthesis very little reagent meaning the process is cheap and sustainable.
Oligionucleotide Synthesis slide Emily Le Proust
Essential Steps
Design the sequence in Benchling and codon-optimize fibroin DNA for Ecoli’s codon usage bias.
Oligonucleotide synthesis: Twist synthesises hundreds of short DNA fragments (oligos) that come together to form the entire gene sequence.
Deprotection: The protecting group on the 5′ end is removed from the support-bound nucleoside leaving a reactive 5′-OH group.
Coupling: This couples to a nucleotide base to form a phosphite triester bond.. This needs to be a very efficient reaction and that is extremely complete.
Capping: Unreacted 5′-OH groups are capped to ensures that only one base can be added at a time and any unreacted sites are chemically blocked to prevent them causing errors in future cycles. The phosphide is then oxidised into a P5 phosphate creating the natural bone of DNA.
Oxidisation: The unstable phosphite triester bond is oxidised to form a stable phosphorus phosphodiester bond creating the backbone of DNA.
Deblocking: finally they deblock the 5" end to regenerate an O-H for the next cycle.
Post-Synthesis Processing: Twist will undergo cleavage, where the completed oligonucleotide is detached from the solid support, deprotection, where the protecting groups on the base residues are removed and purification of the final product and quality control from mass spectrometry.
Limitations of this method:
While Twist can produce many constructs in parallel, very long or highly repetitive sequences are more challenging to synthesize and may need extra optimisation.
5.3 DNA Edit
(i) What DNA would you want to edit and why?
Studio Lionne van Deursen exhibits material created by microorganisms at Milan Design Week
I am very interested in bio and bio-hybrid soft robotics, bio-actuated movement and responsive structures. I am intrigued by the possible applications of genetic editing to design self-actuating and responsive materials and systems.
As an entry point to this idea I have been researching self-folding biomaterials.
So I would like to edit cellulose-producing bacteria Komagataeibacter to create self-folding, pleated bacterial cellulose by genetically programming differences in water retention. Specifically, I would insert the BslA gene from Bacillus subtilis along with a secretion tag, so that the bacteria secrete BslA protein that binds to cellulose. This protien increases the materials hydrophobicity meaning it will retain less water and shrinks less and becomes more rigid when dry. Unengineered bacterial cellulose is highly hydrophilic and shrinks significantly during drying and is more pliable. By layering the two types of cellulose you can create a bilayer structure with a differential shrinkage and controlled folding. By cutting and assembling layers strategically, pleats or origami folds can be programmed. This creates a fully bio-fabricated, biodegradable actuator.
Renewable Self-Folding Origami Constructed from Bioengineered Bacterial Cellulose, Yitong Tseo, Morgan Guempel, Cathy Hogan and Ian Hunter
I think this has interesting applications for design, fashion and sculpture to create a programmable, structured material that is fully degradable. Lots of research has also been done for use in soft robotics for biomedical application. It shows the broader concept of DNA editing to change the genetic instructions of an organism to engineer functional and sustainable materials.
(ii) What technology or technologies would you use to perform these DNA edits and why?
Plasmid-based gene expression
I would use a plasmid-based system to introduce the BslA gene into Komagataeibacter. This method allows controlled expression of a non-native protein. Plasmids are circular DNA molecules that can carry a gene of interest along with regulatory elements, such as promoters, signal peptides and terminators all required for this project. They will replicate independently inside the the bacterial cellulose, providing multiple copies of the gene for strong expression. This method allows precise control over when and how much BslA is expressed without permanently altering the bacterial genome. It is easier to implement and is ideal for experiments because it allows rapid testing. CRISPR is probably overkill but for long-term, wide-scale production integrating the gene into the genome via CRISPR might be more stable.
The Essential Steps:
Prepare DNA sequence: In Benchling assemble the DNA sequence and design regulatory elements e.g a promotor, a signal peptide to direct secretion of BslA outside the cell and a terminator. Previous studies have used Promoter (PLux) that turns on BslA expression in response to chemical signaling, Signal peptide (N22 tag) which directs the BslA-CBM fusion protein to the CsgG gene for secretion in to the cellulose and the LuxR gene
Copy the DNA (PCR): using PCR take a small amount of the BslA gene template, add primers and run it through a thermal cycler to produce billions of copies.
Assemble the plasmid: Use Golden Gate cloning, which employs restriction enzymes to create cuts at precise sites and create unique overhangs (sticky ends) and ligase complimetry sticky ends together to assemble the BslA gene into a plasmid backbone in one reaction with accuracy.
Transformation: Shock the cells via chemical treatment or electroporation to make them competent and deliver the plasmid. Antibiotic resistance genes on the plasmid allow selection of bacteria that have successfully taken up the plasmid.
Expression and secretion: Grow the engineered bacteria in nutrient media, as cellulose is produced, BslA is secreted and binds to the cellulose surface.
The Limitations
The limitations of this process include inconsistency of BsIA production across the bacterial cellulose population as plasmids replicate independently and the number of plasmids per cell can vary. Stability is also an issue as the gene is not permenently integrated into the genome so the plasmids could be lost, for long term or industrial-scale production a genome-integrated systems (e.g. CRISPR knock-in), which maintain stable expression over generations would be better.
Covington, M. F., Panda, S., Liu, X. L., Strayer, C. A., Wagner, D. R., & Kay, S. A. (2001). ELF3 modulates resetting of the circadian clock in Arabidopsis. Plant Cell, 13(6), 1305–1315. https://doi.org/10.1105/tpc.13.6.1305
Zahn, T., Zhu, Z., Ritoff, N., Krapf, J., Junker, A., Altmann, T., Schmutzer, T., Tüting, C., Kastritis, P. L., Babben, S., Quint, M., Pillen, K., & Maurer, A. (2023). Novel exotic alleles of EARLY FLOWERING 3 determine plant development in barley. Journal of Experimental Botany, 74(12), 3630–3650. https://doi.org/10.1093/jxb/erad127
Richard, L. J., Giordano, V. R., Leite, V. B. P., Wigge, P. A., & Hanson, S. M. (2024). Molecular dynamics simulations illuminate the role of sequence context in the ELF3-PrD-based temperature sensing mechanism in plants. eLife, 13, RP102410. https://doi.org/10.7554/eLife.102410.1
Millar, A. J., Straume, M., Chory, J., Chua, N. H., & Kay, S. A. (1995). Circadian clock mutants in Arabidopsis identified by luciferase imaging. Science, 267, 1161–1163. https://doi.org/10.1126/science.7855595
Hicks, K. A., Albertson, T. M., & Wagner, D. R. (2001). EARLY FLOWERING3 encodes a novel protein that regulates circadian clock function and flowering in Arabidopsis. Plant Cell, 13(6), 1281–1292. https://doi.org/10.1105/tpc.13.6.1281
Lee, J., Park, S., Lee, S., Kweon, H. Y., Jo, Y. Y., Kim, J., Chung, J. H., & Seonwoo, H. (2023). Development of silk fibroin-based non-crosslinking thermosensitive bioinks for 3D bioprinting. Polymers (Basel), 15(17), 3567. https://doi.org/10.3390/polym15173567
Trucco, D., Sharma, A., Manferdini, C., Gabusi, E., Petretta, M., Desando, G., Ricotti, L., Chakraborty, J., Ghosh, S., & Lisignoli, G. (2021). Modeling and fabrication of silk fibroin-gelatin-based constructs using extrusion-based three-dimensional bioprinting. ACS Biomaterials Science & Engineering, 7(7), 3306–3320. https://doi.org/10.1021/acsbiomaterials.1c00410
Tseo, Y., Guempel, M., Hogan, C., & Hunter, I. (2025). Renewable self-folding origami constructed from bioengineered bacterial cellulose. bioRxiv. https://doi.org/10.1101/2025.0.0
Homework Part 1: Python Script for Opentrons Artwork
This week we are creating a Python file to run on an Opentrons OT-2 liquid handling robot to create flourescent designs. This is achieved by depositing E.coli genetically engineered to express different fluorescent proteins onto black agar plates. The flouresence will be visible under UV light and so will our designs!
I have no experience programming or coding so this was quite a daunting task and I ended up needing to use Google Gemini to assist. I gave it some of the example code e.g the mathematical heart example and DNA helix as well as the “important notes” highlighted in the Opentrons Collab. Then I gave it the co-ordinates generated in the Automation Art Interface and asked it to assigned each group co-ordinate to Red, green and orange. I then gave it “Your code” template and asked it to help complete it with my co-ordinates.
from opentrons import types
metadata = { # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata
'author': '',
'protocolName': '',
'description': '',
'source': 'HTGAA 2026 Opentrons Lab',
'apiLevel': '2.20'
}
##############################################################################
### Robot deck setup constants - don't change these
##############################################################################
TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'
well_colors = {
'A1' : 'Red',
'B1' : 'Green',
'C1' : 'Orange'
}
def run(protocol):
##############################################################################
### Load labware, modules and pipettes
##############################################################################
# Tips
tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')
# Pipettes
pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])
# Modules
temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)
# Temperature Module Plate
temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul',
'Cold Plate')
# Choose where to take the colors from
color_plate = temperature_plate
# Agar Plate
agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate') ## TA MUST CALIBRATE EACH PLATE!
# Get the top-center of the plate, make sure the plate was calibrated before running this
center_location = agar_plate['A1'].top()
pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)
##############################################################################
### Patterning
##############################################################################
###
### Helper functions for this lab
###
# pass this e.g. 'Red' and get back a Location which can be passed to aspirate()
def location_of_color(color_string):
for well,color in well_colors.items():
if color.lower() == color_string.lower():
return color_plate[well]
raise ValueError(f"No well found with color {color_string}")
# For this lab, instead of calling pipette.dispense(1, loc) use this: dispense_and_detach(pipette, 1, loc)
def dispense_and_detach(pipette, volume, location):
"""
Move laterally 5mm above the plate (to avoid smearing a drop); then drop down to the plate,
dispense, move back up 5mm to detach drop, and stay high to be ready for next lateral move.
5mm because a 4uL drop is 2mm diameter; and a 2deg tilt in the agar pour is >3mm difference across a plate.
"""
assert(isinstance(volume, (int, float)))
above_location = location.move(types.Point(z=location.point.z + 5)) # 5mm above
pipette.move_to(above_location) # Go to 5mm above the dispensing location
pipette.dispense(volume, location) # Go straight downwards and dispense
pipette.move_to(above_location) # Go straight up to detach drop and stay high
###
### YOUR CODE HERE to create your design
###
# Coordinate sets for Hello Kitty outline
point_sets = [
('Red', [(3.3, 23.1),(5.5, 23.1),(18.7, 23.1),(-18.7, 20.9),(7.7, 20.9),(18.7, 20.9),(-20.9, 18.7),(-14.3, 18.7),(-12.1, 18.7),(-5.5, 18.7),(-3.3, 18.7),(-1.1, 18.7),(20.9, 18.7),(-1.1, 16.5),(7.7, 16.5),(20.9, 16.5),(-1.1, 14.3),(20.9, 14.3),(-1.1, 12.1),(7.7, 12.1),(14.3, 12.1),(23.1, 12.1),(-20.9, 9.9),(14.3, 9.9),(16.5, 9.9),(23.1, 9.9),(23.1, 7.7),(-23.1, 5.5),(14.3, 5.5),(16.5, 5.5),(18.7, 5.5),(20.9, 5.5),(23.1, 5.5),(-23.1, 3.3),(23.1, 3.3),(-23.1, 1.1),(25.3, 1.1),(-25.3, -1.1),(-23.1, -1.1),(20.9, -1.1),(23.1, -1.1),(25.3, -1.1),(27.5, -1.1),(-27.5, -3.3),(-25.3, -3.3),(-23.1, -3.3),(25.3, -3.3),(-23.1, -5.5),(-12.1, -5.5),(12.1, -5.5),(23.1, -5.5),(-23.1, -7.7),(-20.9, -7.7),(-12.1, -7.7),(-1.1, -7.7),(1.1, -7.7),(3.3, -7.7),(12.1, -7.7),(20.9, -7.7),(23.1, -7.7),(25.3, -7.7),(27.5, -7.7),(-23.1, -9.9),(3.3, -9.9),(23.1, -9.9),(-20.9, -12.1),(-18.7, -12.1),(20.9, -12.1),(25.3, -12.1),(-16.5, -14.3),(18.7, -14.3),(-14.3, -16.5),(-12.1, -16.5),(12.1, -16.5),(14.3, -16.5),(-9.9, -18.7),(-7.7, -18.7),(-5.5, -18.7),(-3.3, -18.7),(-1.1, -18.7),(1.1, -18.7),(3.3, -18.7),(5.5, -18.7),(7.7, -18.7),(9.9, -18.7)]),
('Green', [(7.7, 23.1),(-20.9, 20.9),(-14.3, 20.9),(9.9, 20.9),(-23.1, 18.7),(-23.1, 16.5),(-23.1, 14.3),(14.3, 14.3),(-23.1, 12.1),(-23.1, 7.7),(18.7, 3.3),(-25.3, 1.1),(-29.7, -3.3),(12.1, -3.3),(23.1, -3.3),(-25.3, -14.3),(-23.1, -14.3),(-14.3, -14.3)]),
('Orange', [(16.5, 23.1),(-16.5, 20.9),(1.1, 20.9),(12.1, 20.9),(14.3, 20.9),(16.5, 20.9),(20.9, 20.9),(-7.7, 18.7),(1.1, 18.7),(9.9, 18.7),(5.5, 16.5),(5.5, 14.3),(9.9, 14.3),(12.1, 14.3),(16.5, 14.3),(18.7, 14.3),(23.1, 14.3),(5.5, 12.1),(1.1, 9.9),(3.3, 9.9),(5.5, 9.9),(7.7, 9.9),(9.9, 9.9),(12.1, 7.7),(23.1, 1.1),(29.7, -1.1),(-20.9, -3.3),(-12.1, -3.3),(25.3, -5.5),(-25.3, -9.9),(-20.9, -9.9),(16.5, -14.3)]),
('Red', [(-1.1, -9.9),(1.1, -9.9)])
]
import numpy as np
for color, points in point_sets:
pts = np.array(points)
pts[:,0] -= np.mean(pts[:,0])
pts[:,1] -= np.mean(pts[:,1])
radii = np.sqrt(pts[:,0]**2 + pts[:,1]**2)
scale = 40 / np.max(radii)
pts *= scale
pipette_20ul.pick_up_tip()
cell_well = location_of_color(color)
for i, (x, y) in enumerate(pts):
if i % 20 == 0:
pipette_20ul.aspirate(min(20, len(pts)-i), cell_well)
adjusted_location = center_location.move(types.Point(x, y))
dispense_and_detach(pipette_20ul, 1, adjusted_location)
pipette_20ul.drop_tip()
# Don't forget to end with a drop_tip()
Kitty went quite wonky and I battled for quite some time with Gemini, but I think it is something wrong with the scaling that I didn't understand. I kept getting issues with my indentation, so I decided to pivot and try a simpler design.
Again I used Google Gemini in the same way feeding it the example codes and then the new co-ordinates catagorised into each colour- red, green and orange. I initially had an issue that the Opentrons library wasn’t available so I added the top line on the code from troubleshooting with Gemini and it worked.
# 1. Install the library (Required for Google Colab)
!pip install opentrons
from opentrons import types
metadata = {
'author': 'Your Name',
'protocolName': 'Microbial Art Assignment',
'description': 'Simple design with Red, Green, and Orange dots',
'source': 'HTGAA 2026 Opentrons Lab',
'apiLevel': '2.20'
}
##############################################################################
### Robot deck setup constants
##############################################################################
TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'
well_colors = {
'A1' : 'Red',
'B1' : 'Green',
'C1' : 'Orange'
}
def run(protocol):
# Load labware
tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')
pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])
temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)
temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul', 'Cold Plate')
color_plate = temperature_plate
agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')
center_location = agar_plate['A1'].top()
pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)
# Helper functions
def location_of_color(color_string):
for well, color in well_colors.items():
if color.lower() == color_string.lower():
return color_plate[well]
raise ValueError(f"No well found with color {color_string}")
def dispense_and_detach(pipette, volume, location):
above_location = location.move(types.Point(z=5))
pipette.move_to(above_location)
pipette.dispense(volume, location)
pipette.move_to(above_location)
### YOUR DESIGN DATA ###
red_points = [(-23.1, 14.3),(-20.9, 14.3),(-18.7, 14.3),(-16.5, 14.3),(-14.3, 14.3),(14.3, 14.3),(16.5, 14.3),(18.7, 14.3),(20.9, 14.3),(23.1, 14.3)]
green_points = [(-18.7, 7.7),(18.7, 7.7),(-20.9, 5.5),(-18.7, 5.5),(-16.5, 5.5),(16.5, 5.5),(18.7, 5.5),(20.9, 5.5),(-18.7, 3.3),(18.7, 3.3)]
orange_points = [(-1.1, -14.3),(3.3, -14.3),(-3.3, -16.5),(-1.1, -16.5),(1.1, -16.5),(3.3, -16.5),(5.5, -16.5),(-5.5, -18.7),(-3.3, -18.7),(-1.1, -18.7),(1.1, -18.7),(3.3, -18.7),(5.5, -18.7),(7.7, -18.7),(-3.3, -20.9),(-1.1, -20.9),(1.1, -20.9),(3.3, -20.9),(5.5, -20.9),(1.1, -23.1)]
# Drawing Red
if red_points:
pipette_20ul.pick_up_tip()
for i in range(0, len(red_points), 20):
batch = red_points[i:i+20]
pipette_20ul.aspirate(len(batch), location_of_color('Red'))
for x, y in batch:
target = center_location.move(types.Point(x=x, y=y))
dispense_and_detach(pipette_20ul, 1, target)
pipette_20ul.drop_tip()
# Drawing Green
if green_points:
pipette_20ul.pick_up_tip()
for i in range(0, len(green_points), 20):
batch = green_points[i:i+20]
pipette_20ul.aspirate(len(batch), location_of_color('Green'))
for x, y in batch:
target = center_location.move(types.Point(x=x, y=y))
dispense_and_detach(pipette_20ul, 1, target)
pipette_20ul.drop_tip()
# Drawing Orange
if orange_points:
pipette_20ul.pick_up_tip()
for i in range(0, len(orange_points), 20):
batch = orange_points[i:i+20]
pipette_20ul.aspirate(len(batch), location_of_color('Orange'))
for x, y in batch:
target = center_location.move(types.Point(x=x, y=y))
dispense_and_detach(pipette_20ul, 1, target)
pipette_20ul.drop_tip()
This worked well and although not as cute as the kitty, I am happy with the design.
Self-dyeing textiles grown from cellulose-producing bacteria with engineered tyrosinase expression
Kenneth T. Walker, Jennifer Keane, Vivianne J. Goosens, Wenzhe Song, Koon-Yang Lee, View ORCID ProfileTom Ellis
doi: https://doi.org/10.1101/2023.02.28.530172
In this study, they engineered bacteria Komagataeibacter rhaeticus to produce melanin (a black pigment) in bacterial cellulose, creating a sustainable, self-dyeing textile for the fashion industry.
To measure and compare how much melanin the bacteria make under controlled conditions, they conducted a eumelanin production assay and used an Opentrons OT-2 liquid handling robot to prepare 384-well reaction plates
The robot handled transferring precise amounts of development buffer into the reaction plates using an 8-channel 300 μL OT-2 Gen2 pipette. The reaction plates were kept cold at 4°C using the OT-2 Thermo-module to slow eumelanin production during preparation.
Bacterial cells were mixed in one round of aspiration using the OT-2 Gen2 pipette, then a defined volume was transferred into each well of the 384-well plate.
After centrifugation, the Opentrons Absorbance Plate Reader Module was heated to 45°C to accelerate eumelanin production and prevent potential cell growth from affecting optical density readings. Optical density measurements were then taken over time to assess melanin production.
This setup allowed them to run hundreds of controlled reactions simultaneously and automatically measure pigment formation over time. This is novel because they are using automation to perform an experiment at this scale, which would be very difficult manually. This allows them to generate a large, consistent dataset for analysis.
Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details.
Light-Controlled textile patterning with bacterial pigment production.
One possible direction for my final project is to use light to control where pigment-producing bacteria grow on textiles and other objects**, essentially creating a form of “bacterial exposure printing.” I want to explore whether projecting patterns of colored light onto a surface can direct the expression of genes that help bacteria stick and form biofilms in specific areas. This idea is inspired by research showing that Escherichia coli can be patterned onto materials by controlling curli fiber production with light, which anchors the cells in place. If successful, this system could be further engineered so that the bacteria produce pigment only where they are induced to grow**, offering a controlled, reproducible, and sustainable method for creating patterns on fabric.
Automation could be useful in this research project as a way of ensuring bacterial preparation and placement is consistent and reproducible.
Moser, Felix, Tham, Eléonore, González, Lina M., Lu, Timothy K.
and Voigt, Christopher A. 2019. "Light Controlled, High Resolution
Patterning of Living Engineered Bacteria Onto Textiles, Ceramics,
and Plastic." Advanced Functional Materials, 29 (30).
Automated Preparation
First, the Opentrons OT-2 could be used for automating the culture preparation before pigment patterning begins. The robot could dispense sterile growth media into wells, inoculate bacteria from starter cultures and measure identical volumes to ensure consistency.
It can also perform precise dilutions so each sample has the same concentration of bacteria.. This automation reduces variables between pigment producing bacteria, so that it is possible to determine if pigment production differences are due to light patterning rather than inconsistent culture preparation. This being automated also saves time preparing lots of bacteria.
3D Printed Holders:
Textile is not a standard lab plates, so it would be necessary to design custom 3D-printed holders that fit onto the Opentron deck slots and clamp the fabric preventing it from any movement or folding that would effect the placement of the bacteria. This holder would allow the robot to treat non-standard materials like regular labware and create automated precision over an organic process.
Similarly, a custom holder for the light source could ensure that the projected patterns are always at a fixed distance and angle from the surface, providing uniform exposure for precise control over where the bacteria adheres. The exposure, duration or intensity of the light could also be programmed or automated by arduino script and sequenced with the Opentrons protocol.
Precisely Depositing Bacterial
Using the Opentrons robot, I could dispense precise amounts of pigment producing bacterial culture onto specific co-ordinates on the textile. This would allows for a controlled a base pattern. This means that there would be consistency across trials to see whether the bacteria responds to growth in the light induced areas so we could reliably compare samples.
Automated Washing
The Opentron can be used for a standardised washing cycle to gently remove excess bacteria from the textile. This would ensure an identical timescales of bacterial growth and light exposure then the same washing force to be able to reliably compare results and reproduce patterns.
Documentation
The Opentron can also be used to capture standardised images under the controlled lighting and see results of pigment production. It can also be used to analyse the images and compare pigment intensity.
References:
Moser, F., Tham, E., González, L. M., Lu, T. K., & Voigt, C. A. (2019). Light-controlled, high-resolution patterning of living engineered bacteria onto textiles, ceramics, and plastic. Advanced Functional Materials, 29(27), 201901788. https://doi.org/10.1002/adfm.201901788
Walker, K. T., Keane, J., Goosens, V. J., Song, W., Lee, K.-Y., & Ellis, T. (2023). Self‑dyeing textiles grown from cellulose‑producing bacteria with engineered tyrosinase expression [Preprint]. bioRxiv. https://doi.org/10.1101/2023.02.28.530172
