Week 1 HW: Principles and Practices

The morphogenetic process in the Drosophila dorsal thorax is explored above: Confocal Image of the dorsal thorax epithelium (left), tracking of cell division (middle) and expression patterns predicted by spatial transcriptomics (right).

First, describe a biological engineering application or tool you want to develop and why.

I’m fascinated by morphogenesis, in which the complex, three-dimensional organisation of biological entities can emerge! The possibilities of synthetically harbouring this process feel endless, however I am most intrigued by the potential of its application in radically-sustainable manufacturing.

Conventional manufacturing techniques (i.e formative plastic methods, subtractive techniques) in their archetypal incarnation contain inherent inefficacies at all stages, from the pre-production through to disposal of the manufactured artefact by the end-user. The plastic spork is an exemplar for how contemporary industrial logic constrains a product from its inception. Compatibility with standard machinery immediately informs material selection. Injection moulding techniques demand a particular design rationale, where minor features like undercuts can cause costs to balloon through added mould complexity. Instead of being repossessed wholly by organic processes at the atomic level when disposed, conventional thermoplastics degrade and colonize such exotic destinations as the mariana trench, our water supply, the human brain etc.

Organisms in the natural world appear unshackled by these constraints. I think immediately of the functionally-graded characteristics of bone, in which matter distribution is varied spatially, optimised to provide lightweight strength and support.

Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm).

• Consistency of use: Fair dissemination of information is necessary to permit strong, equitable discussions between distinct stakeholders (public, policymakers, technicians etc), ensuring the ethical concerns of all groups are platformed to guide accurate policymaking.

• Transparency in research and application: ‘Growth’ processes should likely be strongly standardized (at least initially) to establish a safe, repeatable precedent of procedure.

• Prevention of environmental contamination: Current biowaste disposal processes should be adapted to fit novel requirements as they emerge.

• Prevention of utilisation in scenarios considered universally unethical: This one feels pretty clear: no application should cause innate suffering or distress to involved biological materials (or societies in which they are used).

  • a. Sound disposal practices of biomass bioproducts as industrial waste: Current biowaste disposal processes should be adapted to fit novel requirements as they emerge.

Next, describe at least three different potential governance “actions”.

1. Implement sound genetic safeguards.

Purpose: emergency termination of cellular processes in the event of dangerous circumstances, whereby the health of a population or ecosystem is suddenly threatened.

Induced auxotrophy (metabolic or via xenonucleic biochemistry), toxin gene expression cassettes, and engineered addiction are all contemporary methods of halting cellular activity outside of a specific environment (lab). A mosaic of approaches is recommended as the most robust containment method, especially when accounting for the spontaneous occurrence of safeguard-negating mutations.

Design: continued research, testing, and update of existing safeguards.
Assumptions: established safeguard techniques identified are inadequate or incompatible for this use case.

Risk of failure (or success!):

a. Strategies prove ineffective and difficult to consistently implement. Environmental contamination occurs frequently; consequences are unpredictable and catastrophic.

b. Genetic safeguards utilised are too complex or specific to permit equitable access.

2. Compose a universal convention outlining acceptable and unacceptable applications in manufacturing (and beyond), requiring strict agreement and adherence at a national level prior to industrial use.

Purpose: establish legal basis for international enforcement of manufacturing practices collectively identified as ethical and sound. Dissuade bad actors from potential misuse, encourage international collaboration, and mandate stewardship at the national scale. Unlike traditional manufacturing where inanimate materials are used, it feels like a necessity to require international collaboration where unethical practices could give rise to unpredictable, dangerous outcomes.

Design: The Biological Weapons Convention (BWC) serves as a strong example for the potential of a multilateral agreement to address and prevent misuse of biological practices at a global scale, reaching practically universal agreement across UN member states. I propose the creation of a convention following a similar framework, however with emphasis placed on proactively identifying and sanctioning acceptable and unacceptable use cases.

Assumptions: success hinges heavily upon members reaching a collective understanding. Additionally, it assumes that national governance voices are strongly aligned with the private interests of the manufacturing sector in each member state.

Risk of failure (or success!):

a. Private interests commence manufacturing via synthetic morphogenesis despite absent participation at UN level.

b. An unanimous decision is made, however coverage of convention proves inadequate, requiring frequent amendments.

3. Establish an independent watchdog for regulation of industrial activity.

Purpose: provide additional layer of security to possible corruption at both national and international scales, serving to conduct random audits of the industrial activity within member states.

Design: using universal convention as a basis, establish key measurable indicators as a baseline for monitoring and governance. Indicators must be specific and comprehensive, defining actions by industry which must be precisely met to guarantee compliance.

Assumptions:

a. The watchdog is sufficiently capable of avoiding corruption. Handlings are fair, just, and do not impose on the national security or privacy of member states.

b. Member states (and industry) corporate sufficiently with requests of watchdog.

Risk of failure (or success!):

a. Undetected convention violations threaten international biosecurity or violate agreements on ethical practices.

4. Implement traceability protocols as..

a. Chain Of Custody (CoC) for synthesis and distribution of manufacturing precursors.

b. Integrated genetic barcodes.

Purpose: ensure all stakeholders are accountable for their contribution to the design, production, and distribution of biological materials prior, during, and post manufacture. In the event that a ‘leak’ occurs whereby an ecosystem is threatened, traceability permits rapid response and containment, as well as identification of processes violating the convention.

Design: a. Define ‘custody’ objects, assigning each with a distinct identifier, lifecycle, and scope of use. Establish assignable states (created, modified, transported, archived, destroyed etc.). Design custody record around requirements identified in convention. b. Establish composition, length, and ideal location of barcode locus.

Assumptions: a. Stakeholders adhere stringently to CoC protocol.

Risk of failure (or success!): CoC processes are too authoritative to strongly discourage prospective stakeholders, with sound intent, from entering the market. The approach cannot achieve the required market leverage to replace the use of conventional manufacturing techniques.

Next score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of governance goals.

“Synthetic morphogenesis as a manufacturing technique”

Last, drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties.

Although likely the most politically challenging, the convention outlined in Option 2 appears central to establishing the precedent required for ethical use of the novel technology. Option 1 is almost mandatory as a guardrail in the event of industrial pollution, as built-in failsafes provide the greatest degree of protection where incidental mishaps could have dire consequences for public health.

I propose the initial priorisation of Options 1 and 2 consecutively, whereby research seeks to first establish integrated safeguards appropriate for the requirements unique to manufacturing, whilst national policymakers work collaboratively to negotiate terms of the convention. Due to somewhat uncertain ramifications of misuse, initial collaboration directly with the United Nations Office of the Secretary General will provide the expertise necessary to move forward at a national scale, where progress is likely to be more fruitful.

Principles & Practices - Weekly Homework

Reflecting on what you learnt and did in class this week, outline any ethical concerns that arose, especially any that were new to you.

The ability to control the growth of organic matter in a way that is so precise feels unprecedented in the sense that it is not dissimilar to ‘playing god’; it is clear that this gives rise to countless ethical concerns:

Concern: Could the process inadvertently give rise to conscious organisms under the right (or more so wrong) circumstances?

• Action 2.

Concern: What happens if the technique is abused to grow human flesh for use in applications considered universally immoral?

• Action 2, Action 3.

Concern: Could self-propagating organisms be inadvertently created and released into ecosystems?

• Action 1.

Concern: Could the process create significant social upheaval amongst religious societies?

• Action 2.

I will continue to strongly consider this topic throughout the coming weeks, aiming to further philosophise and describe ideas as they arise!

Preparatory Questions (Week 2)

Professor Jacobsen

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?

• 1:10^6
• Human genome = ~ 3.2 gbp; approximately one incorrect base is inserted per 1 million correct insertions.
• Human biology employs several DNA proofreading and correction techniques; sense of correct geometry of base pairings, slowing catalysis when mismatch detected, and separation of mismatched primer to exonuclease site for removal etc.

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?

• Substantial degree of combinations possible mathematically (approx 10^190)
• Degeneracy of genetic code (multiple distinct codons encode amino acid) means a small fraction of total combinations actually encode for protein.

Dr. LeProust

What’s the most commonly used method for oligo synthesis currently?

• Phosphoramidite oligo synthesis

Why is it difficult to make oligos longer than 200nt via direct synthesis?

• Small discrepancies in coupling efficiency (95% - 99%) compound exponentially beyond 200nt, reducing sequence accuracy beyond.

Why can’t you make a 2000bp gene via direct oligo synthesis?

• As described above, synthesis becomes incredibly imprecise beyond about 200nt. Accumulation of errors will yield strand coding for dysfunctional characteristics.

Fidelity of DNA replication-a matter of proofreading

George Church

[Using Google & Prof. Church’s slide #4] What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

• Arginine, histidine, methionine, isoleucine, leucine, lysine, phenylalanine, threonine, tryptophan, and valine compose the 10 essential amino acids, where arginine is described as ‘conditionally essential’ in some mammalian species, such as humans (Rychen et al., 2018).
• The ‘Lysine Contingency’ describes the knock-out mutation performed in Jurassic Park (1993), in which the dinosaurs were rendered incapable of producing the essential aa Lysine (synthetic auxotrophy), requiring constant supplementation.
• The organic abundance of Lysine as an essential aa effectively renders this method useless, as they would be capable of receiving it through their conventional diet. This is especially interesting when considering prospective use of synthetic auxotrophy to control the growth of a manufactured organism; how might nature creatively find a method to circumvent the integrated safeguard?

Biochemistry, Essential Amino Acids.

Safety and efficacy of l-arginine produced by fermentation with Escherichia coli NITE BP-02186 for all animal species.

Scratchpad

Synthetic biology as…

• A manufacturing technique
• A bioremediation technique
• A fertilization technique
• A method to arrange bacterial growth (magnetotaxis)

Manufacturing technique (key concepts / words):

• Morphogenesis
• Developmental biology
• Tissue patterning
• Hox genes
• Homeoboxes
• Positional information (morphogen gradient)

Assorted references

Huigang, L., Menghui, L., Xiaoli, Z., Cui, H. and Zhiming, Y. (2022). Development of and prospects for the biological weapons convention. Journal of Biosafety and Biosecurity, [online] 4(1), pp.50–53. doi:https://doi.org/10.1016/j.jobb.2021.11.003.

Johnson, M.S., Venkataram, S. and Kryazhimskiy, S. (2023). Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes. Journal of Molecular Evolution, [online] 91(3), pp.263–280. doi:https://doi.org/10.1007/s00239-022-10083-z.

Teufel, J., López Hernández, V., Greiter, A., Kampffmeyer, N., Hilbert, I., Eckerstorfer, M., Narendja, F., Heissenberger, A. and Simon, S. (2024). Strategies for Traceability to Prevent Unauthorised GMOs (Including NGTs) in the EU: State of the Art and Possible Alternative Approaches. Foods, [online] 13(3), p.369. doi:https://doi.org/10.3390/foods13030369.

Trotsyuk, A.A., Waeiss, Q., Bhatia, R.T., Aponte, B.J., Heffernan, I.M.L., Madgavkar, D., Felder, R.M., Lehmann, L.S., Palmer, M.J., Greely, H., Wald, R., Goetz, L., Trengove, M., Vandersluis, R., Lin, H., Cho, M.K., Altman, R.B., Endy, D., Relman, D.A. and Levi, M. (2024). Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research. Nature Machine Intelligence, [online] 6(12), pp.1435–1442. doi:https://doi.org/10.1038/s42256-024-00926-3.

Wang, F. and Zhang, W. (2019). Synthetic biology: Recent progress, biosafety and biosecurity concerns, and possible solutions. Journal of Biosafety and Biosecurity, [online] 1(1), pp.22–30. doi:https://doi.org/10.1016/j.jobb.2018.12.003.

Week 2 HW: DNA Read / Write / Edit

Part 1: Benchling & In-silico Gel Art

Left: practice in-silico digestion featuring base sequence (Lambda) and restriction enzymes as specified.

Right: in-silico digestion representing a three-subunit amino polypeptide! To achieve the central grouping of bands I desired, I first asked chatGPT to recommend a short base sequence, then referenced the linear map in Benchling to iterate with endonucleases which produced cuts at the positions needed to make bp lengths which would match an outline I sketched.

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

No wetlab access. Given access in the coming months, I’ll definitely attempt to create my artwork given the required endonucleases + base sequence are accessible!

Part 3: DNA Design Challenge

3.1. Choose your protein.

From Magnetospirillum magneticum (AMB-1): HtrA/DegP family protease MamE

Magnetotaxic bacteria possess the highly curious ability to form organised, magnetic ‘inclusions’ in structures composed of ‘magnetosomes’. Magnetotaxis as a behavioural characteristic is particularly advantageous in aquatic environments, where detection of the earth’s magnetic field permits spatial sense in the water column as a method to locate nutrient-rich microenvironments. In the model organism Magnetospirillum magneticum AMB-1 (AMB-1), MamE protease is central to the biomineralization process required for magnetic crystal formation within magnetosomes.

I selected this protein as I find this behaviour incredibly enchanting! Its role in both magnetoreception and the biomineralization process is fascinating, and promises many intriguing applications if harnessed.

MamE protease AA sequence:

sp|Q2W8Q8|MAME_PARM1 Magnetosome formation protease MamE OS=Paramagnetospirillum magneticum (strain ATCC 700264 / AMB-1) OX=342108 GN=mamE PE=1 SV=1

>MAMFNGDVEDGGRGDASCGKDLKRYLMLMGVVALVVLFGAFIYRQSSGGLRLGAMLEQMGRGTGPAVNVPVQQGGPSAAVNPAMSVPAGARVAPPSAAGAIATMPPMVDFGPAPIGAGGPFSSVVTLLRNSVVAVTASSANGQAMPDPLGLANPDGLPHFANPATRSVENIGTGVIVRNDGFIVTNYHVVRGANSVFVTVQDDVGSTRYSAEIIKMDEALDLALLKVAPKTPLTAAVLGDSDGVQVADEVIAIGTPFGLDMTVSRGIISAKRKSMVIEGVTHSNLLQTDAAINQGNSGGPLVISNGTVVGINTAIYTPNGAFAGIGFAVPSNQARLFILDEVGWLPTSTAEGASMGLVAMQRPMGGGVGAAGPAIFAGTRAPHTDGRQNMDCTTCHDLIPAGNGRPAPMMPIAAPIPPPPIPMGAVSPHTDGRQNMNCANCHQMLGGAAPIAAPGLGGGAYRFAQPPGSLAINIQGPRGGQSTAAGTGRVTLLGAALTPMSQRLGAQTGVPVGRGVFISGVTPNTPAATAGLRPGDVLLKVDGRPVRLPEEVSAIMVEMHAGRSVRLGVLRDGDVRNMTLVAGPAGLAAAAVQAPAIADMAQPPMGGMAPTAPGMVAVPGGPAVMPKPPTEFNWLGMEIETFQAPRPITGVPGAVPVPGAKGAQVAEVLVGSRAAVAGLQANDLILEVNNRPVAGPARLDAAIKGATNAGQQILLKVNRNGQEFWIVL

3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.

MamE protease gene, reverse-translated via Genecorner:

>reverse translation of sp|Q2W8Q8|MAME_PARM1 Magnetosome formation protease MamE OS=Paramagnetospirillum magneticum (strain ATCC 700264 / AMB-1) OX=342108 GN=mamE PE=1 SV=1 to a 2184 base sequence of most likely codons.

atggcgatgtttaacggcgatgtggaagatggcggccgcggcgatgcgagctgcggcaaagatctgaaacgctatctgatgctgatgggcgtggtggcgctggtggtgctgtttggcgcgtttatttatcgccagagcagcggcggcctgcgcctgggcgcgatgctggaacagatgggccgcggcaccggcccggcggtgaacgtgccggtgcagcagggcggcccgagcgcggcggtgaacccggcgatgagcgtgccggcgggcgcgcgcgtggcgccgccgagcgcggcgggcgcgattgcgaccatgccgccgatggtggattttggcccggcgccgattggcgcgggcggcccgtttagcagcgtggtgaccctgctgcgcaacagcgtggtggcggtgaccgcgagcagcgcgaacggccaggcgatgccggatccgctgggcctggcgaacccggatggcctgccgcattttgcgaacccggcgacccgcagcgtggaaaacattggcaccggcgtgattgtgcgcaacgatggctttattgtgaccaactatcatgtggtgcgcggcgcgaacagcgtgtttgtgaccgtgcaggatgatgtgggcagcacccgctatagcgcggaaattattaaaatggatgaagcgctggatctggcgctgctgaaagtggcgccgaaaaccccgctgaccgcggcggtgctgggcgatagcgatggcgtgcaggtggcggatgaagtgattgcgattggcaccccgtttggcctggatatgaccgtgagccgcggcattattagcgcgaaacgcaaaagcatggtgattgaaggcgtgacccatagcaacctgctgcagaccgatgcggcgattaaccagggcaacagcggcggcccgctggtgattagcaacggcaccgtggtgggcattaacaccgcgatttataccccgaacggcgcgtttgcgggcattggctttgcggtgccgagcaaccaggcgcgcctgtttattctggatgaagtgggctggctgccgaccagcaccgcggaaggcgcgagcatgggcctggtggcgatgcagcgcccgatgggcggcggcgtgggcgcggcgggcccggcgatttttgcgggcacccgcgcgccgcataccgatggccgccagaacatggattgcaccacctgccatgatctgattccggcgggcaacggccgcccggcgccgatgatgccgattgcggcgccgattccgccgccgccgattccgatgggcgcggtgagcccgcataccgatggccgccagaacatgaactgcgcgaactgccatcagatgctgggcggcgcggcgccgattgcggcgccgggcctgggcggcggcgcgtatcgctttgcgcagccgccgggcagcctggcgattaacattcagggcccgcgcggcggccagagcaccgcggcgggcaccggccgcgtgaccctgctgggcgcggcgctgaccccgatgagccagcgcctgggcgcgcagaccggcgtgccggtgggccgcggcgtgtttattagcggcgtgaccccgaacaccccggcggcgaccgcgggcctgcgcccgggcgatgtgctgctgaaagtggatggccgcccggtgcgcctgccggaagaagtgagcgcgattatggtggaaatgcatgcgggccgcagcgtgcgcctgggcgtgctgcgcgatggcgatgtgcgcaacatgaccctggtggcgggcccggcgggcctggcggcggcggcggtgcaggcgccggcgattgcggatatggcgcagccgccgatgggcggcatggcgccgaccgcgccgggcatggtggcggtgccgggcggcccggcggtgatgccgaaaccgccgaccgaatttaactggctgggcatggaaattgaaacctttcaggcgccgcgcccgattaccggcgtgccgggcgcggtgccggtgccgggcgcgaaaggcgcgcaggtggcggaagtgctggtgggcagccgcgcggcggtggcgggcctgcaggcgaacgatctgattctggaagtgaacaaccgcccggtggcgggcccggcgcgcctggatgcggcgattaaaggcgcgaccaacgcgggccagcagattctgctgaaagtgaaccgcaacggccaggaattttggattgtgctg

3.3. Codon optimization.

When engineering the genome of one organism to express a foreign gene, codon optimization increases the chances that the translated sequence will result in the correct, functional protein product. Although the central dogma is inherent to all lifeforms, each class of organism will possess a ‘codon bias’ by which the particular codon (of which there may be multiple for one amino acid; degeneracy) will be represented by a comparatively higher concentration of tRNA molecules.

Vibrio natriegens is a gram negative, marine-dwelling prokaryote understood to possess one of the highest growth rates of any organism. This trait would be highly desirable for manufacturing applications where controlled biomineralisation could be harnessed to create solid artefacts in a short timeframe.

As V. Natriegens is not so forth a model organism and was thus omitted as an option from available online tools, I retrieved the codon usage table for Vibrio natriegens from the Codon Usage Database and used the BioInfromatics Reverse Translation Tool to directly generate the most likely to succeed, non-degenerate DNA sequence. This instead required the input of the AA sequence previously retrieved in 3.1.

>reverse translation of sp|Q2W8Q8|MAME_PARM1 Magnetosome formation protease MamE OS=Paramagnetospirillum magneticum (strain ATCC 700264 / AMB-1) OX=342108 GN=mamE PE=1 SV=1 to a 2184 base sequence of most likely codons.

atggcaatgtttaacggtgatgtagaagatggtggtcgtggtgatgcatcatgtggtaaagatcttaaacgttaccttatgcttatgggtgtagtagcacttgtagtactttttggtgcatttatctaccgtcaatcatcaggtggtcttcgtcttggtgcaatgcttgaacaaatgggtcgtggtacaggtccagcagtaaacgtaccagtacaacaaggtggtccatcagcagcagtaaacccagcaatgtcagtaccagcaggtgcacgtgtagcaccaccatcagcagcaggtgcaatcgcaacaatgccaccaatggtagattttggtccagcaccaatcggtgcaggtggtccattttcatcagtagtaacacttcttcgtaactcagtagtagcagtaacagcatcatcagcaaacggtcaagcaatgccagatccacttggtcttgcaaacccagatggtcttccacattttgcaaacccagcaacacgttcagtagaaaacatcggtacaggtgtaatcgtacgtaacgatggttttatcgtaacaaactaccatgtagtacgtggtgcaaactcagtatttgtaacagtacaagatgatgtaggttcaacacgttactcagcagaaatcatcaaaatggatgaagcacttgatcttgcacttcttaaagtagcaccaaaaacaccacttacagcagcagtacttggtgattcagatggtgtacaagtagcagatgaagtaatcgcaatcggtacaccatttggtcttgatatgacagtatcacgtggtatcatctcagcaaaacgtaaatcaatggtaatcgaaggtgtaacacattcaaaccttcttcaaacagatgcagcaatcaaccaaggtaactcaggtggtccacttgtaatctcaaacggtacagtagtaggtatcaacacagcaatctacacaccaaacggtgcatttgcaggtatcggttttgcagtaccatcaaaccaagcacgtctttttatccttgatgaagtaggttggcttccaacatcaacagcagaaggtgcatcaatgggtcttgtagcaatgcaacgtccaatgggtggtggtgtaggtgcagcaggtccagcaatctttgcaggtacacgtgcaccacatacagatggtcgtcaaaacatggattgtacaacatgtcatgatcttatcccagcaggtaacggtcgtccagcaccaatgatgccaatcgcagcaccaatcccaccaccaccaatcccaatgggtgcagtatcaccacatacagatggtcgtcaaaacatgaactgtgcaaactgtcatcaaatgcttggtggtgcagcaccaatcgcagcaccaggtcttggtggtggtgcataccgttttgcacaaccaccaggttcacttgcaatcaacatccaaggtccacgtggtggtcaatcaacagcagcaggtacaggtcgtgtaacacttcttggtgcagcacttacaccaatgtcacaacgtcttggtgcacaaacaggtgtaccagtaggtcgtggtgtatttatctcaggtgtaacaccaaacacaccagcagcaacagcaggtcttcgtccaggtgatgtacttcttaaagtagatggtcgtccagtacgtcttccagaagaagtatcagcaatcatggtagaaatgcatgcaggtcgttcagtacgtcttggtgtacttcgtgatggtgatgtacgtaacatgacacttgtacaggtccagcaggtcttgcagcagcagcagtacaagcaccagcaatcgcagatatggcacaaccaccaatgggtggtatggcaccaacagcaccaggtatggtagcagtaccaggtggtccagcagtaatgccaaaaccaccaacagaatttaactggcttggtatggaaatcgaaacatttcaagcaccacgtccaatcacaggtgtaccaggtgcagtaccagtaccaggtgcaaaaggtgcacaagtagcagaagtacttgtaggttcacgtgcagcagtagcaggtcttcaagcaaacgatcttatccttgaagtaaacaaccgtccagtagcaggtccagcacgtcttgatgcagcaatcaaaggtgcaacaaacgcaggtcaacaaatccttcttaaagtaaaccgtaacggtcaagaattttggatcgtactt

For HW consistency, I additionally optimised the ‘most likely codon’ MamE sequence for expression in Escherichia Coli (E. Coli) via the tool at Vectorbuilder:

The resulting Codon Adaption Index (CAI) for E. Coli fortunately still appeared to fall within a suitable range of <0.8.

3.4. You have a sequence! Now what?

The mamE sequence could first be integrated in-vitro into a recombinant plasmid, then transformed into V. Natriegen cells via electroporation, by which plasmids enter the cell through the creation of temporary pores in its membrane. The recombinant plasmids should then be transcribed and translated as normal by the appropriate enzymes within the cytosol. Electroporation is advantageous as is both efficient and relatively fast.

3.5. Bonus: How does it work in nature/biological systems?

In prokaryotes such as V. Natriegen, polycistronic transcription for a single mRNA molecule to be translated into different proteins. This is achieved via the inclusion of separate, distinct START and STOP codons, creating separate regions along the same mRNA molecule which are thus translated into distinct proteins.

I attempted to align the flow of information from DNA through polypeptide via the stacking of my three produced Benchling sequences manually. Unfortunately begins to break down in alignment, but accurate for the first three codons:

ChatGPT used to format correct alignment:

Part 4: Prepare a Twist DNA Synthesis Order

This process was really fascinating! I would love to learn more about the different inclusions along the way, and of their functionality + importance.

Review my plasmid here.

Part 5: DNA Read/Write/Edit

5.1 DNA Read (i) What DNA would you want to sequence (e.g., read) and why?

It would be intriguing to sequence morphogen-encoding genes (such as the HOX group) in species which have so far not been included. I would love to compare the base composition of different morphogen-encoding genes across distinct species to better understand the mechanisms behind different types of tissue patterning, and perhaps discover more conserved sequences.

(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?

I would select some combination of Single Molecule (third-generation; sequences individual bases directly without prior amplification) and Fluorescent 3D (other; distant in characteristics) to map genes of interest. Fluorescent 3D could be used to first determine the spatial concentration of suspected morphogen-encoding genes in a given tissue, then use Single Molecule to determine finer structural details. The capacity for Single Molecule to produce very long reads is desirable where developmental regions may be large and complex.

Fluorescent 3D:

• Input: Hybridization target probe pre-prepared based on theorized gene of interest (GOI). Tissue segment of interest pre-prepared on slide.

• Base calling method: iterative cycles of images detect each base as ‘spot in tissue’; corresponds to a particular gene identity.

• Output: spatial coordinates for gene expression + cells it is expressed by.

Single Molecule:

• Input: cells identified to contain GOI previous step are isolated and cultured, then lysed before separating and purifying DNA. Construction (oligonucleotide synthesis) and addition of nanopore sequencing adapters. Segments loaded into flow cell.

• Base calling method: individual DNA molecules pulled through nanopore by sequencing adapter, disrupting baseline ionic current of the detector which is detected and recorded by device. ‘Trace signals’ correspond to a particular base.

• Output: Long-read sequences (LRS)

5.2 DNA Write (i) What DNA would you want to synthesize (e.g., write) and why?

Artificially constructing a cluster of genes which allows for relatively precise, customisable control of tissue patterning would be highly appealing. Think programmable osteablasts, where the functional grading of bone-like structures can be controlled to produce an organic car chassis.

(ii) What technology or technologies would you use to perform this DNA synthesis and why?

Multiplexed Oligo Synthesis followed by Gibson Assembly appears a feasible option for the construction of large genes. Many sub-80 bp oligos can first be synthesised in parallel as a means of error-rate limitation, then combined via Gibson Assembly to construct the entire gene. In regards to morphogenic genes, both high precision and accuracy are required as the resulting phenotype is likely highly sensitive to accurate sequence composition.

Multiplexed Oligo Synthesis (in situ, via inkjet printing)

• Essential steps: computational design of desired DNA sequence, followed by division into 60-80 bp oligos with overlapping regions. Microarray pre-prepared with contact primer at each oligo synthesis site; single added sequentially in cycles at each site adhering to phosphamidite chemistry process. At termination length, cleaved, retrieved, and amplified.

• Limitations: error rate increases as sequence grows larger. Overlapping requirements of gibson assembly requires more cycles to be performed.

Gibson assembly

• Essential steps: Addition of exonuclease to prepared oligonucleotides to create sticky overhangs. Segments hybridise at designed complementary overlapping regions. DNA polymerase introduced to integrate missing nt’s at gaps. Ligase seals gaps throughout the incomplete backbone.

• Limitations: efficiency decreases as quantity of oligo fragments increases. Precise design of overlapping regions essential.

5.3 DNA Edit (i) What DNA would you want to edit and why?

It would be ideal to edit the genomes of more agricultural crops to introduce robust autofertilisation traits via symbiosis with microorganisms, for use in both developed and developing nations alike. In industrialised agriculture, external fertilisation is responsible for damaging pollution events throughout adjacent ecosystems. In developing regions where soil viability is poor, endemic low-yields resulting from limited fertilizer access drive perilous consequences. As the most abundant atmospheric gas, fixation of nitrogen directly from where directly where it is required makes significant sense.

(ii) What technology or technologies would you use to perform these DNA edits and why?

CRISPR/Cas9 currently appears the most desirable method for performing DNA modifications within plant embryo cells, particularly due to its efficiency and precision.

• Preparation + inputs: Design gRNA to match target sequence within plant embryo; incorporate into suitable vector alongside Cas9 gene.

• Limitations: Cuts at sequences similar to DNA target in embryo possible, requiring careful planning and validation post-edit. Editing efficiency dependent on accessibility of target gene.

References

Jerlie Mhay Matres, H., Hilscher, J., Datta, A., Armario-Nájera, V., Baysal, C., He, W., Huang, X., Zhu, C., Valizadeh-Kamran, R., Trijatmiko, K.R., Capell, T., Christou, P., Stoger, E. and Slamet-Loedin, I.H. (2021). Genome editing in cereal crops: an overview. Transgenic Research, 30(4), pp.461–498. https://doi.org/10.1007/s11248-021-00259-6

Lee, H.H., Ostrov, N., Wong, B.G., Gold, M.A., Khalil, A.S. and Church, G.M. (2016). Vibrio natriegens, a new genomic powerhouse. https://doi.org/10.1101/058487

Quinlan, A., Murat, D., Vali, H. and Komeili, A. (2011). The HtrA/DegP family protease MamE is a bifunctional protein with roles in magnetosome protein localization and magnetite biomineralization. Molecular Microbiology, 80(4), pp.1075–1087. https://doi.org/10.1111/j.1365-2958.2011.07631.x

Wan, J., Ji, R., Liu, J., Ma, K., Pan, Y. and Lin, W. (2024). Biomineralization in magnetotactic bacteria: From diversity to molecular discovery-based applications. Cell Reports, 43(12), p.114995. https://doi.org/10.1016/j.celrep.2024.114995

Weinstock, M.T., Hesek, E.D., Wilson, C.M. and Gibson, D.G. (2016). Vibrio natriegens as a fast-growing host for molecular biology. Nature Methods, 13(10), pp.849–851. https://doi.org/10.1038/nmeth.3970

Week 3 HW: Lab Automation

Part 1: OT-2 Automation

This week involved the plating and growth of a design created via the controlled aspiration of fluorescent Escherichia Coli (E. coli) through the OT-2 virtual platform.

Left: final result! P(eace)CR seemed a fitting name.

Right: initial reference image. source: peaceful pixel dove.

After some brief iteration, I settled on an 8-bit interpretation of the classic ‘dove with olive branch’, a relatively universal symbol of peace. As DesignerCells only has access to sfGFP and mRFP1 fluorescent recombinants, this design provided a suitable compromise to the design limitations. I will include the final outcome pending the lab is successful (fingers crossed!).

ChatGPT was used in conjunction with the CoLabs reference code to create the final script for the OT-2 liquid handling platform:

from opentrons import types

metadata = {    # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata
    'author': 'Zander Morris',
    'protocolName': 'P(eace)CR',
    'description': 'Prints the P(eace)CR graphic via the set of data points.',
    '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'
}

sfgfp_points = [(-26.4, 21.6),(-26.4, 19.2),(-24, 19.2),(-26.4, 16.8),(-24, 16.8),(-21.6, 16.8),(-14.4, 16.8),(-26.4, 14.4),(-24, 14.4),(-21.6, 14.4),(-16.8, 14.4),(-14.4, 14.4),(-21.6, 12),(-19.2, 12),(-16.8, 12),(-14.4, 12),(-24, 9.6),(-21.6, 9.6),(-19.2, 9.6),(-16.8, 9.6),(-26.4, 7.2),(-24, 7.2),(-19.2, 7.2),(-16.8, 4.8),(-21.6, 2.4),(-19.2, 2.4),(-16.8, 2.4),(-19.2, 0),(-16.8, 0),(-14.4, 0),(-14.4, -2.4),(-14.4, -4.8),(-14.4, -7.2),(-12, -9.6),(-12, -12),(-9.6, -14.4),(-9.6, -16.8),(-9.6, -19.2)]
mrfp1_points = [(12, 26.4),(7.2, 24),(9.6, 24),(12, 24),(26.4, 24),(4.8, 21.6),(7.2, 21.6),(9.6, 21.6),(12, 21.6),(24, 21.6),(26.4, 21.6),(2.4, 19.2),(4.8, 19.2),(9.6, 19.2),(12, 19.2),(24, 19.2),(26.4, 19.2),(0, 16.8),(9.6, 16.8),(12, 16.8),(21.6, 16.8),(24, 16.8),(26.4, 16.8),(0, 14.4),(9.6, 14.4),(12, 14.4),(19.2, 14.4),(21.6, 14.4),(24, 14.4),(26.4, 14.4),(0, 12),(9.6, 12),(12, 12),(14.4, 12),(16.8, 12),(21.6, 12),(24, 12),(26.4, 12),(0, 9.6),(7.2, 9.6),(9.6, 9.6),(12, 9.6),(21.6, 9.6),(24, 9.6),(26.4, 9.6),(-9.6, 7.2),(-7.2, 7.2),(0, 7.2),(7.2, 7.2),(9.6, 7.2),(21.6, 7.2),(24, 7.2),(-12, 4.8),(-9.6, 4.8),(-4.8, 4.8),(-2.4, 4.8),(0, 4.8),(4.8, 4.8),(21.6, 4.8),(24, 4.8),(-14.4, 2.4),(-12, 2.4),(-2.4, 2.4),(0, 2.4),(2.4, 2.4),(21.6, 2.4),(-9.6, 0),(-7.2, 0),(16.8, 0),(19.2, 0),(21.6, 0),(-7.2, -2.4),(-4.8, -2.4),(14.4, -2.4),(16.8, -2.4),(19.2, -2.4),(-7.2, -4.8),(-4.8, -4.8),(7.2, -4.8),(9.6, -4.8),(12, -4.8),(14.4, -4.8),(16.8, -4.8),(-4.8, -7.2),(-2.4, -7.2),(12, -7.2),(-4.8, -9.6),(-2.4, -9.6),(0, -9.6),(14.4, -9.6),(16.8, -9.6),(19.2, -9.6),(-2.4, -12),(0, -12),(2.4, -12),(4.8, -12),(19.2, -12),(21.6, -12),(0, -14.4),(2.4, -14.4),(4.8, -14.4),(7.2, -14.4),(9.6, -14.4),(12, -14.4),(19.2, -14.4),(21.6, -14.4),(24, -14.4),(26.4, -14.4),(28.8, -14.4),(14.4, -16.8),(19.2, -16.8),(21.6, -16.8),(24, -16.8),(26.4, -16.8),(28.8, -16.8),(16.8, -19.2),(19.2, -19.2),(21.6, -19.2),(24, -19.2),(19.2, -21.6),(21.6, -21.6),(19.2, -24)]

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
  ###

  # -----------------------------
  # Printing parameters
  # -----------------------------
  VOL_PER_DOT = 0.75

  # Keep aspirates comfortably below 20uL for accuracy/safety
  MAX_ASPIRATE_UL = 18.0
  MAX_BATCH_DOTS = int(MAX_ASPIRATE_UL // VOL_PER_DOT)  # 18.0 // 0.75 = 24

  # Choose where on Z you actually want to dispense.
  # Start conservative: 0 means "at agar_plate['A1'].top() plane".
  # If your drops need to touch the agar more, try -0.5 or -1.0 after testing.
  DISPENSE_DZ = 2

  def point_location_from_center(dx, dy, dz=DISPENSE_DZ):
      # Offsets are in mm
      return center_location.move(types.Point(x=dx, y=dy, z=dz))

  def print_points(points, color_name):
      pipette_20ul.pick_up_tip()

      i = 0
      while i < len(points):
          batch = points[i:i + MAX_BATCH_DOTS]
          batch_volume = len(batch) * VOL_PER_DOT

          # Pull enough dye for this batch
          pipette_20ul.aspirate(batch_volume, location_of_color(color_name))

          # Dispense each dot
          for (dx, dy) in batch:
              loc = point_location_from_center(dx, dy)
              dispense_and_detach(pipette_20ul, VOL_PER_DOT, loc)

          i += MAX_BATCH_DOTS

      pipette_20ul.drop_tip()

  # -----------------------------
  # Print your two datasets
  # -----------------------------
  print_points(sfgfp_points, "Green")
  print_points(mrfp1_points, "Red")

Prompts were primarily as follows. Small corrections and confirmations were omitted for clarity:

I want to take a set of predefined x and y points and allow my them to be interpreted by Opentrons OT-2 via a Python script, so that the actuator can move to each point and dispense a set volume of liquid. How do setup a grid and allow the OT-2 to move to each location?

[code inserted]. This is the code I have so far. How can I get it to navigate between the points?

0.75 ul per dot please.

I can’t get my points list to correctly define?

Okay starting over. this is my code so far. what should I add precisely to get it to work?

[series of errors corrected via feedback and correction loop with ChatGPT]

Part 2: Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

“An Automation Workflow for High‐Throughput Manufacturing and Analysis of Scaffold‐Supported 3D Tissue Arrays”

This paper details the use of the OT-2 as a means to automate various processes and assays associated with the culture of patient-derived organoids. Conventionally a process performed manually, the scaffold-supported platform for orgonoid-based tissue (SPOT) method is automated as a possible solution to improve throughput and scalability. This approach provides a possible solution to the limited conventional scalability of SPOT. As SPOT is a method of drug discovery, specifically in the development of therapeutics personalized to the specific tissue of a patient’s tumour, increasing the speed and volume at which this process may be performed via OT-2 lab automation is a highly promising application of lab-automation technology.

Part 3: Write a description about what you intend to do with automation tools for your final project.

I want to investigate the process of controlling the spatial formation of a biofilm (or the biomineralisation process) across the surface of a morphable 3D artifact (scaffold).

Although I am still unsure of the precise outcome, I would like to explore the possibility of integrating automation at two points:

1. At culture production: to perform the recombinant process, automating the transformation process to facilitate the uptake of engineered plasmids via a desirable prokaryotic host.

2. Within physical tool: to serve as a repeatbly programmable platform for biofilm formation.

I will work to crystalise the precise integration of automation methods in the coming weeks.

References

Cao, R., Li, N.T., Latour, S., Cadavid, J.L., Tan, C.M., Forman, A., Jackson, H.W. and McGuigan, A.P. (2023). An Automation Workflow for High‐Throughput Manufacturing and Analysis of Scaffold‐Supported 3D Tissue Arrays. Advanced Healthcare Materials, [online] 12(19). doi:https://doi.org/10.1002/adhm.202202422.

Reference image source: StockCake (2026). Peaceful Pixel Dove. [online] StockCake. Available at: https://stockcake.com/i/peaceful-pixel-dove_3822449_1791913 [Accessed 27 Feb. 2026].