Week 1 HW: Principles and Practices

Homework Week 1: Class Assignment

Biological Engineering Application

First Steps towards “Intelligence in a (warehouse)-dish”

Guided by the vision of building a biological general computing system, the goal of the proposed tool is to provide a minimal, yet replicable brain organoid based system, that can be engineered to exhibit controllable, learning-like signal processing behaviour. The system consists of 3 conceptual parts (input - computation - output), that manifest in 2 integrated physical devices.

Firstly, there is the 3D organoid culture chamber that handles the computation. Brain organoids, based on iPSC cultures, with a diameter below 500 micrometer, containing less than 100,000 cells can be manufactured with a high degree of standardization and scalability. More recent research allowed the long-term culture of brain organoids exceeding one year, displaying spontaneous electrophysical (re)activity, show extensive myelination, and can be enriched with several relevant cell types, e.g. oligodendrocytes, microglia, and astrocytes [1, 2, 3, 4].

Second is the input-output system that handles the input and output functions. As current research focuses on avascular brain organoids that require delivery of nutrients via diffusion, a higher order, more complex brain organoid requires brain vasculature. The microfluidic system mimics above-mentioned vasculature and contributes to the development of higher-order brain organoids. Additionally, the system can deliver and record chemical signals in a spatiotemporal manner [1, 5, 6]. Another activation mechanism lies in 3D microelectronic arrays. These allow precise stimulation and recording of spatiotemporal signals across the entire surface of the brain organoid [7].

Therefore, combining advances in 3D culture of brain organoids, with a combination of microfluidics and microelectronic arrays, poses an exciting research avenue and aims to contribute to the research topic of Organoid Intelligence (OI) [1].

Citations

1. Smirnova, L., Caffo, B.S., Gracias, D.H., Huang, Q., Morales Pantoja, I.E., Tang, B., Zack, D.J., Berlinicke, C.A., Boyd, J.L., Harris, T.D. and Johnson, E.C., 2023. Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish. Frontiers in Science, 1, p.1017235
2. Pamies, D., Barrera, P., Block, K., Makri, G., Kumar, A., Wiersma, D., Smirnova, L., Zhang, C., Bressler, J., Christian, K.M. and Harris, G., 2016. A human brain microphysiological system derived from induced pluripotent stem cells to study neurological diseases and toxicity. Altex, 34(3), p.362.
3. Monzel, A.S., Smits, L.M., Hemmer, K., Hachi, S., Moreno, E.L., van Wuellen, T., Jarazo, J., Walter, J., Brüggemann, I., Boussaad, I. and Berger, E., 2017. Derivation of human midbrain-specific organoids from neuroepithelial stem cells. Stem cell reports, 8(5), pp.1144-1154.
4. Marton, R.M., Miura, Y., Sloan, S.A., Li, Q., Revah, O., Levy, R.J., Huguenard, J.R. and Pașca, S.P., 2019. Differentiation and maturation of oligodendrocytes in human three-dimensional neural cultures. Nature neuroscience, 22(3), pp.484-491.
5. Grigoryan, B., Paulsen, S.J., Corbett, D.C., Sazer, D.W., Fortin, C.L., Zaita, A.J., Greenfield, P.T., Calafat, N.J., Gounley, J.P., Ta, A.H. and Johansson, F., 2019. Multivascular networks and functional intravascular topologies within biocompatible hydrogels. Science, 364(6439), pp.458-464.
6. Sabatini, B.L. and Tian, L., 2020. Imaging neurotransmitter and neuromodulator dynamics in vivo with genetically encoded indicators. Neuron, 108(1), pp.17-32.
7. Huang, Q., Tang, B., Romero, J.C., Yang, Y., Elsayed, S.K., Pahapale, G., Lee, T.J., Morales Pantoja, I.E., Han, F., Berlinicke, C. and Xiang, T., 2022. Shell microelectrode arrays (MEAs) for brain organoids. Science advances, 8(33), p.eabq5031.

Governance Goals

Harm reduction

Minimize the harm on the biological system through careful research design, consideration of biological responses and sensibility at the intersection of the research goal and societal norms.

Biological harm reduction emphasizes preserving the physiological integrity of the organoid system on the biological level. The intent is to avoid inducing unnecessary stress, damage or pathological stress in living tissue and to ensure that experimental interactions remain compatible with healthy biological function.

Limitation of harm arising from emergent properties seeks to prevent unintended transitions toward higher-order dynamics that could raise ethical concerns, as organoids exhibit more complex, self-organizing behavior. This includes constraining system complexity and maintaining clear boundaries on the duration and scope of experiments, as well as ensuring that organoids are not maintained beyond their justified research purpose.

Donor rights

Ensuring that individuals that contribute their biological samples for research retain fair rights, autonomy and benefits and are protected again extractive behaviour of third parties though the entire lifecycle and downstream applications.

Transparent disclosure of organoid intelligence research ensures that donors clearly understand that their biological material may be used to generate brain organoids for learning-like signal processing and computational experimentation.

Fairness in benefit sharing and protection against discrimination aims to include donors into the benefits generated by the research, nor expose them to harm through stigmatization, profiling, or inequitable use of data derived from their biological contributions. Donor participation should not create asymmetries where value is extracted without corresponding ethical consideration.

Privacy preservation protects donors from identity linkage, misuse or inappropriate inference of personal traits.

Access

Ensuring that access to the systems themselves as well as the associated knowledge and benefits are disseminated in a fair way, while extractive use attempts are actively prevented.

Equitable research access seeks to avoid concentration of organoid intelligence capabilities within a small number of research groups and institutions. The intent is to enable participation by a diverse range of (also non-scientific) communities.

Non-exclusive access tries to ensure that foundational biological systems and insights won’t be locked behind proprietary structures. The goal is to preserve openness at the level of core knowledge and enabling technologies.

Limiting extractive use ensures that access to sensitive biological data does not enable exploitation of contributing or downstream affected individuals. This goal emphasizes that organoid intelligence research should generate value that is aligned with societal benefit.

Governance Actions

Action 1: Technically Enforced Graduated Freedom

Purpose: Redefining the locus of governance from external to internal. The goal of this action is to embed harm reduction directly into the technical architecture, while still preserving scientific flexibility. Instead of imposing rigid limits, the system provides ethical “factory settings” that enable safe and broadly acceptable use by default, while allowing controlled exploration beyond these settings when justified.

Design: The organoid computing platform is developed with a set of default operating parameters, e.g. size, culture duration, stimulation intensity, and learning persistence. These can be used without additional ethical review. At the same time, a clearly defined subset of parameters is designated as research-variable, allowing researchers to intentionally explore higher complexity, longer duration, or altered learning dynamics. Deviations beyond default settings require explicit justification and appropriate ethical oversight, but are technically supported rather than prohibited. The system logs when and how parameters are modified.

Assumptions: It is assumed that most researchers will operate within default settings unless there is a genuine scientific reason to deviate.. It also assumes that technical transparency (rather than hard locks) is an effective governance lever.

Risks of Failure & “Success”: The model fails if defaults are treated without care rather than minimum safeguards, or if parameter variation becomes routine without oversight. Successful use of the system could create a false sense of ethical safety. There is also a risk that logging is perceived as surveillance, which in turn would discourage experimentation.

Action 2: Reciprocal Donor Stewardship

Purpose: Current consent frameworks mostly are a one-time action, offering limited protection against extractive use. This action proposes a reciprocal donor stewardship model, in which the collecting institution acts as a fiduciary to protect the donor’s interests, but also to maintain a two-way informational relationship. Donors are recognized as long-term stakeholders.

Design: Universities and biobanks adopt stewardship responsibilities as a condition for ethical approval and public funding. Donors opt into a structured relationship that includes regular high-level updates on relevant scientific developments as well as personalized notifications when findings derived from their samples may have health relevance.

Assumptions: This model assumes donors want an ongoing relationship. Furthermore it assumes that institutions can responsibly manage communication. It also assumes that research findings can be meaningfully categorized into general scientific updates versus personally relevant information.

Risks of Failure & “Success”: The model may fail if institutions lack the willingness to maintain long-term engagement. There is also a risk that donors misinterpret research signals as medical diagnoses, causing anxiety or harm. Successful implementation could blur the boundary between research and clinical care.

Action 3: Simple Public-Interest Licensing

Purpose: Biological computing moves toward commercialization, this creates the option that foundational technologies become locked behind exclusive or opaque licensing arrangements. The goal of this action is to preserve the public-interest while enabling rapid and practical commercialization, ensuring that ethical constraints do not themselves become barriers to innovation.

Design: Universities and spin-outs adopt standardized, plain-language public-interest licenses. These licenses are intentionally short, unambiguous, and easy to interpret, defining only a small number of clearly prohibited applications, while leaving all other commercial uses unrestricted. Investors and companies opt in upfront, gaining predictability.

Assumptions: This approach assumes that ethical constraints can be expressed in a small number of clear, enforceable prohibitions. It also assumes that companies and investors value legal certainty and speed of commercialization enough to accept modest limits on exclusivity and application scope.

Risks of Failure & “Success”: The model fails if prohibited-use categories are defined too broadly or too minimally. Conversely, “success” could lead to widespread adoption, which may normalize these constraints.

Scoring Matrix

Recommendation to ethics boards

For research on organoid intelligence, ethics boards should prioritize governance mechanisms that operate at the point of experimental design and focus on setting default use behaviour. Based on the policy goals of harm reduction, donor rights, and access, I recommend ethics boards focus primarily on Action 1 (Technically Enforced Graduated Freedom) for the experiment design and Action 2 (Reciprocal Donor Stewardship) ensuring a modern relationship management. Action 3 (Simple Public-Interest Licensing) will become more relevant in the near future, therefore it should be considered down the road.

First, ethics boards should require technically enforced ethical defaults for organoid intelligence systems. Rather than relying on lengthy binary approval decisions, projects should be judged on justification of any intended deviations. Second, ethics boards should transition from one-time consent towards reciprocal stewardship plans. These plans should treat donors as long-term stakeholders engaging in two-way communication when findings may be personally relevant. This strengthens donor autonomy and public trust without conflating research with clinical care. While ethics boards do not manage IP, they should recommend investigators to include public interest licensing in their research lifecycle.

The risk that overly cautious governance discourages legitimate research. These uncertainties argue for graduated, revisitable oversight rather than rigid prohibitions.

Use of Generative AI

Generative AI was used as a drafting aid throughout the development of this homework assignment. Specifically, it supported the structuring and refinement of complex ideas at the intersection of organoid intelligence, and governance, including the logical separation and articulation of policy goals and governance actions. The AI was used to iteratively clarify language and explore alternative framings, while all substantive ideas, judgments, and final decisions were made by the author.

Week 2 Lecture Prep

Prof. Dr. Jacobson

Nature’s machinery for copying DNA is called polymerase.

What is the error rate of polymerase?

The error rate is 1:10E6 (see page 8, right side “biological systhesis”)

How does this compare to the length of the human genome?

The human genome has a length of 3.2 Gbp or 3.2 x 10E9 (see page 10 diagram)

How does biology deal with that discrepancy?

The human body engages in a “proofreading” process (page 8). The error correction process, called “MutS Repair System” is described in detail in Lamers et al. Nature 407:711 (2000) (page 14)

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?

The average human protein is 1036 bp, equating to 1036 / 3 = 345 amino acids. On average 3 codons encode an amino acid, therefore the number of number of different DNA sequences encoding the same 345 amino acids is 3 x 10E345.

Dr. LeProust

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

Phosphoramidite chemistry. Page 2 illustrates the 4 step process. See the historical development from page 9.

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

Not directly found in the slides. Searching further ressources says the fundamental issue is cumulative coupling efficiency loss. Each cycle has a coupling efficiency of ~99–99.5%. The overall yield is the stepwise efficiency raised to the power of the oligo length: for a 200-mer at 99.5% coupling efficiency, the yield of full-length product is roughly 37%.

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

Same rational as answer above. Decay is exponential, therefore the efficiency drops to negligible levels.

Prof. Dr. 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”?

Phenylalanine (F), Valine (V), Threonine (T), Tryptophan (W), Isoleucine (I), Methionine (M), Leucine (L), Lysine (K), Histidine (H), Arginine (R)

Opus 4.6 Prompt: How does this relate to Lysine Deficiency?

The connection to Jurassic Park wasn’t clear to me, as i haven’t seen the movie. Therefore here is Opus answer (which was an amusing read): “The “Lysine Contingency” from Jurassic Park

In the film/novel, the engineers claim dinosaurs were designed to be lysine-deficient — unable to produce lysine — so they’d die without supplemental lysine provided by the park, serving as a biocontainment mechanism.

Why It Doesn’t Work The “contingency” is essentially meaningless as a containment strategy because lysine is already an essential amino acid for all animals. No animal synthesizes its own lysine. The dinosaurs wouldn’t be any more dependent on external lysine than a cow, a bird, or a human. Any environment with protein-containing food sources (plants, prey, insects) would supply adequate lysine. The dinosaurs would simply eat and obtain lysine the same way every other animal does.

Connection to Church’s Work Interestingly, slide #4’s emphasis on “knowing your monomers” and the references to non-standard amino acids (NSAAs) and xenomicrobiology (genetic code engineering, referenced from the 2016 Microb. Biotech paper) point to what actual biocontainment might look like. As shown in slides 39–42, Church’s lab has built Genomically Recoded Organisms (GROs) that depend on NSAAs like BipA — amino acids that truly do not exist in nature. This is a real lysine contingency done right: organisms that require a synthetic amino acid unavailable in any natural food web, providing genuine genetic and metabolic isolation.

So Crichton’s fictional biocontainment was flawed at the basic biochemistry level, but Church’s lab has essentially built the version that actually works.”

Week 2 HW: DNA Read, Write & Edit

Part 0: Basics of Gel Electrophoresis

• Watch Week 2 Lecture (Zoom)
• Watch Week 2 Recitation (Zoom)
• Watch BioBootcamp Day 1 - Day 3 (Zoom)

Part 1: Benchling & In-silico Gel Art

• Make a free account at benchling.com
• Import the Lambda DNA.
• Simulate Restriction Enzyme Digestion with the following Enzymes:
  • EcoRI
  • HindIII
  • BamHI
  • KpnI
  • EcoRV
  • SacI
  • SalI

Artwork

After struggling quite some time with the task of creating artwork with the limited amount of restriction enzymes, in the end decided to stick to a relatively easy and repetitive pattern that with a little imagination has a lot of versatile interpretations: It can be two friends hanging out It can be DNA (or at least a rought estimation of the firrst two loops) To help you visualize it a bit better i created some generative AI art

To create the loop like structure, I used the Restriction enzymees BamHI - KpnI - SalI - KpnI - BamHI

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

not relevant as I don’t have access to a lab

Part 3: DNA Design Challenge

3.1. Choose your protein.

For Week 2 Homework I choose the Growth/differentiation factor 8 (short GDF-8), also known as human myostatin protein. I choose myostatin for the inital reason, that it was the first protein that came to mind. Being known for the viral video of Jo Zayner injecting the DIY-Gene Therapy to knock out the myostatin associated gene, or the many pictures of muscled animals, like cattle and dogs.

Digging further, myostatin seemed to be a good choice, not only for it’s fame. It is a well studied protein, with a clear function to negatively regulate muscle growth, as seen with the example of the “jacked” bagle or the cattle. Furthermore myostatin is not only interesting to biohackers and instagram scrollers, it has actual therapeutical interest, and is actively researched to combat muscular dystrophy. In the up and coming field of Longevity, myostatin is researched to mitigate age-related muscle loss.

Sequence

>sp|O14793|GDF8_HUMAN Growth/differentiation factor 8 OS=Homo sapiens OX=9606 GN=MSTN PE=1 SV=1 MQKLQLCVYIYLFMLIVAGPVDLNENSEQKENVEKEGLCNACTWRQNTKSSRIEAIKIQI LSKLRLETAPNISKDVIRQLLPKAPPLRELIDQYDVQRDDSSDGSLEDDDYHATTETIIT MPTESDFLMQVDGKPKCCFFKFSSKIQYNKVVKAQLWIYLRPVETPTTVFVQILRLIKPM KDGTRYTGIRSLKLDMNPGTGIWQSIDVKTVLQNWLKQPESNLGIEIKALDENGHDLAVT FPGPGEDGLNPFLEVKVTDTPKRSRRDFGLDCDEHSTESRCCRYPLTVDFEAFGWDWIIA PKRYKANYCSGECEFVFLQKYPHTHLVHQANPRGSAGPCCTPTKMSPINMLYFNGKEQII YGKIPAMVVDRCGCS

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

The Central Dogma discussed in class and recitation describes the process in which DNA sequence becomes transcribed and translated into protein. 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. Using one of the tools discussed in class, NCBI or online tools (google “reverse translation tools”), determine the nucleotide sequence that corresponds to the protein sequence you chose above.

Using the Reverse Translate tool from Bioinformatics.org, I got the following results:

>reverse translation of sp|O14793|GDF8_HUMAN Growth/differentiation factor 8 OS=Homo sapiens OX=9606 GN=MSTN PE=1 SV=1 to a 1125 base sequence of most likely codons. atgcagaaactgcagctgtgcgtgtatatttatctgtttatgctgattgtggcgggcccg gtggatctgaacgaaaacagcgaacagaaagaaaacgtggaaaaagaaggcctgtgcaac gcgtgcacctggcgccagaacaccaaaagcagccgcattgaagcgattaaaattcagatt ctgagcaaactgcgcctggaaaccgcgccgaacattagcaaagatgtgattcgccagctg ctgccgaaagcgccgccgctgcgcgaactgattgatcagtatgatgtgcagcgcgatgat agcagcgatggcagcctggaagatgatgattatcatgcgaccaccgaaaccattattacc atgccgaccgaaagcgattttctgatgcaggtggatggcaaaccgaaatgctgctttttt aaatttagcagcaaaattcagtataacaaagtggtgaaagcgcagctgtggatttatctg cgcccggtggaaaccccgaccaccgtgtttgtgcagattctgcgcctgattaaaccgatg aaagatggcacccgctataccggcattcgcagcctgaaactggatatgaacccgggcacc ggcatttggcagagcattgatgtgaaaaccgtgctgcagaactggctgaaacagccggaa agcaacctgggcattgaaattaaagcgctggatgaaaacggccatgatctggcggtgacc tttccgggcccgggcgaagatggcctgaacccgtttctggaagtgaaagtgaccgatacc ccgaaacgcagccgccgcgattttggcctggattgcgatgaacatagcaccgaaagccgc tgctgccgctatccgctgaccgtggattttgaagcgtttggctgggattggattattgcg ccgaaacgctataaagcgaactattgcagcggcgaatgcgaatttgtgtttctgcagaaa tatccgcatacccatctggtgcatcaggcgaacccgcgcggcagcgcgggcccgtgctgc accccgaccaaaatgagcccgattaacatgctgtattttaacggcaaagaacagattatt tatggcaaaattccggcgatggtggtggatcgctgcggctgcagc

3.3. Codon optimization.

Once a nucleotide sequence of your protein is determined, you need to codon optimize your sequence. You may, once again, utilize google for a “codon optimization tool”. In your own words, describe why you need to optimize codon usage. Which organism have you chosen to optimize the codon sequence for and why?

Organisms have different procivities for using certain codons. Some codons are used more frequently in one orgamis. So for the same amino acide certain codons are “preferred” over others. If the inserted DNA matches the preferred codons of the organism more translation happens. In my case, as the protein I’m trying to express is of human origin, though expressed in e.coli (common for early experiments), many of the codons common in homo sapiens, are rare in e.coli.

Using the Codon Optimization tool from VectorBuilder, and choosing e.coli K-12 as the organism i get the following optimized codon:

ATGCAGAAACTGCAGCTGTGCGTTTACATTTATCTGTTCATGCTGATTGTGGCCGGCCCGGTGGATCTGAACGAAAACAGTGAACAGAAAGAAAACGTGGAAAAAGAAGGTCTGTGCAACGCCTGTACCTGGCGCCAGAATACCAAATCGAGCCGCATTGAAGCCATTAAAATTCAGATCCTGTCAAAACTGCGTCTGGAAACCGCGCCGAATATTAGCAAAGATGTGATCCGTCAGCTGCTGCCGAAAGCCCCGCCGCTGCGTGAACTGATTGATCAGTATGATGTGCAGCGCGATGATAGCAGCGATGGCAGCCTGGAAGATGATGATTATCACGCGACCACCGAAACCATTATTACCATGCCGACCGAAAGCGATTTTCTGATGCAGGTGGATGGCAAACCGAAATGCTGCTTCTTCAAATTTAGCTCGAAAATTCAATATAATAAAGTGGTGAAAGCGCAGCTGTGGATCTATCTGCGCCCGGTGGAAACCCCGACCACCGTGTTTGTGCAGATTCTGCGCCTGATTAAACCGATGAAAGATGGCACCCGCTACACCGGCATTCGCAGCCTGAAACTGGATATGAACCCGGGCACCGGCATCTGGCAGAGCATTGATGTGAAAACCGTTCTGCAGAATTGGCTGAAACAGCCGGAAAGCAACCTGGGCATTGAAATTAAAGCCCTGGATGAAAATGGCCATGATCTGGCAGTGACCTTTCCGGGCCCGGGCGAAGATGGCCTGAATCCGTTCCTGGAAGTGAAAGTGACCGATACCCCGAAACGCAGCCGCCGCGACTTTGGCCTGGATTGCGATGAACACAGCACCGAAAGCCGCTGCTGCCGCTACCCGCTGACCGTGGATTTTGAAGCGTTCGGCTGGGATTGGATTATTGCGCCGAAACGCTATAAGGCGAACTACTGCAGCGGTGAATGCGAATTTGTGTTTCTGCAGAAATATCCGCACACCCATCTGGTGCACCAGGCAAACCCGCGCGGCAGCGCGGGCCCGTGCTGTACCCCGACCAAAATGAGCCCGATTAACATGCTGTATTTTAACGGCAAAGAACAGATTATCTATGGCAAAATCCCGGCGATGGTTGTGGATCGCTGCGGTTGTAGC while avoiding cleavage sites of restriction enzymes: BamHI HindIII

Whether the e.coli is the proper host for this application is debateable, as e.coli lacks the post-translational modification capabilities. Other hosts like CHO cells or human cells, could prove to be a better choice, if one aims for a properly folded, functioning protein.

3.4. You have a sequence! Now what?

What technologies could be used to produce this protein from your DNA? Describe in your words the DNA sequence can be transcribed and translated into your protein. You may describe either cell-dependent or cell-free methods, or both.

Let me focus on cell-dependent methods. There are several expression systems like e.coli, yeast and mamalian cells. Each system has their pros and cons. E.coli is a procaryote, it’s cheap, fast and well established, though it lacks post translational modification abilities. Therefore the myostatin might be misfolded. Addtionally the cells have to be lysed to get to the myostatin. Yeast is eukaryotic, therefore it has some post-translational folding capabilities, additionally secretion is possible. Mammalian cells offer human-like folding, and secretion, though they grow slower, are a-lot harder to handle, offer lower yields and are significantly more expensive than e.coli or yeast.

To get the DNA into the orgamism:

1. Perform PCR on the optimized myostatin DNA strand, to generate many copies of said gene. make sure the DNA strand is flanked with BanHI and HindIII sites.
2. Open the plasmid at the previously avoided restriction sites (BamHI HindIII), creating compatible sticky ends.
3. Cut the DNA strand with the same restriction enzymes
4. Mix plasmid and DNA strand, introduce ligase enzymes and ATP, to connect the matching sticky ends.
5. Transform the plasmid into e.coli using common transformation methods, e.g. heat-shock or electroporation
6. (optional) screen for uptake of plasmid

Having chosen e.coli here the DNA would transscribe into mRNA, which in turn would translate into myostating facilitated by ribosomes. For lab-sized fermentation, a batch system is sufficient, on industrial scale a fed-batch system would be used.

Part 4: Prepare a Twist DNA Synthesis Order

4.1. Create a Twist account and a Benchling account

• Create Twist Account
• Create Benchling Account

4.2. Build Your DNA Insert Sequence

Following the example on the course site, this is the linear map of the sequence:

>Test_myostatin TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGCCATTAAAGAGGAGAAAGGTACCATGCAGAAACTGCAGCTGTGCG TTTACATTTATCTGTTCATGCTGATTGTGGCCGGCCCGGTGGATCTGAACGAAAACAGTGAACAGAAAGAAAACGTGGA AAAAGAAGGTCTGTGCAACGCCTGTACCTGGCGCCAGAATACCAAATCGAGCCGCATTGAAGCCATTAAAATTCAGATC CTGTCAAAACTGCGTCTGGAAACCGCGCCGAATATTAGCAAAGATGTGATCCGTCAGCTGCTGCCGAAAGCCCCGCCGC TGCGTGAACTGATTGATCAGTATGATGTGCAGCGCGATGATAGCAGCGATGGCAGCCTGGAAGATGATGATTATCACGC GACCACCGAAACCATTATTACCATGCCGACCGAAAGCGATTTTCTGATGCAGGTGGATGGCAAACCGAAATGCTGCTTC TTCAAATTTAGCTCGAAAATTCAATATAATAAAGTGGTGAAAGCGCAGCTGTGGATCTATCTGCGCCCGGTGGAAACCC CGACCACCGTGTTTGTGCAGATTCTGCGCCTGATTAAACCGATGAAAGATGGCACCCGCTACACCGGCATTCGCAGCCT GAAACTGGATATGAACCCGGGCACCGGCATCTGGCAGAGCATTGATGTGAAAACCGTTCTGCAGAATTGGCTGAAACAG CCGGAAAGCAACCTGGGCATTGAAATTAAAGCCCTGGATGAAAATGGCCATGATCTGGCAGTGACCTTTCCGGGCCCGG GCGAAGATGGCCTGAATCCGTTCCTGGAAGTGAAAGTGACCGATACCCCGAAACGCAGCCGCCGCGACTTTGGCCTGGA TTGCGATGAACACAGCACCGAAAGCCGCTGCTGCCGCTACCCGCTGACCGTGGATTTTGAAGCGTTCGGCTGGGATTGG ATTATTGCGCCGAAACGCTATAAGGCGAACTACTGCAGCGGTGAATGCGAATTTGTGTTTCTGCAGAAATATCCGCACA CCCATCTGGTGCACCAGGCAAACCCGCGCGGCAGCGCGGGCCCGTGCTGTACCCCGACCAAAATGAGCCCGATTAACAT GCTGTATTTTAACGGCAAAGAACAGATTATCTATGGCAAAATCCCGGCGATGGTTGTGGATCGCTGCGGTTGTAGCCAT CACCATCACCATCATCACTAACCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTG TTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

Link to Test_myostatin

In Twist having chosen the Clonal Gene, with the vector: pTwist Amp High Copy, this is the output from Twist Following is the final Clonal Gene in Benchling

Part 5: DNA Read/Write/Edit

5.1 DNA Read

(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).

I’m interested in genetic origin for muscle growth. People have different outcomes for the similar inputs, I’m interested into the marginal influence Gene’s have on ones physiological development. While overall health is important, this also has clinical application, e.g. for patients with muscle loss diseases, age-related muscle atrophy. Therefore a number of genes can be studied. Using search and LLMs, these are relevant proteins for muscle growth,

• Myostatin: given that my Design a Gene Challenge was about this, it makes sense to study this gene. I suspect mutations in the gene lead to enhanced or reduced function.
• ACTN3: Determines between “fast” or “slow” muscle fiber twitches. They determine whether one is dispositioned for heavy lifting or endurance.
• IGF1: This gene expresses a growth factor that aids muscle repair and their growtn after exercise. People with different versions might respond differently to certain training styles.

(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’d choose NGS sequencing, as I’m interested in targeted genes. NGS allows for a good middle ground of cost, and accuracy, and also has established protocols for variant detetction.

Also answer the following questions:

Is your method first-, second- or third-generation or other? How so?

Second-generation sequencing. DNA fragments are sequenced simultaniously in massively paralellized fashion.

What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.

With NGS library prep is necessary. First DNA is extracted. Then the DNA is fragmented into smaller chunks (around 300-500 bp). Next adapter ligation is used to attache each DNA chunk to the coded oligonucleotide on the flow plate. PCR afterwards is optional

What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)?

NGS sequencing takes DNA and fragments it, then ligates it to adapter sequences, which add to a sequencing library. The fragments bind to a flow cell, then aplified to create clusters of identical DNA strands. The gene strand is sequenced by synthesizing a complementary strand using labeled nucleotides, and only one base is incorporated per cycle. After incorporation the flow cell is imaged, and the specific fluoresence is used to identify the added base.

What is the output of your chosen sequencing technology?

A FASTQ files, with a large number of small DNA fragment readouts. Additionally these fragements are then aligned and referenced. These can then be screened for differences.

5.2 DNA Write

(i) What DNA would you want to synthesize (e.g., write) and why? These could be individual genes, clusters of genes or genetic circuits, whole genomes, and beyond. As described in class thus far, applications could range from therapeutics and drug discovery (e.g., mRNA vaccines and therapies) to novel biomaterials (e.g. structural proteins), to sensors (e.g., genetic circuits for sensing and responding to inflammation, environmental stimuli, etc.), to art (DNA origamis). If possible, include the specific genetic sequence(s) of what you would like to synthesize! You will have the opportunity to actually have Twist synthesize these DNA constructs! :)

A synthetic DNA construct that can be injected locally, that will express miRNAs that silence myostatin mRNA, to allow for temporary enhanced muscle growth. This would pose a therapeutical intervention, either for biohackers or patients with degenerative muscle diseases.

(ii) What technology or technologies would you use to perform this DNA synthesis and why? Also answer the following questions:

I’d probably choose Phosphoramidite Chemistry, as its the current gold standard and ample for smaller DNA fragments.

What are the essential steps of your chosen sequencing methods? What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?

I’m a bit confused why sequencing instead of synthesis methods are mentioned. So assuming that synthesis is meant, I’ll talk about the synthesis based on Phosphoramidite Chemistry The essential steps with PC is repeating the so called Coupling cycle, that is repeated for each base. First the protecting group gets removed (deetritylation), theen the next phosphoramidite nucleeatide is added (coupling), the unreacted 5’ groups get blocked (cappping), lastly unstable phosphite is converted to stable phosphate (oxidation). This way fragemnts up to 200bp are synthesised and later assembled.

5.3 DNA Edit

(i) What DNA would you want to edit and why? In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why?

Going with the theme a reversible knockout for myostatin expression. It could be an alternative treatment for patients with degenerative muscular diseases as well as biohackers.

(ii) What technology or technologies would you use to perform these DNA edits and why? To ensure reversability, I’d consider base editors, introducing stop codons into the myostatin coding sequence. I’d chose base editors as they make single-nucleotide changes without cutting DNA, to reduce the chance of unwanted off-target effects, compared to classical CRISPR-Cas9 technology. Also answer the following questions:

How does your technology of choice edit DNA? What are the essential steps?

First Guide RNA detects a specific myostatin sequence, then guides the Base editor there. Next a base conversion occures, where e.g. a cytosine base editor converts a C to a T. With this edit normal codons are converted to pre-mature stop codons. This leads to the myostatin being misformed and ideally not bioactive.

What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing?

Select a target sequence to turn a normal codon into a premature stop codon, e.g. CGA to TGA. Next you have to design a Guide RNA to position the base editor at the chosen target. Next check whether there are any off target effects. I’d need the DNA of the base editor, the DNA Template for the guide RNA, an plasmid that integrates the base editor with the guideRNA, restricition and ligation enzymes. For injection into humans, probably also the targeted muscle cells as well as culture media and transfection equipment.

What are the limitations of your editing methods (if any) in terms of efficiency or precision?

Base editing has some key limitation, the most severe one being the low efficiency compared to classical CRISPR methods. Human cells are notoriously difficult to work with, muscle cells are some of the hardest to edit in humans. The delivery of the editor to muscle cells poses another challenge. The base editors have limited target options, based on their capability to make edits in a narrow basepair window. Off target effects are also a concern. Also it needs to be checked, that the editor only comes in contact with the intended C to T conversion as it converts Cs indiscriminantly.

Use of Generative AI

Generative AI was used as a drafting aid throughout the development of this homework assignment. Specifically, it supported the structuring and refinement of complex ideas at the of DNA Design as well as aiding the understanding of DNA Read, Write and Edit technologies. The AI was used to iteratively clarify language and explore alternative framings, while all substantive ideas, judgments, and final decisions were made by the author.

Week 3 HW: Lab Automation

Python Script for Opentrons Artwork

• Review this week’s recitation and this week’s lab for details on the Opentrons and programming it.
• Generate an artistic design using the GUI at opentrons-art.rcdonovan.com. I generated a quick design using the above mentioned tool: BioPunk Initials See the:
• Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons.
  • You may use AI assistance for this coding — Google Gemini is integrated into Colab (see the stylized star bottom center); it will do a good job writing functional Python, while you probably need to take charge of the art concept.
  • If you’re a proficient programmer and you’d rather code something mathematical or algorithmic instead of using your GUI coordinates, you may do that instead.
  • If the Python component is proving too problematic even with AI and human assistance, download the full Python script from the GUI website and submit that:
  • If you use AI to help complete this homework or lab, document how you used AI and which models made contributions.

As its good practice in Software Engineering, not to reinvent the wheel, I had a look at the provided examples, and figured Example 7 would be a good basis for my requirements. Nevertheless, significant updates needed to be done to make the code useful for my purposes. These include:

1. Importing necessary libraries
2. Converting CSV file reading to Pandas Dataframe
3. Removing inversion of y-data
4. Changing the color scheme

  ###
  ### YOUR CODE HERE to create your design
  ###
  # Using nonstandard color setup - will not run on a robot with this lab's standard deck setup.
  well_colors = {
    'B1' : 'Green'
  }

  # Converting coordinates for the design to Opentrons usable dataframe
  bp_coord = [(-19.8,17.6), (-17.6,17.6), (-15.4,17.6), (-13.2,17.6), (-11,17.6), (2.2,17.6), (4.4,17.6), (6.6,17.6), (8.8,17.6), (11,17.6), (13.2,17.6), (-19.8,15.4), (-8.8,15.4), (2.2,15.4), (15.4,15.4), (-19.8,13.2), (-6.6,13.2), (2.2,13.2), (17.6,13.2), (-19.8,11), (-6.6,11), (2.2,11), (17.6,11), (-19.8,8.8), (-6.6,8.8), (2.2,8.8), (17.6,8.8), (-19.8,6.6), (-6.6,6.6), (2.2,6.6), (17.6,6.6), (-19.8,4.4), (-6.6,4.4), (2.2,4.4), (17.6,4.4), (-19.8,2.2), (-6.6,2.2), (2.2,2.2), (17.6,2.2), (-19.8,0), (-8.8,0), (2.2,0), (15.4,0), (-19.8,-2.2), (-17.6,-2.2), (-15.4,-2.2), (-13.2,-2.2), (-11,-2.2), (2.2,-2.2), (4.4,-2.2), (6.6,-2.2), (8.8,-2.2), (11,-2.2), (13.2,-2.2), (-19.8,-4.4), (-8.8,-4.4), (2.2,-4.4), (-19.8,-6.6), (-6.6,-6.6), (2.2,-6.6), (-19.8,-8.8), (-6.6,-8.8), (2.2,-8.8), (-19.8,-11), (-6.6,-11), (2.2,-11), (-19.8,-13.2), (-6.6,-13.2), (2.2,-13.2), (-19.8,-15.4), (-6.6,-15.4), (2.2,-15.4), (-19.8,-17.6), (-8.8,-17.6), (2.2,-17.6), (-19.8,-19.8), (-17.6,-19.8), (-15.4,-19.8), (-13.2,-19.8), (-11,-19.8), (2.2,-19.8)]
  data = pd.DataFrame(bp_coord, columns=["x", "y"])
  data.columns = ["x", "y"]

  #Get min and max x-/y-values from my coordinates
  raw_x_min = np.amin(data['x'])
  raw_x_max = np.amax(data['x'])
  raw_y_min = np.amin(data["y"])
  raw_y_max = np.amax(data["y"])


  #Shift data, so that the centerpoint 0/0 is at the center of my design
  bp_x_shifted = data['x']-((raw_x_min + raw_x_max)/2)
  bp_y_shifted = data["y"]-((raw_y_min + raw_y_max)/2)

  all_distances_to_center = np.sqrt(np.square(bp_x_shifted) + np.square(bp_y_shifted));

  bp_x_85mm_shifted = 40/np.amax(all_distances_to_center)*bp_x_shifted;
  bp_y_85mm_shifted = 40/np.amax(all_distances_to_center)*bp_y_shifted;

  # Get the top-center of the plate, make sure the plate was calibrated before running this
  center_location = agar_plate['A1'].top()
  cell_well = color_plate['B1'] # Change to location of green transformands

  # Aspirate
  pipette_20ul.pick_up_tip()

  for i in range(len(bp_x_85mm_shifted)):
    if i%20 == 0:
      # pick up more every 20 uL, but only as much as we're going to need!
      pipette_20ul.aspirate(min(20, len(bp_x_85mm_shifted)-i), cell_well)

    adjusted_location = center_location.move(types.Point(bp_x_85mm_shifted[i], bp_y_85mm_shifted[i]))
    pipette_20ul.dispense(1, adjusted_location)
    hover_location = adjusted_location.move(types.Point(z = 2))
    pipette_20ul.move_to(hover_location)

  # Don't forget to end with a drop_tip()
  pipette_20ul.drop_tip()

• Submit your Python file via this form.

Post-Lab Questions

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

Given my background in tissue engineering I picked the paper “Fabrication of cell culture hydrogels by robotic liquid handling automation for high-throughput drug testing” published in “Communications Engineering” (2025)4:222 DOI

The paper introduces HYDRA (HYDrogels by Robotic liquid-handling Automation), a method for fabricating flat and thin hydrogel films directly in standard 96- and 384-well HTS plates using an Opentrons liquid handling machine. The paper tackles one of the main problems in modern drug discovery. Around 50% of compounds passing preclinical assays fail human trials. This has several proposed reasons, the paper tackles one of the widely mentioned problems. In standard HTS cells are grown on plastic dishes that do not resemble the ECM structure of the human body, nor have the dynamic context (mechanical, electrical, etc.) that the ECM provides. Organ on a chip improve the missing biomimicry, but are not compatible with HTS. Additionally, they are too complex too manufacture cheaply and are incompatible with automated screening pipelines. There are existing hydrogel coatings, but they either are so thin that they don’t provide the proper ECM environment mechanically, or so thick that they block high-resolution imaging. Both produce a curved meniscus that makes uniform cell seeding impossible.

This gap, hydrogel coatings thin enough for imaging and thick enough for mechanosensing, is tackled with HYDRA. HYDRA uses fish gelatin for its hydrogel base material, dissolved in PBS at 5-20% w/v and is cross-linked enzymatically with transglutaminase at 0.5-2% w/v. The method was demonstrated on an Opentrons, for easy accessibility, for scalability on a INTRGRA Assist Plus. The robot dispenses precalculated sub-contact volumes 96 well plates: 12 micro liter, 382 well plate: 1 micro liter), and immediately re-aspirates the volume, to archive a thin film. This is possible by using contact angle hysteresis. The volume was optimized using FE simulations. The process takes around 10 minutes for the whole plate, then the plates are gelled, and incubated at 37 degree Celsius, sterilized and swollen and rinsed in PBS. Afterwards cells can be seeded. The hydrogel thin films are between 10-50 micrometer thickness, with tunable stiffness. Lastly the authors validated several imaging platforms (digital holographic, widefield fluorescence, and high-resolution confocal microscopy) see Fig. 1

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.

Developmental biology aims to understand how tissues and cells develop from dynamic, history-dependent processes governed by interacting biochemical, electrical, mechanical cues in a temporal manner. While these properties are well established theoretically, in practice experiments lack behind by what can be executed manually. Even if automation is deployed it only addresses one of the drawbacks of manual experimentation. This forced experiments to make simplifications, such as searching only low-parameter search spaces, coarse temporal guiding and open loop design of experiments. This results in heuristic sampling and interpretation of results with lacking search spaces. Using automation directly addresses these limitations by enabling precise temporal control, systematic exploration of parameter rich search spaces, and adaptive, feedback-driven intervention. This is especially relevant in organoid and organ-on-chip systems.

Mimicking the ECM in developmental biology

Many developmental signals only have their proper effect through the timing, duration, and frequency, rather than concentration alone. Temporal regimes are rarely explored experimentally, e.g. competence windows, pulsed signaling, and oscillatory dynamics due to the impracticality of executing. Automated liquid-handling systems can change that by enabling stable, repeatable temporal execution.

Exploring the design space

Cell fate and tissue morphology have very high-dimensional development search spaces. This comes from the interaction of multiple biochemical and mechanical variables that evolve over time. Contrasting this intuition or slow sequential tuning is currently used. Automation enables systematic, multi-parameter exploration of said search space. The goal of automation is to find non-linear responses and regime changes that are not obvious in manual experiments.

Guiding Experiments

Development proceeds through continuous feedback between tissue state and signaling environment, yet most experiments are designed in an open-end fashion, with data analysis and redesign being manual and in a discrete fashion. Automation should allow experiments to close the loop, where readouts inform the next steps of the experiment. The goal is to adaptively explore tissue formation.

Final Project Ideas

As explained in this week’s recitation, add 1-3 slides in your Node’s section of this slide deck with 3 ideas you have for an Individual Final Project. Be sure to put your name, city, and country on your slide!

• slides are added

Use of Generative AI

Generative AI was used as a conceptual drafting aid during the development of this project. Specifically, it supported the structuring and refinement of complex ideas related to automated biological experimentation and experimental design in tissue engineering, as well as clarifying concepts around temporal control, high-dimensional experimentation, and reproducibility in laboratory automation. The AI was used to iteratively improve clarity of language and to explore alternative conceptual framings. No generative AI was used for the implementation or coding components of the project, and all substantive ideas, technical decisions, and final judgments were made by the author.

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project