Week 1 homework

Principles and practices 💼

1. First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about.

Purification of enzymes for natural pigment synthesis facilitated by microalgal cell wall release

In recent decades, microalgae have emerged as promising platforms for the sustainable biosynthesis of various high-value compounds ^{1 2}, however, during their purification, challenges can arise. This project aims to propose a method of metabolite purification from microalgal cultures by tapping into a largely overlooked resource of microalgal cells of various species, namely their cell wall. Purification of proteins of interest could be carried out by fusing them to elements of the microalgal cell wall and then harvesting the shedded cell walls following nitrogen starvation-induced sexual reproduction, as this method is inspired by the ecdysis of insects. Theoretically, this method of purification could be implemented for any fused proteins, but here, for a more tangible example stemming from an interest in the production of eco-friendly ink, the idea described below will focus on the synthesis of indigoidine synthetase, the primary enzyme implicated in the generation of the indigo dye.

The organism chosen for the purposes of this project is the model green microalga Chlamydomonas reinhardtii, as its physiology and metabolism have been extensively studied ^{3 4 5}, while multiple tools have already been developed and established for its genetic manipulation ^{6 7 8}. In more detail, the first step of this process should be to engineer C. reinhardtii ^{9 10} to overexpress pherophorin (microlgal cell wall protein)-indigoidine synthetase fusion proteins (Figure 1.1A). Subsequently, gamete generation can be induced by depleting the nitrogen in the growth medium, leading to mating of haploid microalgal cells followed by shedding of their cell walls (Figure 1.1B). The rejected cell wall components can then be isolated by sucrose-gradient centrifugation (Figure 1.1C), before being subjected to enzymatic processing both for polysaccharide degradation and for separation of indigoidine synthetase molecules from pherophorins (Figure 1.1D). Lastly, affinity chromatography can be employed for further purification of the synthesized indigoidine synthetase units (Figure 1.1E), which can afterwards be screened in an activity assay (Figure 1.1F) to monitor the enzymatic conversion of L-glutamate into indigoidine (indigo dye).

Figure 1.1 Schematic overview of cell wall release-based purification of indigoidine synthetase. Figure modified from Sekimoto, 2017 ¹¹ and partially created on BioRender.com.

2. 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). Break big goals down into two or more specific sub-goals. Below is one example framework (developed in the context of synthetic genomics) you can choose to use or adapt, or you can develop your own. The example was developed to consider policy goals of ensuring safety and security, alongside other goals, like promoting constructive uses, but you could propose other goals for example, those relating to equity or autonomy.

The primary policy behind this project is sustainability, as microalgae require minimal resources to efficiently synthesize numerous valuable compounds utilized in human food and animal feed, in pharmaceuticals and cosmetics, and even in the energy sector with the production of biofuels through photosynthesis-driven carbon sequestration. Besides their role as promising green cell factories and a potential carbon sink, microalgae, and in turn projects revolving around them, contribute to sustainability by offering great spatial autonomy in terms of their cultivation, as they are not confined by the availability of arable land or freshwater-based irrigation. From a performance aspect, microalgae have been advocated for their high photosynthetic capacity too, enabling them to generate biomass more efficiently than most crops. Taking all the aforementioned benefits into consideration, another principle emerges, as microalgal cultivation gives equal opportunities for development both to established facilities, but most importantly, to low-income communities and developing countries, including small island nations. Culturing of microalgae merely requires exposing them to light and letting them fixate atmospheric inorganic carbon and has become even more affordable with the introduction of the plastic tubular photobioreactor ¹², which also allows for the exploitation of otherwise unutilized vertical space.

3. Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Try to outline a mix of actions (e.g. a new requirement/rule, incentive, or technical strategy) pursued by different “actors” (e.g. academic researchers, companies, federal regulators, law enforcement, etc). Draw upon your existing knowledge and a little additional digging, and feel free to use analogies to other domains (e.g. 3D printing, drones, financial systems, etc.).

• Purpose: What is done now and what changes are you proposing?
• Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc)
• Assumptions: What could you have wrong (incorrect assumptions, uncertainties)?
• Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

Option 1. Initiative to establish a teaching module on biosafety and biosecurity in high school Biology classes, with lectures from Biotechnology experts and academics of the local university or from iGEM teams that are active in the area. Raising awareness for biosafety and biosecurity issues as early as in high school years will ensure that future generations of researchers have the necessary stimuli to pursue good scientific practices. This initiative can also help high school students gain an overview both of the academic research being concucted at local universities, along with the regulatory frameworks defining that research, especially when that happens through the lens of an aspiring young scientist closer to their age, such as a university student member of a local iGEM team. In case of a smaller town or village without a university, lectures from invited experts coming from larger cities could be arranged, perhaps with the help and support of the Ministry of Education, which could even twin schools in rural areas with metropolitan universities.

Option 2. Enforcement of regular inspections from EU representatives to ensure that Biotechnology-focused academic, research, and industrial facilities adhere to imposed regulations. Surprisingly, there is no official EU institution, board, or committee primarily devoted to matters of biosafety and biosecurity, so pushing for the establishment of one should be a priority at this stage. In any case, laws that require regular inspections of biotechnological facilities even by experts appointed by the local government should be imposed. Close monitoring of academic and industrial facilities to verify that, for instance, HEPA filters are renewed and biohazardous waste is handled appropriately, is paramount and heavy fines should be issued in case of non-compliance.

Option 3. Increased funding of programs dedicated to designing and integrating novel kill-switches and other biocontainment mechanisms to expand the arsenal of available strategies both for a wider range of projects/conditions but also for a wider range of genetically modified organisms. This could be an action to promote innovative research in the field of biosecurity. By diverting resources or even creating new programs to fund research into novel biocontainment approaches at an EU (for example, the Marie-Skłodowska-Curie grants) or at a national level, more scientists could be encouraged to engage in discovering or devising cutting-edge kill-switch mechanisms ^{13 14 15 16} (Figure 1.2B), genetic safeguards or firewalls for preventing horizontal gene transfer, and auxotrophic strains ¹⁷ (Figure 1.2A). Hopefully, such an initiative could contribute to expanding the research and use of biocontainment not only to bacteria and yeast, for which the field of biosecurity is already established to a significant degree, but also to less widespread biotechnological hosts, such as microalgae, whose kill-switch and auxotrophy strategies are currently far less advanced.

Figure 1.2 Proposed biocontainment strategies for genetically engineered microalgae. (A) A promising biocontainment method involves the developement of auxotrophic strains, such as microalgae that can solely survive on a non-standard source of P, for example, phosphite ¹⁷. (B) Kill-switch mechanism employing a light-controlled riboregulator. The main premise behind this circuit is that genetically engineered microalgae are primarily cultured in well-illuminated ponds. In this case, the light-inducible pLIP promoter from Dunaliella sp. ¹³ drives the expression of a trigger RNA molecule (trRNA) that, through binding to a three-way junction riboregulator ^{14 15} at the 5’ UTR of NucA nuclease’s coding sequence, suppresses the gene’s expression. However, if the engineered microalgae accidentally end up in an underground aquifer or inside another organism after ingestion, the dark conditions will reveal an integrated internal ribosome entry site (IRES), enabling the nuclease’s expression ¹⁶ and, effectively, exterminating the microalgal cell. Figure from Motomura et al., 2018 ¹⁷ and partially created on BioRender.com.

4. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

5. 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. For this, you can choose one or more relevant audiences for your recommendation, which could range from the very local (e.g. to MIT leadership or Cambridge Mayoral Office) to the national (e.g. to President Biden or the head of a Federal Agency) to the international (e.g. to the United Nations Office of the Secretary-General, or the leadership of a multinational firm or industry consortia). These could also be one of the “actor” groups in your matrix. Reflecting on what you learned and did in class this week, outline any ethical concerns that arose, especially any that were new to you. Then propose any governance actions you think might be appropriate to address those issues. This should be included on your class page for this week.

Among the three actions listed above, the least feasible one would be the enforcement of regular inspections by an EU board devoted to addressing biosafety and biosecurity issues. Since such an institution does not exist right now, its establishment would require time and a large bureaucratic, as well as administrative and legislative mobilization, implicating several bodies of the EU, including the European Council and the European Commission.

On the other hand, the other two initiatives, namely integrating modules about biosafety and biosecurity in high school curricula and increasing funding for research on biocontainment practices, appear more feasible and promising in the short term and should, therefore, be prioritized. The basic rationale behind this prioritization is that, judging from personal experience, the educational community, including secondary and higher education, has always been more open-minded and welcoming towards new initiatives compared to legislative bodies and political agents. As educational institutions, regardless of level, have always been united by ideals such as service to humanity and a strong drive towards innovation and intellectual progress, it is generally much easier and faster to put internal actions for institutional twinning and raising awareness about biosafety and biosecurity in motion, mostly concerning the first option. Another benefit of prioritizing the initiative presented in the first option would be providing the opportunity to promote other principles alongside biosafety and biosecurity to younger audiences too, for instance, sustainability and equity as previously mentioned. Regarding the third option, increasing funding for relevant strategies internally appears more feasible as well, since in many cases, research groups and laboratories are given substantial flexibility in terms of allocating resources to individual projects from national grants that do not specify a particular objective. In the long run, both options one and three could have a significantly positive influence in establising more fundamental and impactful governance policies in the future, as they incorporate a more “bottom-up” approach, where members of the general public that have become more aware of the aforementioned principles, school students, university students, and academics, can start building an initial framework. This will lay the foundation for more radical reforms, as more citizens become aware of biosafety, sustainability, and equity issues and push (through voting and other sociopolitical manifestations) for more generalized changes and a renewal of governance policies.

Preparation for week 2 lecture 🔎

Homework Questions from Prof. Jacobson

1. 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?

The primary polymerases for nuclear genome replication in human cells are DNA polymerases δ (delta) and ε (epsilon) ^{18 19 20}. Purely based on their replicase activity, they display an error rate of 10^-4 to 10^-5 per base pair per replication cycle. Given that the entire haploid human genome spans approximately 3.3×10⁹bp, it can be calculated that, in every replication cycle, (3.3×10⁹bp) × 10^-4 = 3.3×10⁵bp can be erroneous in a worst-case scenario.

However, DNA polymerases δ and ε have evolved to have a 3’ to 5’ proofreading exonuclease activity, meaning that they can recognize and correct misplaced nucleotides based on strand complementarity by “going in reverse”, excising, and replacing them ^{18 19 20}. It has been shown that, due to their proofreading capacity, those polymerases exhibit extremely low error rates, estimated at less than 10^-9 per base pair per replication cycle ²⁰, which, according to the calculation above, can be translated into approximately 3.3 errors for every replication. Except for the polymerases’ proofreading capacity, cells also possess mismatch repair (MMR) mechanisms that correct mistakes in DNA replication missed by the polymerases ²⁰. In case all the previously mentioned systems fail, there are cell cycle checkpoints in place, which will not allow cellular division to proceed if mistakes in DNA replication persist.

Another factor that contributes to a lower rate of mutations in the “final product” of the genome, namely the proteins, is that protein-coding genes constitute about 1% of the entire genome, as they are “diluted” due to what has been called “junk DNA”. Because of this “dilution” effect, it seems far more likely that, even if an error occurs, it will be located in a non-coding region of the genome. Although this could influence the regulation of expression, it will not affect the final amino acid sequence of the protein of interest. To this end, namely the preservation of a protein’s primary structure, the genetic code has also evolved to be degenerate. The degeneracy of the genetic code, meaning the redundancy where multiple, distinct codons (nucleotide triplet combinations) encode for the same single amino acid, allows for increased flexibility in protein expression. Therefore, even if an error occurs within a coding region, the mutated codon can still be translated into the correct amino acid. Finally, as a fail-safe on the protein level, in case a replication error results in a mutated codon that corresponds to a different amino acid, the tertiary structure, as well as the functionality of the final protein, could still be preserved, if the altered amino acid demonstrates the same or very similar biochemical properties as the original one (for instance, aspartic acid and glutamic acid).

2. 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 do not work to code for the protein of interest?

Each of the 20 standard amino acids can be represented by one to six different codons, with a three-codon representation in the genetic code on average. The coding sequence for an average human protein is 1,036bp long, meaning that an average human protein consists of approximately 1,036 / 3 = 345 amino acids. Based on this information, an average human protein could be translated from about 3³⁴⁵ = 4×10¹⁶⁴ different codon combinations.

Although this is a valid theoretical assumption, in reality, every organism, including humans, does not show such flexibility in the coding sequences of its genome due to codon biases, as well as the genome’s GC content. Codon biases mostly relate to the actual availability of aminoacyl-tRNA synthetases and aminoacyl-tRNAs corresponding to every amino acid inside the cell, as aminoacyl-tRNAs are heavily involved in protein synthesis and their availability substantially affects the rate and the eventual success of the overall process. Specific codon usage databases (also known as “Kazusa tables”) have been composed based on studies of different organisms’ codon usages and biases ^{21 22}. When synthesizing an artificial coding sequence for the expression of a human protein, the GC content of the genome needs to be taken into consideration too, as large variations can lead to silencing effects. Another factor that can drastically diminish the number of functional combinations for a protein’s coding sequence are the respective mRNA’s thermodynamic properties. Different combinations of codons, hence nucleotidic triplets, can facilitate the formation of secondary structures, such as hairpins, in a transcript, which can delay or even hinder translation. The selected codon sequence should, therefore, produce an mRNA with the appropriate thermodynamic profile for optimal protein expression. Finally, more limitations can be posed by the synthesis method in case the coding sequence has to be artificially assembled, as DNA molecules with low GC content and a low percentage of repeated sequences are preferred. Additionally, if the artificially synthesized gene also needs to contain introns that stabilize or enhance transcription, selection of a codon combination should ensure the presence of positions where introns can be inserted, for instance, GGs at determined intervals or spots.

Homework Questions from Dr. LeProust

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

Currently, the most established and widely used method for oligonucleotide synthesis is the solid-phase phosphoramidite (S-PP) method. This constitutes a type of chemical synthesis that utilizes a solid-phase material, typically controlled pore glass or microporous polystyrene, as a platform where oligonucleotides are added in a 3’ to 5’ direction. S-PP synthesis can be highly automated, enabling high-throughput production of oligonucleotides in 96- and 384-well plates.

The basic principle behind S-PP synthesis can be summarized as exposing biochemical groups that should react while simultaneously protecting groups that should not react. To this end, specifically modified nucleotides, called phosphoramidite monomers, which have their reactive groups “concealed”, need to be used. In particular, to prevent the reactive groups of the monomers from forming undesirable bonds during the process, their nitrogenous bases are protected by a benzoyl or isobutyryl moiety (Figure 1.3A, pink), their 3’-OH by a 2-cyanoethyl-diisopropylamino moiety (Figure 1.3A, purple, orange, and yellow), and their 5’-OH by a dimethoxytrityl (DMT) moiety (Figure 1.3A, green). In the case of RNA oligonucleotide synthesis, the highly reactive 2’-OH has to be “concealed” by a ter-butyldimethylsilyl moiety as well.

In more detail, the method consists of a cyclic four-step process that allows the assembly of the oligonucleotide chain by starting with a first monomer that has already been covalently attached to the synthesis platform and then elongating it one nucleotide at a time (Figure 1.3B):

• Step 1. Detritilation The 5′-DMT protecting group is removed by lowering the pH of the reaction, thus exposing the 5′-OH.
• Step 2. Coupling The incoming phosphoramidite monomer is added in a substantially high concentration and its phosphoramidite moiety “attacks” the now exposed 5′-OH (usually in the presence of an azole catalyst) to form a phosphite triester bond, ultimately linking the new monomer to the growing oligonucleotide. During this step, the diisopropylamino group is cleaved as well.
• Step 3. Capping Unreacted 5′-OH groups are capped through acetylation to avoid the formation of oligonucleotides with the wrong sequence.
• Step 4. Oxidation The unstable phosphite triester is oxidized into a stable phosphate triester bond.

Once the last cycle of the process is completed, the synthesized oligonucleotide is excised from the solid-phase platform by the implementation of alkaline conditions. This increase in pH will also cause the detachment of the 2-cyanoethyl groups, as well as of the nitrogenous bases’ protective moieties, from all the building blocks of the nucleotide, essentially rendering the newly synthesized oligonucleotide biochemically suitable for downstream applications ^{23 24 25}.

Figure 1.3 (A) Representation of a phosphoramidite monomer. (B) Schematic overview of solid-phase phosphoramidite synthesis. Figure from Twist Bioscience phosphoramidite chemistry and BOC Sciences S-PP cycle.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis? Why can 2000bp genes not be made via direct oligo synthesis?

Generating oligonucleotides via direct chemical synthesis, such as the S-PP method, presents challenges due to
cumulative inefficiencies in the building process. These inefficiencies ultimately lead to a drastic decrease in the yield and purity of the final product as, for several factors analyzed below, the latter drop exponentially with the increasing length of the desired oligonucleotide.

One such factor appears to be cumulative yield loss. While S-PP synthesis is highly efficient (>99% per step), this discrepancy from 100% increases exponentially as the chain grows. Given that the addition of every new phosphoramidite monomer probabilistically constitutes an independent event from previous additions and by assuming a coupling efficiency of 99%, the yield of a 100-mer is approximately 0.99¹⁰⁰ = 36.6%, while the yield of a 200nt oligonucleotide amounts to an even lower percentage, 0.99²⁰⁰ = 13.4%. Accordingly, the yield of a 2,000nt sequence is practically 0, since 0.99^2,000 = 18.6×10^-9%.

Another shortcoming stems from the accumulation of truncated sequences, which can occur at any stage of the assembly due to incomplete coupling. Those shorter oligonucleotides (also called “failure sequences”) accumulate as impurities in the pool of S-PP synthesized oligonucleotides. As the elongation advances, the length difference between the desired full-length product and the truncated oligonucleotide becomes negligible. For instance, the two nucleotides that differentiate a truncated 198-mer from the desired 200-mer correspond to a 1% discrepancy, however, in the case of a 1,198nt truncated oligonucleotide and the desired 2,000-mer, the discrepancy is 0.1%. Due to resolution constraints of high-performance liquid chromatography (HPLC) or polyacrylamide gel electrophoresis (PAGE), which are employed to separate the desired products from truncated entities, isolating the desired oligonucleotide (especially in the case of the 2,000-mer) proves to be extremely difficult, negatively impacting the purity of the final product.

Lastly, the physical properties of the support platform, as well as the biochemistry of the oligonucleotides themselves, can post limitations to the synthesis, which intensify with the increasing length of the desired oligonucleotide. More specifically, when CPG beads are used as a support matrix, their pores can become clogged as the oligonucleotide chain grows longer, ultimately hindering the diffusion of reagents to the reactive 5’-OH group and reducing coupling efficiency. Similarly, repeated cycles of detritilation under acidic conditions can induce depurination (chemical degradation and loss of purine bases). Depurination can accumulate over 200 cycles and even reach concerningly high levels at 2,000 cycles, leading to damaged, fragmented, or incorrect sequences.

Homework Questions from Prof. Church

3. Given the one paragraph abstracts for these real 2026 grant programs sketch a response to one of them or devise one of your own:

• BioStabilization Systems
• Smart-RBC
• Generative Optogenetics

For the generative optogenetics program, an approach could be to design and express a light-activated polymerase that has a “pinwheel” structure/shape and works the way a danish Christmas star is folded/woven (Figure 1.4A,B, and C). More specifically, the polymerase would have four arms (one for each of the standard DNA nucleotides) that are activated by a specific wavelength. For the light activation element, domains from different opsins can be incorporated into the molecule, which would cause conformational changes in the appropriate arm resulting in its bending towards the central cavity where the nascent oligonucleotide is anchored (Figure 1.4D). Recruitment of the right nucleotide could be facilitated by selective affinity through specific amino acid interactions, while each arm should retain the properties of a polymerase to also form phosphodiester bonds. For an initial DNA fragment, which is normally needed for enzymatic DNA amplification, a poly-A tail (such as the one mature eukaryotic trnascripts have attached in their 3’ end) could be employed, which can be cleaved after synthesis of the desired oligonucletide is completed. This approach can, theoretically, be easily automated and scaled up by utilizing a 96-well Light Plate Apparatus (LPA) or it can be enhanced with the addition of more arms to integrate non-standard nucleotides as well, such as pseudouridine and inosine, for different applications. ¹

Figure 1.4 (A) and (B) Photographs of folding/weaving a danish Christmas star, which can be seen finished in (C). (D) Schematic representation of the “pinwheel” polymerase, whose arms bend and add a particular nucleotide upon activation with a specific wavelength. Figure from How to make a danish Christmas star, the Bureau of Betterment, and partially created on BioRender.com.

Week 2 homework

DNA read, write, and edit 🧬

Part 1: Benchling and in-silico gel art

The genome of the λ-phage was imported and virtually digested with the following restriction endonucleases: EcoRI, HindIII, BamHI, KpnI, EcoRV, SacI, and SalI before being visualized on Benchling’s agarose gel simulator (Figure 2.1).

Figure 2.1 Virtual digest of λ-phage’s DNA treated with seven different restriction enzymes (as indicated by the gel lane legend on the top left). Figure created on Benchling.com.

Part 3: DNA design challenge

3.1. Choose your protein. In recitation, we discussed that you will pick a protein for your homework that you find interesting. Which protein have you chosen and why? Using one of the tools described in recitation (NCBI, UniProt, Google), obtain the protein sequence for the protein you chose.

The entire medical establishment relies heavily on a sea creature so ancient and resilient that it has barely changed since its ancestral form first appeared in the tree of life when Planet Earth had its own ring system (like Saturn and Uranus), approximately 450 million years ago. Besides a hardy armour-like exoskeleton and a bizarre body plan, horseshoe crabs bring to the table a primitive, yet extremely functional, immune system tightly-packed in their blue-colored blood. A key component of their immune response are the granular amoebocytes found in their blood, which, upon contact with a bacterial endotoxin, initiate a coagulation cascade that protects the horseshoe crab by sequestering and neutralizing the harmful agent. This very property of horseshoe crab immunity has been harnessed by numerous medical corporations since the 1960s-1970s as an effective method to safely screen vaccines, other injectable pharmaceuticals, as well as implantable biomedical devices, for the presence of bacteria-derived toxins ¹.

Based on this premise, an interesting idea to pursue would be to perform whole-cell engineering of bacteria or, preferably, yeast cells to render them functionally similar to the granular amoebocytes contained in horseshoe crab blood. This endeavor, primarily inspired by an old iGEM project aiming to convert Escherichia coli bacteria into red blood cells ², could contribute to the conservation of the fragile ecosystem to which horseshoe crabs belong, but also drastically confine the invasive, time-consuming, and expensive practice of harvesting horseshoe crab haemolymph ³.

To this end, a critical step is to transform the cells-to-be-engineered with the gene coding for factor C, namely the main protein that initiates the immune response and triggers the coagulation pathway ^{4 5}. The amino acid sequence for the factor C protein of the mangrove horseshoe crab Carcinoscorpius rotundicauda (CrFactor C) was retrieved from UniProt under the accession number “Q26422” ^{4 6}:

>sp|Q26422|LFC_CARRO Limulus clotting factor C OS=Carcinoscorpius rotundicauda OX=6848 PE=2 SV=1 MVLASFLVSGLVLGLLAQKMRPVQSKGVDLGLCDETRFECKCGDPGYVFNIPVKQCTYFY RWRPYCKPCDDLEAKDICPKYKRCQECKAGLDSCVTCPPNKYGTWCSGECQCKNGGICDQ RTGACACRDRYEGVHCEILKGCPLLPSDSQVQEVRNPPDNPQTIDYSCSPGFKLKGMARI SCLPNGQWSNFPPKCIRECAMVSSPEHGKVNALSGDMIEGATLRFSCDSPYYLIGQETLT CQGNGQWNGQIPQCKNLVFCPDLDPVNHAEHKVKIGVEQKYGQFPQGTEVTYTCSGNYFL MGFDTLKCNPDGSWSGSQPSCVKVADREVDCDSKAVDFLDDVGEPVRIHCPAGCSLTAGT VWGTAIYHELSSVCRAAIHAGKLPNSGGAVHVVNNGPYSDFLGSDLNGIKSEELKSLARS FRFDYVRSSTAGKSGCPDGWFEVDENCVYVTSKQRAWERAQGVCTNMAARLAVLDKDVIP NSLTETLRGKGLTTTWIGLHRLDAEKPFIWELMDRSNVVLNDNLTFWASGEPGNETNCVY MDIQDQLQSVWKTKSCFQPSSFACMMDLSDRNKAKCDDPGSLENGHATLHGQSIDGFYAG SSIRYSCEVLHYLSGTETVTCTTNGTWSAPKPRCIKVITCQNPPVPSYGSVEIKPPSRTN SISRVGSPFLRLPRLPLPLARAAKPPPKPRSSQPSTVDLASKVKLPEGHYRVGSRAIYTC ESRYYELLGSQGRRCDSNGNWSGRPASCIPVCGRSDSPRSPFIWNGNSTEIGQWPWQAGI SRWLADHNMWFLQCGGSLLNEKWIVTAAHCVTYSATAEIIDPNQFKMYLGKYYRDDSRDD DYVQVREALEIHVNPNYDPGNLNFDIALIQLKTPVTLTTRVQPICLPTDITTREHLKEGT LAVVTGWGLNENNTYSETIQQAVLPVVAASTCEEGYKEADLPLTVTENMFCAGYKKGRYD ACSGDSGGPLVFADDSRTERRWVLEGIVSWGSPSGCGKANQYGGFTKVNVFLSWIRQFI

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.

The nucleotide coding sequence for CrFactor C was extracted from the respective NCBI entry ⁷:

>S77063.1 factor C=endotoxin-sensitive intracellular serine protease zymogen {clone CrFC21} [Carcinoscorpius rotundicauda=Singapore horseshoe crabs, blood, amoebocytes, CDS, 3060 nt] ATGGTCTTAGCGTCGTTTTTGGTGTCTGGTTTAGTTCTAGGGCTACTAGCCCAAAAAATGCGCCCAGTTCAGTCCAAAGGAGTAGATCTAGGCTTGTGTGATGAAACGAGGTTCGAGTGTAAGTGTGGCGATCCAGGCTATGTGTTCAACATTCCAGTGAAACAATGTACATACTTTTATCGATGGAGGCCGTATTGTAAACCATGTGATGACCTGGAGGCTAAGGATATTTGTCCAAAGTACAAACGATGTCAAGAGTGTAAGGCTGGTCTTGATAGTTGTGTTACTTGTCCACCTAACAAATATGGTACTTGGTGTAGCGGTGAATGTCAGTGTAAGAATGGAGGTATCTGTGACCAGAGGACAGGAGCTTGTGCATGTCGTGACAGATATGAAGGGGTGCACTGTGAAATTCTCAAAGGTTGTCCTCTTCTTCCATCGGATTCTCAGGTTCAGGAAGTCAGAAATCCACCAGATAATCCCCAAACTATTGACTACAGCTGTTCACCAGGGTTCAAGCTTAAGGGTATGGCACGAATTAGCTGTCTCCCAAATGGACAGTGGAGTAACTTTCCACCCAAATGTATTCGAGAATGTGCCATGGTTTCATCTCCAGAACATGGGAAAGTGAATGCTCTTAGTGGTGATATGATAGAAGGGGCTACTTTACGGTTCTCATGTGATAGTCCCTACTACTTGATTGGTCAAGAAACATTAACCTGTCAGGGTAATGGTCAGTGGAATGGACAGATACCACAATGTAAGAACTTGGTCTTCTGTCCTGACCTGGATCCTGTAAACCATGCTGAACACAAGGTTAAAATTGGTGTGGAACAAAAATATGGTCAGTTTCCTCAAGGCACTGAAGTGACCTATACGTGTTCGGGTAACTACTTCTTGATGGGTTTTGACACCTTAAAATGTAACCCTGATGGGTCTTGGTCAGGATCACAGCCATCCTGTGTTAAAGTGGCAGACAGAGAGGTCGACTGTGACAGTAAAGCTGTAGACTTCTTGGATGATGTTGGTGAACCTGTCAGGATCCACTGTCCTGCTGGCTGTTCTTTGACAGCTGGTACTGTGTGGGGTACAGCCATATACCATGAACTTTCCTCAGTGTGTCGTGCAGCCATCCATGCTGGCAAGCTTCCAAACTCTGGAGGAGCGGTGCATGTTGTGAACAATGGCCCCTACTCGGACTTTCTGGGTAGTGACCTGAATGGGATAAAATCGGAAGAGTTGAAGTCTCTTGCCCGGAGTTTCCGATTCGATTATGTCCGTTCCTCCACAGCAGGTAAATCAGGATGTCCTGATGGATGGTTTGAGGTAGACGAGAACTGTGTGTACGTTACATCAAAACAGAGAGCCTGGGAAAGAGCTCAAGGTGTGTGTACCAATATGGCTGCTCGTCTTGCTGTGCTGGACAAAGATGTAATTCCAAATTCGTTGACTGAGACTCTACGAGGGAAAGGGTTAACAACCACGTGGATAGGATTGCACAGACTAGATGCTGAGAAGCCCTTTATTTGGGAGTTAATGGATCGTAGTAATGTGGTTCTGAATGATAACCTAACATTCTGGGCCTCTGGCGAACCTGGAAATGAAACTAACTGTGTATATATGGACATCCAAGATCAGTTGCAGTCTGTGTGGAAAACCAAGTCATGTTTTCAGCCCTCAAGTTTTGCTTGCATGATGGATCTGTCAGACAGAAATAAAGCCAAATGCGATGATCCTGGATCACTGGAAAATGGACACGCCACACTTCATGGACAAAGTATTGATGGGTTCTATGCTGGTTCTTCTATAAGGTACAGCTGTGAGGTTCTCCACTACCTCAGTGGAACTGAAACCGTAACTTGTACAACAAATGGCACATGGAGTGCTCCTAAACCTCGATGTATCAAAGTCATCACCTGCCAAAACCCCCCTGTACCATCATATGGTTCTGTGGAAATCAAACCCCCAAGTCGGACAAACTCGATAAGTCGTGTTGGGTCACCTTTCTTGAGGTTGCCACGGTTACCCCTCCCATTAGCTAGAGCAGCCAAACCTCCTCCAAAACCTAGATCCTCACAACCCTCTACTGTGGACTTGGCTTCTAAAGTTAAACTACCTGAAGGTCATTACCGGGTAGGGTCTCGAGCCATCTACACGTGCGAGTCGAGATACTACGAACTACTTGGATCTCAAGGCAGAAGATGTGACTCTAATGGAAACTGGAGTGGTCGGCCAGCGAGCTGTATTCCAGTTTGTGGACGGTCAGACTCTCCTCGTTCTCCTTTTATCTGGAATGGGAATTCTACAGAAATAGGTCAGTGGCCGTGGCAGGCAGGAATCTCTAGATGGCTTGCAGACCACAATATGTGGTTTCTCCAGTGTGGAGGATCTCTATTGAATGAGAAATGGATCGTCACTGCTGCCCACTGTGTCACCTACTCTGCTACTGCTGAGATTATTGACCCCAATCAGTTTAAAATGTATCTGGGCAAGTACTACCGTGATGACAGTAGAGACGATGACTATGTACAAGTAAGAGAGGCTCTTGAGATCCACGTGAATCCTAACTACGACCCCGGCAATCTCAACTTTGACATAGCCCTAATTCAACTGAAAACTCCTGTTACTTTGACAACACGAGTCCAACCAATCTGTCTGCCTACTGACATCACAACAAGAGAACACTTGAAGGAGGGAACATTAGCAGTGGTGACAGGTTGGGGTTTGAATGAAAACAACACCTATTCAGAGACGATTCAACAAGCTGTGCTACCTGTTGTTGCAGCCAGCACCTGTGAAGAGGGGTACAAGGAAGCAGACTTACCACTGACAGTAACAGAGAACATGTTCTGTGCAGGTTACAAGAAGGGACGTTATGATGCCTGCAGTGGGGACAGTGGAGGACCTTTAGTGTTTGCTGATGATTCCCGTACCGAAAGGCGGTGGGTCTTGGAAGGGATTGTCAGCTGGGGCAGTCCCAGTGGATGTGGCAAGGCGAACCAGTACGGGGGCTTCACTAAAGTTAACGTTTTCCTGTCATGGATTAGGCAGTTCATTTGA

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?

Different organisms display different codon biases regarding protein translation ^{8 9}. Codon bias and codon usage are predominantly determined by the relative abundance of aminoacyl-tRNAs and aminoacyl-tRNA synthetases inside a cell, as they both constitute crucial components of the translational machinery, carrying the proteinogenic amino acids and loading the aminoacyl-tRNAs with the proper amino acid respectively. This codon bias has to be taken into account when transforming an organism with a gene from another organism to modify the coding sequence accordingly, enabled by the degeneracy of the genetic code, and render it compatible with the host cell’s translational machinery, thus ensuring smooth heterologous expression of the protein of interest. If the gene of interest is not codon optimized for the expression host, it is likely that the protein will be synthesized at very low levels or not at all.

Similarly, when choosing an expression host for synthesizing the protein of interest, several parameters have to be considered as well. In this specific case, factor C is a protein derived from a eukaryote, has a complex molecular structure involving disulfide bonds, and is glycosylated at several amino acid positions. For the expression of a protein with those characteristics, a putative host should also be a eukaryote, as eukaryotic cells harbor the necessary biochemical pathways for protein post-translational modifications, such as glycosylation, and, additionally, should have an oxidizing intracellular environment to facilitate the formation of disulfide bridges. A promising candidate that fulfils all those criteria is the methylotrophic yeast Pichia pastoris, for which the original coding sequence for factor C has been codon optimized employing Benchling’s codon optimization tool:

>factor C=endotoxin-sensitive intracellular serine protease zymogen {clone CrFC21} [codon optimized for Pichia pastoris, CDS, 3060 nt] ATGGTCTTAGCGTCGTTTTTGGTTTCTGGTTTAGTTCTAGGGCTACTAGCCCAAAAAATGCGCCCAGTTCAGTCCAAAGGAGTAGATCTAGGCTTGTGTGATGAAACGAGGTTCGAGTGTAAGTGTGGCGATCCAGGCTATGTTTTCAACATTCCAGTCAAACAATGCACATACTTTTATCGATGGAGGCCGTATTGTAAACCATGTGATGACCTGGAGGCTAAGGATATTTGTCCAAAGTACAAGCGATGTCAAGAGTGTAAGGCTGGTCTTGATAGTTGTGTTACTTGTCCACCTAACAAGTATGGTACTTGGTGTAGCGGTGAATGTCAGTGCAAGAACGGAGGTATCTGTGACCAGAGGACAGGAGCTTGTGCATGTCGTGACAGATATGAAGGGGTGCACTGCGAAATTCTCAAAGGTTGTCCTCTTCTTCCATCGGATTCTCAGGTTCAAGAAGTCAGAAATCCACCAGATAATCCCCAAACTATTGACTACAGCTGCTCACCAGGGTTCAAGCTTAAGGGTATGGCACGAATTAGCTGCCTCCCAAATGGACAGTGGAGTAACTTTCCACCAAAATGTATTAGAGAATGTGCCATGGTTTCATCTCCAGAACATGGTAAAGTTAATGCTCTTTCCGGTGATATGATAGAAGGTGCTACTTTACGGTTCTCCTGTGATAGTCCCTACTACTTGATTGGTCAAGAAACATTAACCTGCCAAGGTAATGGTCAGTGGAATGGACAGATACCACAATGTAAGAACTTGGTCTTTTGCCCTGACCTGGATCCTGTAAACCATGCTGAACACAAGGTTAAAATTGGTGTTGAACAAAAATATGGTCAGTTTCCTCAAGGAACTGAAGTTACCTATACGTGTTCGGGTAACTACTTCTTGATGGGTTTTGATACCTTAAAATGCAACCCTGATGGGTCTTGGTCAGGATCACAGCCATCCTGTGTTAAAGTGGCAGACAGAGAGGTCGACTGTGACAGTAAAGCTGTAGACTTCTTGGATGATGTTGGTGAACCGGTCAGGATCCACTGTCCTGCTGGCTGTTCTTTGACAGCTGGTACTGTTTGGGGTACAGCCATATACCATGAGCTTTCCTCCGTGTGCCGCGCAGCCATCCATGCTGGCAAGCTTCCAAACTCTGGAGGAGCTGTCCATGTTGTGAACAATGGCCCGTACTCCGACTTTCTGGGTTCCGACCTGAATGGTATAAAATCGGAAGAGTTGAAGTCTCTTGCCAGAAGTTTTAGATTCGATTATGTCCGTTCCTCCACAGCAGGTAAGTCAGGATGCCCTGATGGATGGTTTGAGGTAGACGAGAACTGTGTGTATGTTACATCAAAGCAGAGAGCATGGGAAAGAGCTCAAGGTGTGTGCACCAATATGGCTGCTAGACTTGCTGTGCTGGACAAAGATGTAATTCCAAACTCGTTGACTGAGACTCTAAGAGGGAAAGGTTTAACCACCACGTGGATAGGATTGCATAGACTAGATGCTGAGAAGCCCTTTATTTGGGAGTTAATGGATCGTAGTAATGTGGTTCTGAATGATAACCTAACCTTCTGGGCCTCTGGTGAACCTGGAAATGAAACTAACTGCGTATATATGGACATCCAAGATCAGTTGCAGTCTGTGTGGAAAACCAAGTCATGTTTTCAGCCATCTAGTTTTGCTTGCATGATGGATCTGTCAGATAGAAATAAAGCCAAGTGCGATGATCCTGGATCATTGGAAAATGGACACGCCACACTTCATGGACAATCCATTGATGGTTTCTATGCTGGTTCTTCTATAAGGTACAGCTGCGAGGTTCTCCACTACCTCAGTGGAACTGAAACCGTAACTTGTACCACAAATGGCACTTGGAGTGCTCCGAAACCGCGATGTATCAAAGTCATCACCTGCCAAAACCCCCCTGTACCATCATATGGTTCTGTGGAAATCAAACCCCCAAGTAGAACTAACTCGATAAGTCGTGTTGGGTCACCTTTCTTGAGGTTGCCAAGATTACCCCTCCCATTAGCTAGAGCAGCCAAGCCTCCTCCAAAGCCTAGATCCTCACAACCCTCTACTGTGGACTTGGCCTCTAAGGTTAAATTGCCTGAAGGTCATTACCGTGTCGGGTCTAGGGCCATCTACACGTGCGAGTCGAGATACTACGAACTATTGGGATCTCAAGGCAGAAGATGTGACTCTAACGGAAACTGGTCCGGTCGGCCAGCGAGCTGTATTCCAGTTTGCGGACGGTCAGATTCTCCTCGTTCTCCTTTTATCTGGAATGGTAATTCTACAGAAATTGGTCAGTGGCCGTGGCAGGCAGGAATCTCTAGATGGCTTGCAGACCACAATATGTGGTTTCTCCAATGTGGAGGATCTCTATTGAATGAGAAGTGGATCGTCACTGCTGCCCATTGTGTCACCTACTCTGCTACTGCTGAGATTATTGACCCCAATCAATTTAAAATGTATCTGGGCAAGTACTACCGTGATGACTCCAGAGATGATGACTATGTACAAGTAAGAGAGGCTCTTGAGATCCACGTCAATCCTAACTACGACCCCGGCAATTTGAACTTTGACATAGCCTTGATTCAACTGAAAACTCCTGTTACTTTGACTACACGAGTCCAACCAATTTGTCTGCCTACTGACATCACGACAAGAGAACATTTGAAGGAGGGAACATTAGCAGTTGTTACGGGTTGGGGTTTGAATGAAAACAACACCTATTCAGAGACTATTCAACAAGCTGTGTTGCCTGTTGTTGCAGCCAGCACCTGCGAAGAGGGGTACAAGGAGGCAGACTTACCACTGACTGTTACAGAGAACATGTTCTGTGCAGGTTACAAGAAGGGACGTTATGATGCCTGCTCCGGTGACAGCGGAGGACCTTTAGTGTTTGCTGATGATTCCCGTACCGAAAGGAGATGGGTCTTGGAAGGGATTGTCAGCTGGGGCAGTCCCTCCGGATGTGGAAAGGCGAACCAGTATGGTGGCTTCACTAAAGTTAACGTTTTCCTGTCATGGATTAGACAATTCATTTAA

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

For the expression of the codon-optimized version of CrFactor C in P. pastoris, the first step would be to replace the regulatory elements integrated in the cassette generated above, as they have been selected for bacterial expression, with parts that would be recognized in a yeast cell, such as the methanol-activated AOX1 promoter, an appropriate Kozak sequence, and the AOX1 terminator. After assembling the new genetic cassette for P. pastoris expression, it would have to be inserted into an integrative vector (probably from the pPICZα series), which would also carry a selection marker, for instance, zeocin, in conjunction with the expression cassette for the protein of interest. Subsequently, this integrative vector would be employed for the transformation of the yeast cells. In a portion of the successfully transformed yeast cells, the expression cassette-selection marker sequence would then be incorporated into the organism’s genome and, through the antibiotic resistance conferred by the selection marker, those positive transformants could be identified. By utilizing zeocin in particular, the most highly expressing strains can be readily isolated through increasing the dose of the antibiotic, as the resistance provided by zeocin is directly proportional to the number of selection marker genes integrated, which is a direct indication for the number of genes encoding the protein of interest integrated as well. The highly-expressing positive transformants would, afterwards, be cultured in the presence of methanol, which can strongly induce the transcription of the codon-optimized CrFactor C gene, whose mRNA would then be translated (ensured by the Kozak consensus) into CrFactor C protein molecules. Lastly, the nascent CrFactor C protein would be transferred to the endoplasmic reticulum (ER) and the Golgi apparatus for post-translational modifications.

For proteins with extended post-translational modification requirements, such as factor C, a cell-free expression system would not be recommended. However, a T7-based in vitro transcription method, coupled with a highly active and reliable in vitro eukaryotic translation system, such as rabbit reticulocyte lysate (RRL), would be an appealing alternative. For the expression of recombinant CrFactor C, though, the translation system should also be supplemented with microsomal membranes to secure the capacity for glycosylation.

Part 4: Prepare a Twist DNA Synthesis Order

By following the instructions on preparing a DNA synthesis order for Twist, a plasmid containing the CrFactor C sequence codon-optimized for P. pastoris was generated (Figure 2.2).

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).

In continuation of the CrFactor C project, once again, expressing the codon-optimized sequence in P. pastoris would require assembling the genetic cassette, including the gene and all its flanking regulatory elements. Sequencing the assembled construct constitutes an important step before proceeding with the transformation in order to verify that the cloning was indeed successful and that the newly assembled expression cassette is identical to the designed one.

(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?

Also answer the following questions:

1. Is your method first-, second- or third-generation or other? How so?
2. What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.
3. What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)?
4. What is the output of your chosen sequencing technology?

For whole-plasmid sequencing of the integrative vector carrying the CrFactor C codon-optimized sequence, I would choose Oxford Nanopore sequencing. It is a third-generation sequencing technology that combines high speed, reliability, and read accuracy (98-99%), as well as low cost.

Week 3 homework

Lab automation 🦾

Python script for Opentrons artwork

Consistent with this week’s highly automated and digitized theme, for this assignment, I drew inspiration from an image popularized by the Internet, KC Green’s web comic strip “On Fire”, which, in 2014, became a famous -and my personal favorite- online meme (Figure 3.1). As many other people from all over the world, I deeply relate to this meme, which, I feel, accurately describes my life.

Figure 3.1 Panel from KC Green’s web comic strip “On Fire”, which generated the popular “This is fine” online meme in 2014.

As a first step, I fed the right part of the meme into Ronan’s automation art interface, however, the generated artwork required a lot of additional manual processing to resemble the original image (Figure 3.2).

Figure 3.2 (A) Initial image produced by inserting the right-hand ptych of the “This is fine” meme into Ronan’s automation art interface. (B) Final artwork generated after manual rendering of the initial image.

After composing the final artwork (Figure 3.2B), I imported the nine bacterial dyes I utilized (mClover3, mLychee_TF, mWatermelon, Ultramarine, mKO2, dsRed, mScarlet_I, mCherry, mKate2) along with their respective coordinates into Gemini 2.5 Flash (which was incorporated into my personal copy of HTGAA26 Opentrons Colab notebook) and asked it to write a Python script that would generate the “This is fine”-inspired artwork on a petri dish with the Opentrons system. The Pyhton script obtained from this prompt, slightly augmented with some basic tweaking, including the addition of comments at several steps and functions, as well as renaming the bacterial dyes with their corresponding Hex codes, can be found below:

  # Optional, for importing the needed libraries
  import subprocess, sys
  subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy", "pandas", "opentrons"])

  from opentrons import types

  metadata = {    # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata
  'Sofia_Oikonomou': '',
  'This_is_fine_meme_Opentrons_artwork': '',
  'Generate_a_representation_of_the_This_is_fine_meme_with_9_different_bacteria-synthesized_dyes': '',
  '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' : '#409945',
  'A2' : '#40223D',
  'A3' : '#E45741',
  'A4' : '#B9474B',
  'A5' : '#B39223',
  'A6' : '#0F7449',
  'A7' : '#0F184C',
  'A8' : '#45203E',
  'A9' : '#7C463E'
  }

  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

  # Define all points for each dye
    all_dye_points = {
       #mClover3-Background wall lower
       '#409945': [(-11, 11),(-9, 11),(-31, 9),(-9, 9),(-3, 9),(-1, 9),(1, 9),(5, 9),(7, 9),(9, 9),(15, 9),(17, 9),(21, 9),(23, 9),(33, 9),(35, 9),(-39, 7),(-29, 7),(-27, 7),(-25, 7),(-7, 7),(-5, 7),(-3, 7),(-1, 7),(1, 7),(3, 7),(5, 7),(7, 7),(9, 7),(11, 7),(13, 7),(15, 7),(17, 7),(19, 7),(21, 7),(23, 7),(35, 7),(37, 7),(39, 7),(-39, 5),(-5, 5),(-3, 5),(-1, 5),(1, 5),(3, 5),(5, 5),(7, 5),(9, 5),(11, 5),(13, 5),(17, 5),(19, 5),(21, 5),(37, 5),(39, 5),(-5, 3),(-3, 3),(-1, 3),(1, 3),(3, 3),(5, 3),(7, 3),(9, 3),(11, 3),(19, 3),(21, 3),(37, 3),(39, 3),(-7, 1),(-5, 1),(-3, 1),(-1, 1),(1, 1),(3, 1),(5, 1),(7, 1),(9, 1),(19, 1),(39, 1),(-3, -1),(-1, -1),(1, -1),(3, -1),(5, -1),(7, -1),(9, -1),(-1, -3),(1, -3),(3, -3),(5, -3),(7, -3),(1, -5),(3, -5),(5, -5),(7, -5),(1, -7),(3, -7),(5, -7),(7, -7),(1, -9),(3, -9),(5, -9),(-1, -27),(1, -27),(3, -27),(-1, -29),(1, -29),(3, -29),(1, -31)],
       #mCherry-Dog outline
       '#40223D': [(-29, 25),(-27, 25),(-25, 15),(-23, 15),(-13, 15),(-11, 9),(-9, 7),(-27, 5),(-25, 5),(-7, 5),(-27, 3),(-7, 3),(-25, 1),(-23, 1),(-21, 1),(-19, 1),(-17, 1),(-15, 1),(-13, 1),(-11, 1),(-9, 1),(-7, -3),(-5, -3),(-3, -3),(-19, -5),(-17, -5),(-15, -5),(-9, -5),(-1, -5),(-21, -7),(-13, -7),(-11, -7),(-1, -7),(-21, -9),(-11, -9),(-1, -9),(7, -9),(-21, -11),(-11, -11),(-1, -11),(1, -11),(3, -11),(5, -11),(-21, -13),(-11, -13),(-9, -13),(-3, -13),(-1, -13),(1, -13),(-21, -15),(-11, -15),(-7, -15),(-5, -15),(-19, -17),(-11, -17),(-17, -19),(-15, -19),(-13, -19),(13, -19),(11, -21),(3, -23),(5, -23),(7, 23),(9, -23),(-9, -25),(-7, -25),(-5, -25),(-3, -25),(-1, -25),(1, -25),(3, -25),(-3, -27),(-3, -29),(-11, -31),(-3, -31),(-9, -33),(-3, -33),(-7, -35),(-5, -35)],
       #mLychee_TF-Dog and hat
       '#E45741': [(-21, 15),(-19, 15),(-17, 15),(-15, 15),(-23, 13),(-21, 13),(-19, 13),(-17, 13),(-15, 13),(-13, 13),(-23, 11),(-21, 11),(-19, 11),(-17, 11),(-15, 11),(-13, 11),(-23, 9),(-21, 9),(-19, 9),(-17, 9),(-15, 9),(-13, 9),(-11, 7),(-11, 5),(-9, 5),(-25, 3),(-23, 3),(-21, 3),(-19, 3),(-17, 3),(-15, 3),(-13, 3),(-11, 3),(-9, 3),(-23, -1),(-21, -1),(-19, -1),(-17, -1),(-15, -1),(-13, -1),(-11, -1),(-9, -1),(-7, -1),(-5, -1),(-25, -3),(-23, -3),(-21, -3),(-19, -3),(-17, -3),(-15, -3),(-13, -3),(-11, -3),(-9, -3),(-27, -5),(-25, -5),(-23, -5),(-21, -5),(-13, -5),(-11, -5),(-25, -7),(-23, -7),(-25, -9),(-23, -9),(-25, -11),(-23, -11),(-25, -13),(-23, -13),(3, -13),(5, -13),(-25, -15),(-23, -15),(-9, -15),(-3, -15),(-1, -15),(1, -15),(3, -15),(5, -15),(-25, -17),(-23, -17),(-21, -17),(-9, -17),(-7, -17),(-5, -17),(-3, -17),(-1, -17),(1, -17),(3, -17), (5, -17),(7, -17),(-25, -19),(-23, -19),(-21, -19),(-19, -19),(-11, -19),(-9, -19),(-7, -19),(-5, -19),(-3, -19),(-1, -19),(1, -19),(3, -19),(5, -19),(7, -19),(9, -19),(11, -19),(-25, -21),(-23, -21),(-21, -21),(-19, -21),(-17, -21),(-15, -21),(-13, -21),(-11, -21),(-9, -21),(-7, -21),(-5, -21),(-3, -21),(-1, -21),(1, -21),(3, -21),(5, -21),(7, -21),(9, -21),(-25, -23),(-23, -23),(-21, -23),(-19, -23),(-17, -23),(-13, -23),(-11, -23),(-9, -23),(-7, -23),(-5, -23),(-3, -23),(-1, -23),(1, -23),(-25, -25),(-23, -25),(-21, -25),(-19, -25),(-17, -25),(-27, -27),(-25, -27),(-23, -27),(-21, -27),(-19, -27),(-17, -27),(-13, -27),(-11, -27),(-9, -27),(-27, -29),(-25, -29),(-23, -29),(-21, -29),(-19, -29),(-17, -29),(-15, -29),(-11, -29),(-9, -29),(-7, -29),(-25, -31),(-23, -31),(-21, -31),(-19, -31),(-17, -31),(-15, -31),(-13, -31),(-9, -31),(-7, -31),(-5, -31),(-21, -33),(-19, -33),(-17, -33),(-15, -33),(-13, -33),(-11, -33),(-7, -33),(-5, -33),(-19, -35),(-17, -35),(-15, -35),(-13, -35),(-11, -35),(-9, -35),(-3, -35),(-15, -37),(-13, -37),(-11, -37),(-9, -37),(-7, -37),(-5, -37),(-3, -37),(-7, -39),(-5, -39),(-3, -39)],
       #mScarlet_I-Flame outline
       '#B9474B': [(27, 13),(29, 13),(-35, 11),(25, 11),(31, 11),(-35, 9),(-33, 9),(25, 9),(31, 9),(-37, 7),(-31, 7),(25, 7),(33, 7),(-37, 5),(-29, 5),(15, 5),(23, 5),(35, 5),(-39, 3),(13, 3),(17, 3),(23, 3),(35, 3),(11, 1),(17, 1),(21, 1),(37, 1),(11, -1),(19, -1),(39, -1),(9, -3),(9, -5),(9, -7),(5, -25),(5, -27),(5, -29),(-1, -31),(3, -31),(-1, -33),(1, -33)],
       #mKO2-Flame body
       '#B39223': [(27, 11),(29, 11),(27, 9),(29, 9),(-35, 7),(-33, 7),(27, 7),(29, 7),(31, 7),(-35, 5),(-33, 5),(-31, 5),(25, 5),(27, 5),(29, 5),(31, 5),(33, 5),(-37, 3),(-35, 3),(-33, 3),(-31, 3),(-29, 3),(15, 3),(25, 3),(27, 3),(29, 3),(31, 3),(33, 3),(-39, 1),(-37, 1),(-35, 1),(-33, 1),(-31, 1),(-29, 1),(-27, 1),(13, 1),(15, 1),(23, 1),(25, 1),(27, 1),(29, 1),(31, 1),(33, 1),(35, 1),(-39, -1),(-37, -1),(-35, -1),(-33, -1),(-31, -1),(-29, -1),(-27, -1),(-25, -1),(13, -1),(15, -1),(17, -1),(21, -1),(23, -1),(25, -1),(27, -1),(29, -1),(31, -1),(33, -1),(35, -1),(37, -1),(-39, -3),(-37, -3),(-35, -3),(-33, -3),(-31, -3),(-29, -3),(-27, -3),(11, -3),(13, -3),(15, -3),(17, -3),(19, -3),(21, -3),(23, -3),(25, -3),(27, -3),(29, -3),(31, -3),(33, -3),(35, -3),(37, -3),(39, -3),(-39, -5),(-37, -5),(-35, -5),(-33, -5),(-31, -5),(-29, -5),(11, -5),(13, -5),(15, -5),(17, -5),(19, -5),(21, -5),(23, -5),(25, -5),(27, -5),(29, -5),(31, -5),(33, -5),(35, -5),(37, -5),(39, -5),(-39, -7),(-37, -7),(-35, -7),(-33, -7),(-31, -7),(11, -7),(13, -7),(15, -7),(17, -7),(19, -7),(21, -7),(23, -7),(25, -7),(27, -7),(29, -7),(31, -7),(33, -7),(35, -7),(37, -7),(39, -7),(-37, -9),(-35, -9),(-33, -9),(15, -9),(17, -9),(19, -9),(21, -9),(23, -9),(25, -9),(27, -9),(29, -9),(31, -9),(33, -9),(35, -9),(37, -9),(-37, -11),(-35, -11),(17, -11),(19, -11),(21, -11),(23, -11),(25, -11),(27, -11),(29, -11),(31, -11),(33, -11),(35, -11),(37, -11),(-37, -13),(-35, -13),(17, -13),(19, -13),(21, -13),(23, -13),(25, -13),(27, -13),(29, -13),(31, -13),(33, -13),(35, -13),(37, -13),(-37, -15),(17, -15),(19, -15),(21, -15),(23, -15),(25, -15),(27, -15),(29, -15),(31, -15),(33, -15),(35, -15),(37, -15),(15, -17),(17, -17),(19, -17),(21, -17),(23, -17),(25, -17),(27, -17),(29, -17),(31, -17),(33, -17),(35, -17),(15, -19),(17, -19),(19, -19),(21, -19),(23, -19),(25, -19),(27, -19),(29, -19),(31, -19),(33, -19),(35, -19),(13, -21),(15, -21),(17, -21),(19, -21),(21, -21),(23, -21),(25, -21),(27, -21),(29, -21),(31, -21),(33, -21),(11, -23),(13, -23),(15, -23),(17, -23),(19, -23),(21, -23),(23, -23),(25, -23),(27, -23),(29, -23),(31, -23),(7, -25),(9, -25),(11, -25),(13, -25),(15, -25),(17, -25),(19, -25),(21, -25),(23, -25),(25, -25),(27, -25),(29, -25),(31, -25),(-29, -27),(7, -27),(9, -27),(11, -27),(13, -27),(15, -27),(17, -27),(19, -27),(21, -27),(23, -27),(25, -27),(27, -27),(29, -27),(7, -29),(21, -29),(23, -29),(25, -29),(27, -29),(5, -31),(7, -31),(19, -31),(23, -31),(25, -31),(3, -33),(5, -33),(7, -33),(19, -33),(-1, -35),(1, -35),(3, -35),(5, -35),(7, -35),(19, -35),(-1, -37),(1, -37),(3, -37),(5, -37),(7, -37),(-1, -39),(1, -39),(3, -39),(5, -39),(7, -39)],
       #mWatermelon-Background wall upper
       '#0F7449': [(-37, 13),(-35, 13),(-33, 13),(-31, 13),(-11, 13),(-9, 13),(-1, 13),(1, 13),(7, 13),(9, 13),(11, 13),(13, 13),(15, 13),(17, 13),(25, 13),(31, 13),(33, 13),(35, 13),(37, 13),(-37, 11),(-33, 11),(-31, 11),(-29, 11),(-27, 11),(-25, 11),(-7, 11),(-5, 11),(-3, 11),(-1, 11),(1, 11),(5, 11),(7, 11),(9, 11),(11, 11),(13, 11),(15, 11),(17, 11),(19, 11),(21, 11),(23, 11),(33, 11),(35, 11),(37, 11),(-37, 9),(-29, 9),(-27, 9),(-25, 9),(-7, 9),(-5, 9),(11, 9),(13, 9),(19, 9),(37, 9)],
       #Ultramarine-Dark blue elements
       '#0F184C': [(-25, 31),(-23, 31),(-21, 31),(-19, 31),(-15, 31),(-13, 31),(-11, 31),(-9, 31),(-7, 31),(-3, 31),(-1, 31),(1, 31),(3, 31),(7, 31),(9, 31),(11, 31),(13, 31),(15, 31),(21, 31),(23, 31),(25, 31),(-23, 29),(-19, 29),(-15, 29),(-13, 29),(-11, 29),(-3, 29),(-1, 29),(7, 29),(13, 29),(15, 29),(17, 29),(21, 29),(23, 29),(-23, 27),(-19, 27),(-17, 27),(-15, 27),(-13, 27),(-11, 27),(-9, 27),(-7, 27),(-3, 27),(-1, 27),(1, 27),(3, 27),(7, 27),(9, 27),(11, 27),(13, 27),(15, 27),(19, 27),(21, 27),(23, 27),(25, 27),(27, 27),(-23, 25),(-19, 25),(-15, 25),(-13, 25),(-7, 25),(-3, 25),(3, 25),(7, 25),(13, 25),(15, 25),(21, 25),(23, 25),(-23, 23),(-19, 23),(-15, 23),(-13, 23),(-11, 23),(-9, 23),(-7, 23),(-3, 23),(-1, 23),(1, 23),(3, 23),(7, 23),(13, 23),(15, 23),(21, 23),(23, 23),(25, 23),(27, 23),(-27, 21),(31, 21),(-33, 19),(-31, 19),(-29, 19),(33, 19),(35, 19),(-37, 15),(-35, 15),(-33, 15),(-31, 15),(-11, 15),(-9, 15),(-1, 15),(1, 15),(9, 15),(11, 15),(13, 15),(15, 15),(17, 15),(25, 15),(27, 15),(29, 15),(31, 15),(-29, 13),(-27, 13),(-25, 13),(-7, 13),(-5, 13),(-3, 13),(19, 13),(21, 13),(23, 13),(-23, 7),(-21, 7),(-19, 7),(-17, 7),(-15, 7),(-13, 7),(-23, 5),(-21, 5),(-19, 5),(-17, 5),(-15, 5),(-13, 5),(-29, -7),(-27, -7),(-7, -7),(-5, -7),(-3, -7),(-31, -9),(-29, -9),(-27, -9),(-17, -9),(-15, -9),(-7, -9),(-5, -9),(-3, -9),(9, -9),(11, -9),(13, -9),(-33, -11),(-31, -11),(-29, -11),(-27, -11),(-17, -11),(-15, -11),(-13, -11),(-7, -11),(-5, -11),(-3, -11),(7, -11),(9, -11),(11, -11),(13, -11),(15, -11),(-33, -13),(-31, -13),(-29, -13),(-27, -13),(-17, -13),(-15, -13),(-13, -13),(-5, -13),(7, -13),(9, -13),(11, -13),(13, -13),(15, -13),(-35, -15),(-33, -15),(-31, -15),(-29, -15),(-27, -15),(-17, -15),(-15, -15),(-13, -15),(7, -15),(9, -15),(11, -15),(13, -15),(15, -15),(-35, -17),(-33, -17),(-31, -17),(-29, -17),(-27, -17),(-17, -17),(-15, -17),(-13, -17),(9, -17),(11, -17),(13, -17),(-35, -19),(-33, -19),(-31, -19),(-29, -19),(-27, -19),(-33, -21),(-31, -21),(-29, -21),(-27, -21),(-31, -23),(-29, -23),(-27, -23),(-31, -25),(-29, -25),(-27, -25),(-7, -27),(-5, -27),(-5, -29),(11, -29),(13, -29),(15, -29),(17, -29)],
       #mKate2-Puff of smoke
       '#45203E': [(-7, 39),(-5, 39),(-3, 39),(-1, 39),(1, 39),(3, 39),(5, 39),(7, 39),(-15, 37),(-13, 37),(-11, 37),(11, 37),(13, 37),(15, 37),(-19, 35),(-27, 29),(-29, 27),(-27, 27),(-31, 25),(31, 25),(-31, 23),(-29, 23),(-27, 23),(31, 23),(-33, 21),(-31, 21),(-29, 21),(-25, 21),(29, 21),(33, 21),(-35, 19),(-27, 19),(-25, 19),(-23, 19),(27, 19),(29, 19),(31, 19),(-35, 17),(-33, 17),(-31, 17),(-29, 17),(-27, 17),(-25, 17),(-23, 17),(-21, 17),(-19, 17),(-17, 17),(-15, 17),(-13, 17),(-11, 17),(-9, 17),(-7, 17),(-5, 17),(-3, 17),(-1, 17),(1, 17),(9, 17),(11, 17),(13, 17),(15, 17),(17, 17),(19, 17),(21, 17),(23, 17),(25, 17),(27, 17),(29, 17),(31, 17),(33, 17),(35, 17),(-29, 15),(-27, 15),(-7, 15),(-5, 15),(-3, 15),(19, 15),(21, 15),(23, 15),(33, 15),(35, 15),(37, 15),(-15, -23),(-15, -25),(-13, -25),(-11, -25),(-15, -27),(-13, -29)],
       #dsRed-Coffee mug
       '#7C463E': [(9, -29),(19, -29),(9, -31),(11, -31),(13, -31),(15, -31),(17, -31),(21, -31),(9, -33),(11, -33),(13, -33),(15, -33),(17, -33),(21, -33),(9, -35),(11, -35),(13, -35),(15, -35),(17, -35),(9, -37),(11, -37),(13, -37),(15, -37)]
  }

  for dye_name, points_list in all_dye_points.items():
  if not points_list: # Skip if no points for this dye
      continue

  color_source_well = location_of_color(dye_name)

  pipette_20ul.pick_up_tip()

  # Aspirate in batches to ensure enough liquid
  for i, (x_coord, y_coord) in enumerate(points_list):
      if i % 20 == 0 or pipette_20ul.current_volume < 1: # Re-aspirate if volume is low or every 20 points
          # Aspirate enough for remaining points or 20uL, whichever is less
          volume_to_aspirate = min(20, len(points_list) - i)
          if volume_to_aspirate > 0:
              pipette_20ul.aspirate(volume_to_aspirate, color_source_well)

      adjusted_location = center_location.move(types.Point(x=x_coord, y=y_coord))
      dispense_and_detach(pipette_20ul, 1, adjusted_location)

  pipette_20ul.drop_tip()

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

  # Execute Simulation / Visualization -- don't change this code block
    protocol = OpentronsMock(well_colors)
    run(protocol)
    protocol.visualize()

Lastly, when I simulated the artwork, the Python script presented above did produce the designed image (Figure 3.3).

Figure 3.3 Simulation of the “This is fine” artwork generated by running the Python script displayed above.