Week 1 HW: Principles and Practices

Class Assignment

Describe a biological engineering application or tool you want to develop and why.

Aplication title: De novo design of proteins binders for neutralizing Bothrops venom toxins

Antivenoms are a mix of immunoglobulins produced traditionally by the hyperimmunization of large animals with crude venom obtained from clinically-relevant snakes (Ratanabangkoon, K., 2023). Novel alternatives have emerged to neutralize venom toxins without the use of animals. For example, Torres and collaborators (2025) designed proteins with high affinity for important regions of cytotoxins from the 3FTx family. These proteins showed great neutralizating capacity in vitro and great protective capacity in vivo .

“Omics” strategies applied to snake venoms have been developed as Venomics, these strategies allows the characterization of whole venoms building protein profiles. Similar estrategies can also be used for studying antibody-toxin complexes as Antivenomics (Lomonte, 2017). The information obtained through venomics and antivenomics give us the ability to build databases with structural and functional information of snake toxins; this information is important for disigning de novo proteins

Bothrops genus is one of the most relevant in South America and has been studied broadly. Many venomics and antivenomics studies has been developed giving high amounts of information that can be used for the design of proteins with high affinity to their important regions.

The following proposal aims to create a system that involves venomics and antivenomics estudies of Bothrops venoms to create a database that can be used to identify key regions with high impact in their toxic activities. Through these regions I propose the design of proteins using artificial intelligence. These candidates could be useful to explore the possibility of designing synthetic antivenoms that don’t depend on the use of animals for their production (Figure 1)

Justification: Snakebites are classified as Neglected Tropical Diseases by the World Health Organization (WHO) affecting low-and middle-income countries from Africa, Asia, and South America (World Health Organization, 2019).

Antivenoms are the only approved treatment against snakebites. Antivenoms show several limitations in their efficacy and production. Snake venoms present variability in their composition. This could lead to antivenoms with different efficacies depending on the venom used for their production. Additionally, antibodies present in the antivenom can cause adverse reactions when administered to the patient.

Antivenom production is technologically complex with high costs, resulting in a limitation for low-income countries (Alangode et al., 2020). Antivenoms not only present but also numerous challenges in their production but also in their requisites at different levels to be used safely (Figure 2, Potet et al, 2021)

The limitations observed in antivenoms produced traditionally supports the necessity of novel alternatives that can be produced safely and with low cost in their design. The use of artificial intelligence with the information provided by venomics and antivenomics opens the possibility of creating synthetic alternatives for the neutralization of venom toxins, and their design could also be optimized to a production at large scale increasing their availabilty and reducing their cost.

Analysis of Protein Binders for Governance Goals and Actions

The World Health Organization has established a programme to evaluate the safety and effectivenes of current antivenoms intended for their use in different countries. This programme led to the recruit of several world experts, forming the Working Group on Snakebite Envenoming. Through this group, the WHO has established a goal of reducing the mortality and disabiluty of snakebite envenomings by 2030. (World Health Organization, 2019)

To accomplish this goal, the working group has developed a road map with objetives at different scales (Figure 3, Williams et al, 2019)

Designing protein binders De novo fits with the objective “Safe and Effective Treatment” from the WHO roadmap. This objective proposes 5 key activities:

• Make safe, effective antivenoms available, and affordable to all
• Better control and regulation of antivenoms
• Prequalification of antivenoms
• Integrated health worker training and education
• Improving clinical decision making, treatment, recovery and rehabilitation
• Investing in innovative research on new therapeutics

The implementation of these protein binders as an alternative to traditionally-produced antivenoms should meet with these 5 key activites. The image below analysis how portein binders could contributo to these key activities and proposes 4 potential governance actions according to the objetive and key activites proposes by the WHO (Figure 4)

One of the most important governance actions that I would prioritize is the development of reproducible protocols for the design and use of protein binders against snake venoms. Reproducible protocols require the participation of public and private research institutions and involves the development of clear and highly reproducible strategies for de novo prediction of these protein binders, recombinant production and scalation. This action may contribute to other actions like the creation of guidelines promoted by the WHO using these protocols. In Peru, the National Health Institute is in charge of antivenom production, the development of reproducible protocols requires the association of research laboratories with this institute. A pilot program can also be created using different species of the genus Bothrops to design and test the efficacy of protein binders.

References

• Alangode, A., Rajan, K. & Nair, B. G. (2020). Snake antivenom: Challenges and alternate approaches.. Biochemical Pharmacology, 181. https://doi.org/10.1016/J.BCP.2020.114135
• Lomonte, B. and Calvete, J. J. (2017). Strategies in ‘snake venomics’ aiming at an integrative view of compositional, functional, and immunological characteristics of venoms. Journal of Venomous Animals and Toxins including Tropical Diseases, 23(1). https://doi.org/10.1186/S40409-017-0117-8
• Potet, J., Beran, D., Ray, N., Alcoba, G., Habib, A. G., Iliyasu, G., Waldmann, B., Ralph, R., Faiz, M. A., Monteiro, W. M., Sachett, J. d. A. G., Di Fábio, J. L., Cortés, M. d. l. Á., Brown, N. & Williams, D. (2021). Access to antivenoms in the developing world: A multidisciplinary analysis.. Toxicon: X, 12. https://doi.org/10.1016/J.TOXCX.2021.100086
• Ratanabanangkoon, K. (2023). Polyvalent Snake Antivenoms: Production Strategy and Their Therapeutic Benefits. Toxins, 15. https://doi.org/10.3390/TOXINS15090517
• Thumtecho, S., Burlet, N. J., Ljungars, A. & Laustsen, A. H. (2023). Towards better antivenoms: navigating the road to new types of snakebite envenoming therapies. Journal of Venomous Animals and Toxins including Tropical Diseases, 29.
• Torres, S. V., Valle, M. B., Mackessy, S., Menzies, S. K., Casewell, N. R., Ahmadi, S., Muratspahić, E., Sappington, I., Overath, M., Rivera-de-Torre, E., Ledergerber, J., Laustsen, A. H., Boddum, K., Bera, A. K., Kang, A., Brackenbrough, E., Cardoso, I. A., Crittenden, E., Edge, R. & Decarreau, J. (2025). De novo designed proteins neutralize lethal snake venom toxins.. Nature, 639. https://doi.org/10.1038/S41586-024-08393-X
• Williams, D., Faiz, M. A., Abela-Ridder, B., Ainsworth, S., Bulfone, T. C., Nickerson, A., Habib, A. G., Junghanss, T., Wen, F. H., Turner, M. J., Harrison, R. A. & Warrell, D. A. (2019). Strategy for a globally coordinated response to a priority neglected tropical disease: Snakebite envenoming.. PLoS Neglected Tropical Diseases, 13. https://doi.org/10.1371/JOURNAL.PNTD.0007059
• World Health Organisation. (2019, April 8). Snakebite Envenoming. Who.int; World Health Organization: WHO. https://www.who.int/news-room/fact-sheets/detail/snakebite-envenoming
• OpenAI (2026). CHATGPT(GTP-5-based-model). Used for conceptual discussion and feedback on project development. https://chat.openai.com/

Week 2 Lecture Prep

Professor Jacobson Questions:

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?

During DNA replication our cells use DNA polymerases for DNA synthesis, these polymerases can have an error rate of 1 bp per every 100,00 nucleotides. As the human genome is composed of 6 billion bp per diploid cell, every time a cell divides DNA polymerases will make about 120,000 errors (Pray, 2008). While these errors may become mutations that could lead to new adaptations, it is important to correct these errors since they could lead to many dangerous effects on the organism’s life. To correct these errors some DNA polymerases come with an extra exonuclease 3’-5’ activity that serves as proofreading. For example, PolƐ is a DNA polymerase that is involved in the process of DNA replication of the leading strand. PolƐ is a holoenzyme compose of many subunits, when a mismatch is detected in the pol site of PolƐ the proteins arrest the pol activity and the protein moves away from the mismatched 3’end preventing additional base incorporation. Then, the proofreading region generates a change in the DNA conformation. This takes the mismatched base to the exo site of the polymerase generating the excision, after that the polymerase resumes its activity after correcting the mistake (Wang et al, 2025). Proofreading mechanisms help to reduce errors induced by the replication process, for that reason, polymerases with proofreading activity are highly important in different applications. To design complex synthetics systems, it is necessary to reduce the possibility of bp mismatches caused by the polymerase, for that reason, high fidelity polymerase with proofreading activity is available commercially

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?

Human genetic code is a set of three RNA bases called codon; every codon decodes a specific amino acid. The genetic code shows codon degeneracy, in other word codons that can be used to decode the same amino acid. In average most amino acids correspond to three codons, with some exceptions like Methionine and Tryptophan that only belong to a single codon. While codon degeneracy allows the use of different codons to produce an amino acid, different organisms have different preferences for the codons they use. Codon preference may occur for different reasons like metabolic pressures where some specific tRNAs are used instead of a wide variety of tRNAs for every codon available. Similarly, protein characteristics may influence the preference for some tRNAs than others (Ford, n.d).

Dr. LeProust Questions:

• What’s the most used method for oligo synthesis currently? The most used method for DNA synthesis is through Phosphoramidite chemistry. This technology consists of the use of Nucleoside Phosphoramidites, a type of modified nucleosides that allows the sequential addition of new bases in a cyclic manner. These modified nucleosides are protected in a way that chemists can control the reaction of oligonucleotide synthesis by exposing only the regions of the nucleotide they desire.
• Why is it difficult to make oligos longer than 200nt via direct synthesis? One of the reasons why synthesis of oligos longer than 200 nt is the increase of errors caused by the natural DNA polymerases error rate or fidelity of nucleoside phosphoramidite thecnology. Another reason could be the limitations of Quality Control, since oligos require MALDI spectrometry to test their quality, this method limits the length to 10-50 nucleotides.
• Why can’t you make a 2000bp gene via direct oligo synthesis? Production of long oligos faces the main challenge of accumulating errors in their formation making it difficult to obtain high yield of oligos with high quality (Yin et al, 2024)

George Church Questions:

What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

While many organisms are capable to synthesize all these 20 amino acids, some groups like ours (Metazoa) have lost the capability to synthetize nine EAAs. These amino acids are histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine. Additionally, arginine can also be considered essential because of the incapacity of the body to synthetize it under special periods of growth (Lopez & Mohiuddin, 2024). One explanation for the loss of the synthetic capacity of these essential amino acids is because of energetic efficiency. Estimates suggest that essential amino acids have high energetic costs in their synthesis. Selective pressures towards energy efficiency may contribute to the loss of capacity to produce essential amino acids and relying on them by direct consumption (Kasalo et al., 2026). Considering that in Jurassic Park movies the lysine contingency consists in limiting the expansion of dinosaurs by creating to them the incapacity of lysine production. This contingency now seems futile because animals have more essential amino acids than dinosaurs in that case. Animals have overcome this limitation through the ingest of these amino acids in their diets, so in consequence dinosaurs can also survive by consuming other living things that produce lysine either from animal or plant sources.

References:

• Ford, T. (n.d.). Plasmids 101: Codon usage bias. Available at https://blog.addgene.org/plasmis-101-codon-usage-bias?
• Kasalo, N., Domazet-Lošo, M., & Domazet-Lošo, T. (2026). Outsourcing of energetically costly amino acids at the origin of animals. Nature Communications. https://doi.org/10.1038/s41467-026-68724-6
• Lopez, M. J., & Mohiuddin, S. S. (2024, April 30). Biochemistry, essential amino acids. StatPearls - NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK557845/
• Pray, L. (2008) DNA Replication and Causes of Mutation. Nature Education 1(1):214
• Wang, F., He, Q., O’Donnell, M. E., & Li, H. (2025). The proofreading mechanism of the human leading-strand DNA polymerase ε holoenzyme. Proceedings of the National Academy of Sciences, 122(22), e2507232122. https://doi.org/10.1073/pnas.2507232122
• Yin, Y., Arneson, R., Yuan, Y., & Fang, S. (2024). Long oligos: direct chemical synthesis of genes with up to 1728 nucleotides. Chemical Science, 16(4), 1966–1973. https://doi.org/10.1039/d4sc06958g

Week 2 HW: DNA Read, Write & Edit

Part 1: Benchling & In-silico Gel Art

Lambda Sequence: Sequence from E.coli I cl857 S7 lambda bateriophage (Daniels, et al., 1983) available at New England Biolabs (N3011)

A digest simulation was performed using the lambda sequence and 7 different restriction enzyme (EcoRI, HindIII, BamHI, KpnI, EcoRV, SacI, and SalI). The range of fragments obtained from this simulation varies depending on the enzyme used.

EcoRV, for example, has 21 restriction sites, giving 22 band in the simulation. On the other hand, KpnI, SacI, and SalI have a few restriction sites, showing only two bands in the simulation (Figure 2).

Since restriction enzymes cleave specific sequences in the genome, the difference between the number of sites for EcoRV compared to KpnI, SacI, and SalI raises the question: **Why does the lambda genome have more restriction sites for EcoRV than others?

Bacteriophages usually present fewer restriction sites as a response to this defense mechanism. This difference may change depending on the interaction between the bacteriophage and its host (Pleška & Guet, 2017)

Gel Art: Raimondi Stela Using a simulation of the digestion of lambda genome with different restriction enzymes, I tried to portray the “god of staffs”. This is a deity found in the Raimondi Stela that belonged to the Chavin culture (Figure 3)

The gel created tried to be similar to the deity holding their staffs.

Part 3: DNA design challenge

Protein Chosen: Bothrops atrox snake venom nerve growth factor

Description: Nerve Growth Factor (NGF) is a member of the neurotrophin family that regulates the growth, differentiation, and survival of peripheral neurons during the development of the nervous system. This factor acts through two key receptors, tyrosine kinase A receptor (TrkA) and p75 neurotrophin receptor (p75NTR). The TrkA receptor activates signaling cascades that promote neuron differentiation and neurite growth.

Snake venom NGF (sNGF) is a protein that has been reported from the venom of elapid and viperid snakes. It is proposed that the presence of sNGF in the venom helps the envenomation process by causing the release of signaling chemicals that promote inflammatory reactions and increase vascular permeability, aiding the spread of other toxins and promote the apoptosis of cells (Sunagar et al., 2013).

Justification: NGFs have been proposed as promising options to treat neurodegenerative diseases and promote regenerative processes. sNGFs show high similarities to human NGFs and have been studied for many applications like chondrogenesis, neurite outgrowth, neuroprotection, tumor growth inhibition, etc. (Devi & Jayaraman, 2025).

Because sNGFs present special activities during envenomation, the study of sNGFs from other snake species may help to find new functions with a possible use in the study of regeneration and nervous development. These new functions may contribute to the design of synthetic alternatives with specific functions that can be applied for therapeutic purposes.

Protein Sequence: I have chosen the sequence of a sNGF from Bothrops atrox snake venom available at the UniProt database (ID: A0A1L8D608). The existence of this protein was proved through transcription level.

Selection of the expression system To continue with the process of reverse translation and codon optimization, I investigated which expression system would be the most suitable to produce this protein. Schütz et al. (2023) offers a concise guide for expression system selection with a decision graph depending on the characteristics of the protein to be expressed (Figure 3).

I gathered the following information of the protein based on four decision points proposed by Schütz:

1. The target is eukaryotic protein
2. Uniprot PTM/Processing section describes that the protein contains a signal region related to its secretion between the 1-18 amino acids and three disulfide bonds (Figure 4).
3. The resulting protein would have 241 amino acids in total when expressed and 233 amino acids when secreted with a molecular mass of 27.197 KDa.

3. Uniprot information from other NFGs does not show that requires glycosylation when expressed
4. In the case of this design, it wouldn’t be necessary to have an expression system with higher yield

Based on this information the decision graph suggests using an expression system using a strain of E. coli that promotes disulfide bond formation. This decision is also supported by other studies that use E. coli to express human NFGs in vitro (Tilko et al., 2016; Dicou, 1992)

Reverse Translate Before performing the reverse translation of the protein, I decided to eliminate the amino acids 1-18 because they are part of the signal region of the protein and this won’t be used for this design. I included an initiator methionine at the N-terminus to allow translation initiation. The sequence modification was realized using the Benchling software.

Using the same software I reversed translated the protein using Escherichia coli (K12) genetic code using the method Match codon usage. The result of this process is an optimized sequence of 672 bp. This sequence was used later to perform a Blastx analysis where was found that the resulting sequence matches with other NGF from snakes (Figure 5)

Codon optimization To simulate the creation of a clonal gene using the Twist Bioscience environment, the optimized sequence was uploaded in the software. A codon optimization was performed in the application. During the configuration of the optimization, I conserved the region 321-524 since it’s predicted as the NGF region by the Blastx result.

The resulting sequence was later labeled as optimized B.atrox NGF (BatroxNGFOptimized) and finally chosed as the sequence to be used for the creation of the expression cassette.

Expression Vector Selection To select a suitable expression vector, it is necessary to consider that the protein requires a proper environment to develop three disulfide bonds. The formation of disulfide bonds can be achieved by expressing the protein in E. coli periplasm or in the cytoplasm of engineered E coli.

A study performed by Shamriz et al. 2016 uses the pET-32a expression vector that contains the Trx-tag for increasing the solubility of the protein and its expression in E. coli Origami (DE3) to promote the correct formation of disulfide bonds in the cytoplasm of E. coli. Another strategy aims to translocate the recombinant protein into the periplasm using a signal peptide that helps the formation of disulfide bonds and increases its stability (Pouresmaeil & Azizi-Dargahlou, 2023).

Based on this information I opted for a pET-29b(+) expression vector from Twist Bioscience because it contains an N-terminal S•Tag™ sequence and may help with the protein solubility and a C-terminal His•Tag® sequence for its easy purification.

To help with the sulfide formation I selected SHuffle® strain from New England Biolabs that is engineered for the formation of disulfide bond in the cytoplasm.

Part 4: Preparation of Twist DNA Synthesis Order A simulation of DNA Synthesis order was generated using the optimized NGF sequence obtained from the previous part and inserted into the pET-29b (+) expression vector generating the plasmid as can be observed below (Figure 6)

Part 5: DNA Read/Write/Edit

DNA Read Sequencing Idea: Genome-Wide Association Studies of Genetic Elements Related with Peanut Allergy Diversity in Peru Description: Allergies are misdirected immune reactions against a specific molecule (Allergens) to a previously exposed patient. These reactions are associated with an immune response mediated by a particular type of antibody called IgE. Allergies are diverse in nature and involve several ambiental and congenital factors, but also genetic factors. Several genes have been investigating for their involvement in allergic reactions, showing a complex heterogeneity that varies person to person (Falcon & Caoili, 2023) Genetic factors associated with allergies may help to elucidate the mechanisms that promote allergies predisposition. For that purpose, Genome-wide association studies (GWASs) offer a good option to study the genetic elements associated with allergies. GWAS are used to identify the association between genotypes with phenotypes. This is performed by selecting a group of individuals to obtain their phenotypic information. Using different GWAS arrays or sequencing strategies, genotypes of these individuals are obtained. Phenotypic and genotypic information is later used to conduct association tests to obtain relevant genetic elements that may be important for the phenotype studied (Uffelmann et al., 2021)

Technologies to perform GWAS genotyping GWAS genotyping technologies are microarrays, Whole Exome Sequencing (WES) or Whole Genome Sequencing (WGS). To study the genetic component of peanut allergy in Perú we can use previously associated genes like HLA-DQ and HLA-DR o genes located in chromosome six (Allergies and Genetics | Health and Medicine | Research Starters | EBSCO Research, n.d.). The objective of genotyping these genes is to determine Single Nucleotide Polymorphisms (SNPs) that might have strong association with peanut allergy.
Whole genome sequencing technologies can be applied for SNP genotyping and involves sequencing all regions of the entire genome. On the other hand, Whole exome sequencing is a method for sequencing only the exonic region of the human genome.

• Is the method first-, second- or third- generation or other? Whole and Exome genome sequencing are part of the Next Generation Sequencing (NGS) because they are based in the massively parallel sequencing process.
• What is your input? How do you prepare your input? For this study my input is genomic DNA extracted from a representative Peruvian sample of individuals diagnosed with peanut allergy.
• What are the essential steps of your chosen technology, how does it decode the bases of your DNA sample? For WGS studies, Illumina uses a sequencing technology by synthesis, where fluorescently labeled nucleotides to sequence millions of clusters on a cell surface in parallel.
• What is the output of your sequencing technology? Illumina sequencing data is obtained through the signal intensity measurement of the labeled nucleotides that serve a terminators

DNA Write Project Idea: Snake venom NGF from B atrox The following idea aims to express a snake venom NGF from B. atrox. sNGFs have been applied in numerous studies to test their potential effect on regenerative processes because of their similarity with the human NGF and because of novel properties that may appear because of its evolution in the snake venom. For its production I propose the recombinant production of this protein, for that I realized used a sequence available at UniProt (ID: A0A1L8D608) a reverse translated to then propose it cloning using a vector in E. coli and expression in the same organism.

DNA Edit Project Idea: Using genetic engineered cells in hydrogels for cartilage regeneration Hydrogels are tridimentional networks polymers that can be used as scaffold for cartilage tissue engineering. A promising approach is to modify the genome of stem cells, creating specific gene circuits to promote cartilage regeneration. Trough gene edition, we could use steam cells to modify their proliferation capacity or control it using genetic circuits, a concept that may help with this idea is the concept of BioBricks that allows to create libraries that coul be used to modify the behavior of these stem cells (Elnaggar et al., 2025).

References

• Allergies and genetics | Health and Medicine | Research Starters | EBSCO Research. (n.d.). EBSCO. https://www.ebsco.com/research-starters/health-and-medicine/allergies-and-genetics
• Daniels, D.L. et al. (1983). Appendix II: Complete Annotated Lambda Sequence. R.W. Hendrix, J.W. Roberts, F.W. Stahl and R. A. Weisberg(Ed.), Lambda-II. 519-676. New York: Cold Spring Harbor Laboratory Press.
• Devi, S., & Jayaraman, G. (2025). Unraveling the molecular basis of snake venom nerve growth factor: human TrkA recognition through molecular dynamics simulation and comparison with human nerve growth factor. Frontiers in Bioinformatics, 5, 1674791. https://doi.org/10.3389/fbinf.2025.1674791
• Elnaggar, K. S., Gamal, O., Hesham, N., Ayman, S., Mohamed, N., Moataz, A., Elzayat, E. M., & Hassan, N. (2025). A guide in synthetic biology: Designing genetic circuits and their applications in stem cells. SynBio, 3(3), 11. https://doi.org/10.3390/synbio3030011
• Falcon, R. M. G., & Caoili, S. E. C. (2023). Immunologic, genetic, and ecological interplay of factors involved in allergic diseases. Frontiers in Allergy, 4, 1215616. https://doi.org/10.3389/falgy.2023.1215616
• Pleška, M., & Guet, C. C. (2017). Effects of mutations in phage restriction sites during escape from restriction–modification. Biology Letters, 13(12). https://doi.org/10.1098/rsbl.2017.0646
• Pouresmaeil, M., & Azizi-Dargahlou, S. (2023). Factors involved in heterologous expression of proteins in E. coli host. Archives of Microbiology, 205(5), 212. https://doi.org/10.1007/s00203-023-03541-9
• Shamriz, S., Ofoghi, H., & Amini-Bayat, Z. (2016). Soluble Expression of Recombinant Nerve Growth Factor in Cytoplasm of Escherichia coli. Iranian Journal of Biotechnology, 14(1), 16–22. https://doi.org/10.15171/ijb.1331
• Sunagar, K., Fry, B. G., Jackson, T. N. W., Casewell, N. R., Undheim, E. a. B., Vidal, N., Ali, S. A., King, G. F., Vasudevan, K., Vasconcelos, V., & Antunes, A. (2013). Molecular Evolution of Vertebrate Neurotrophins: Co-Option of the Highly Conserved Nerve Growth Factor Gene into the Advanced Snake Venom Arsenalf. PLoS ONE, 8(11), e81827. https://doi.org/10.1371/journal.pone.0081827
• Uffelmann, E., Huang, Q. Q., Munung, N. S., De Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021). Genome-wide association studies. Nature Reviews Methods Primers, 1(1). https://doi.org/10.1038/s43586-021-00056-9

Week 3 HW: Lab Automation

Opentrons Artwork: Gel Designing

Design: Snake Trimeresurus puniceus Inspired from a snake photo taken in the Oswaldo Meneses serpentarium, Lima, Peru. Art created Donovan’s Automation art interface

Python Script Design Opentrons script was created following the instructions and ideas offered by the HTGAA Opentrons Colab. To create the script first I created a pseudocode with the idea of how the robot will work

Following the idea of the pseudocode I followed the script design from Dominika Wawrzyniak, 2021 student and adapted to the coordinates from Donovan’s Automation page. For this first draft I decided to copy and paste the coordinates and give them a dictionary structure, then I changed the color names using the names from the robot deck setup constants. The resulting script is the following :

# Set the initial coordinates take from the donovan's page (Converted into a dictionary)
  Coordinates = { 
    "Green" : [],
    "Red" : [],
    "Blue" : [],
    "Yellow" : [],
    "Cyan" : []
    }
  
#To avoid using many tips the objective is to create a function that takes up the points and add the volume per color
  def Coordinate_per_color(color_string):
    # Pick up a 20 ul tip
    pipette_20ul.pick_up_tip()
    # For every coordinate per color
    for i in range(len(Coordinates[color_string])):
    # i shows the number of positions
       if i % 20 == 0:
    # Aspirate a volume 20 if the total of remaining coord to paint is more than 20
         pipette_20ul.aspirate(min(20, len(Coordinates[color_string])-i), location_of_color(color_string))

        # Get the x and y coordinates
        x_coordinate = Coordinates[color_string][i][0]
        y_coordinate = Coordinates[color_string][i][1]

        # Move to the x and y coordinates
        adjusted_location = center_location.move(types.Point(x_coordinate, y_coordinate))
        # Dispense 1 ul to the position
        pipette_20ul.dispense(1, adjusted_location)
        hover_location = adjusted_location.move(types.Point(z = 2))
        pipette_20ul.move_to(hover_location)

      # Finishing drop the tip
      pipette_20ul.drop_tip()

      #Call the function Coordinate_per_color for every color in the dictionary
      for name in Coordinates.keys():
        print(name)
        Coordinate_per_color(name)

After executing the script, I simulated the visualization and got the Image I wanted to create

Second Design: Geometrical Green/Red Yin and Yang

After recieving the instructions from my node y change the design for a Yin-Yang inspired design. To create the design I comtemplated the idea of using mathematical formulas to desing the pattern.

First I created a code testing the mathematical approach which consisted in creating the design using circles and semicircles, first I integrated the mathematical code with the robot operation code resulting in a messy code

      // First Yin-Yang Code
      # Start at the center
      cursor = center_location.move(types.Point(x=0, y=0))
      # Define de radius as 20 (To reduce the times to aspirate a volume)
      radius = 20
      # Define the number of point (Default 40)
      points = 40
      # Function to create semicircles
      def thetha(i):
        theta = np.pi * i / (points - 1)
        return theta
      # Fucntion aspirate
      def aspirate(color):
        if i % 20 == 0:
          pipette_20ul.aspirate(min(20, points - i), location_of_color(color))
      # Function hover
      def hover():
        hover_location = adjusted_location.move(types.Point(x = 0, y = 0, z = 2))
        pipette_20ul.move_to(hover_location)
      # Create a green semicircle
      pipette_20ul.pick_up_tip()
      for i in range(points):
        aspirate("Green")
        theta = thetha(i)
        x = radius * np.sin(theta)
        y = (radius * np.cos(theta)) 
        adjusted_location = cursor.move(types.Point(x=x, y=y))
        pipette_20ul.dispense(1, adjusted_location)
        hover()
      # Create and S-divider in the circle (UpperSide)
        inner_radius = 10
        for i in range(points):
        aspirate("Green")
        theta = thetha(i)
        x = inner_radius * np.sin(theta)
        y = (inner_radius * np.cos(theta)) + radius/2
        adjusted_location = cursor.move(types.Point(x=x, y=y))
        pipette_20ul.dispense(1, adjusted_location)
        hover()
        
        pipette_20ul.drop_tip()
      # Create a Red semicircle
      pipette_20ul.pick_up_tip()
      for i in range(points):
        aspirate("Red")
        theta = thetha(i)
        x = radius * np.sin(theta)
        y = (radius * np.cos(theta))
        adjusted_location = cursor.move(types.Point(x=-x, y=y))
        pipette_20ul.dispense(1, adjusted_location)
        hover()
      # Create and S-divider in the circle (LowerSide)
      inner_radius = 10
      for i in range(points):
        aspirate("Red")
        theta = thetha(i)
        x = inner_radius * np.sin(theta) * -1
        y = (inner_radius * np.cos(theta)) - radius/2
        adjusted_location = cursor.move(types.Point(x=x, y=y))
        pipette_20ul.dispense(1, adjusted_location)
        hover()

Second Attemp: To make the code more readable I created two custom functions called create_circle and create_semicircle. Also adapted the logic to get a list of coordinates so it can be used by the code example shared by the Node.

  ## Ying-Yang Code 
  ## Create two functions that will give the coordinate for circles
  ## Circle function
  def create_circle(x_center, y_center, radius, points):
     coordinates = []
     for i in range(points):
      angle = 2 * math.pi * i / points
      x = x_center + radius * math.cos(angle)
      y = y_center + radius * math.sin(angle)
      coordinates.append((x, y))
     return coordinates
  
  ## Semicircle function
  def create_semicircle(x_center, y_center, radius, points, direction = "left"):
    """
    Four semicircle orientations:
      - right
      - left
      - up
      - down
    """
    coordinates = []
    for i in range(points):
      angle = math.pi * i / (points)
      # Change direction
      if direction == "right":
        # Base x and y coordinates (Default = right)
        x = x_center + radius * math.sin(angle)
        y = y_center + radius * math.cos(angle)
        coordinates.append((x, y))
      elif direction == "left":
        x = x_center + radius * math.sin(angle)
        y = y_center + radius * math.cos(angle)
        coordinates.append((-x, -y))
      elif direction == "up":
        x= x_center + radius * math.cos(angle)
        y = y_center + radius * math.sin(angle)
        coordinates.append((x, y))
      elif direction == "down":
        x= x_center + radius * math.cos(angle)
        y = y_center + radius * math.sin(angle)
        coordinates.append((-x, -y))
      else:
        raise ValueError("direction must be: right, left, top or bottom")
    return coordinates

# Ying-Yang Design: Using the robot script offered by the node

# Green parts
  # Green middle circle boundary 
  pipette_20ul.pick_up_tip()

  green_big_circle = create_semicircle(0, 0, 20, 40, "right")

  for x,y in green_big_circle:
    adjusted_location = center_location.move(types.Point(x=x, y=y))
    if pipette_20ul.current_volume == 0:
      pipette_20ul.aspirate(1, location_of_color("Green"))
    dispense_and_detach(pipette_20ul, 1, adjusted_location)

  # Semicircle which center is the middle inferior part of the circle
  green_S_semicircle = create_semicircle(0, 10, 10, 40, "left")

  for x,y in green_S_semicircle:
    adjusted_location = center_location.move(types.Point(x=x, y=y))
    if pipette_20ul.current_volume == 0:
      pipette_20ul.aspirate(1, location_of_color("Green"))
    dispense_and_detach(pipette_20ul, 1, adjusted_location)
  
  # Small circle whose center is at the center of the s_semicircle
  green_small_circle = create_circle(0, 10, 5, 20)

  for x,y in green_small_circle:
    adjusted_location = center_location.move(types.Point(x=x, y=y))
    if pipette_20ul.current_volume == 0:
      pipette_20ul.aspirate(1, location_of_color("Green"))
    dispense_and_detach(pipette_20ul, 1, adjusted_location)

  pipette_20ul.drop_tip()

# Red parts
  # Red middle circle boundary 
  pipette_20ul.pick_up_tip()

  red_big_circle = create_semicircle(0, 0, 20, 40, "left")

  for x,y in red_big_circle:
    adjusted_location = center_location.move(types.Point(x=x, y=y))
    if pipette_20ul.current_volume == 0:
      pipette_20ul.aspirate(1, location_of_color("Red"))
    dispense_and_detach(pipette_20ul, 1, adjusted_location)

  # Semicircle which center is the middle inferior part of the circle
  red_S_semicircle = create_semicircle(0, 10, 10, 40, "right")

  for x,y in red_S_semicircle:
    adjusted_location = center_location.move(types.Point(x=x, y=y))
    if pipette_20ul.current_volume == 0:
      pipette_20ul.aspirate(1, location_of_color("Red"))
    dispense_and_detach(pipette_20ul, 1, adjusted_location)
  
  # Small circle whose center is at the center of the s_semicircle
  red_small_circle = create_circle(0, -10, 5, 20)

  for x,y in red_small_circle:
    adjusted_location = center_location.move(types.Point(x=x, y=y))
    if pipette_20ul.current_volume == 0:
      pipette_20ul.aspirate(1, location_of_color("Red"))
    dispense_and_detach(pipette_20ul, 1, adjusted_location)

  pipette_20ul.drop_tip()

The final code allowed me to obtain the desired Yin-Yang Design

Post-Lab Questions

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

The article I found interesting is developed by Kverneland et al (2024). In this article an automated workflow is designed with the objective of preparing protein samples for LC-MS/MS Analysis. Using Opentons OT-2 robot Hela cells samples and plasma serum from patients were prepared with a shotgun approach to prepare the sample for the proteomic analysis. From this approach they analyzed 192 HeLa samples and consistently identified approximately 8000 protein groups and 130,000 peptide precursors. The importance of this study relies on the necessity of identifying and analyzing protein profiles of many samples. Proteomics approaches offer valuable information that can be used to discover novel biomarkers and contribute to the development of personalized treatments. Also, this article provides a potential approach to creating databases containing proteomic information that could be used for novel synthetic technologies like, for example, de novo design of proteins.

3. Write a description about what you intend to do with outomation tools Idea 1: An automated pipeline for De novo Design and Production of small neutralizing peptides against Bothrops atrox Venom Toxins

For this idea I designed a pipeline and identified the use of automation approaches in two key activities as shown below

The use of automated proteomic can help us to identify novel proteins with potential therapeutical applications and also identify protein families and relevant sequences, this sequences can be used for the desing of small neutralizing peptides that may be used against snake venoms. Since the design would produce a high amount of potential candidates it is necessary an automated process of protein synthesis. For this I identify the use of AI driven cell-free protein synthesis as promising aproach to produce and test possible candidates to neutralize snake venom toxins.

Final Project Ideas

I created three slides containing my three final project ideas using the lessons learned until now:

Presentation available below: Ver presentación en Google Slides