Week 1 HW: Principles and Practices

Scale-up of nanocapsules for drug delivery using bacteria as ferritin manufacturers

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

Biologics are drugs synthesized by living organisms, which have gained more notoriety throughout the years (Walsh, 2018). Cancer drugs and vaccines are some of the achievements scientists have accomplished with biotechnology. This is a novel area with increasing knowledge and endless applications. Currently, iron deficiency is one of the main global issues affecting overall health (Lee et al., 2025). This project aims to develop a drug delivery system using bacteria-made ferritin, given the popularity and extended use of these microorganisms over the years for drug manufacturing (Kulkarni, 2026).

As I entered my senior year in university, I wanted to work on topics related to drug development or delivery, and scaling that process up, which is why I was doing some research for my bachelor’s thesis. However, I ended up reaching a different path due to life, but here I am trying to learn and see how far I can take this idea.

2. Describe one or more governance policy goals related to ensuring this application contributes to an ethical future & prevents harm.

• Ensure quality in scaled production: Ensure that the biologic product complies with Good Manufacturing Practices (GMP).

• Prevent non-maleficence in biomanufacturing: Avoid harmful use of bioengineered bacteria.

• Foster and promote innovation and global access: Enable technology transfer to low-resource settings.

3. Describe at least three different potential governance actions by considering the purpose, design, assumptions, and risks of failures & “success”.

Below is a table with the three main governance actions:

Table 1. Governance actions

4. Score each of your governance actions against your rubric of policy goals

Table 2. Scoring

5. Based on scores, describe which governance option or combination of options you would prioritize, and why.

After reviewing the different options and their scores, the most reasonable combination of options to prioritize would be Standardized GMPs and Traceability of Biological Products. The former is selected due to its strong impact on both productivity and product quality, as well as its capacity to establish clear guidelines that ensure biological safety for both production staff and consumers. The latter is essential because traceability enables the identification of errors and deviations throughout the production process, allowing them to be corrected in a timely manner and ensuring the ethical and responsible use of this technology.

References

Walsh, G. (2018). Biopharmaceutical benchmarks 2018. Nature Biotechnology, 36, 1136–1145. https://www.nature.com/articles/nbt.4305

Lee, G. R., et al. (2025). Global burden of iron deficiency and its impact on health. Nature Medicine. https://www.nature.com/articles/s41591-025-03624-8

Kulkarni, S. (2026). Engineered microbes as API manufacturers in pharma. Pharma Now. https://www.pharmanow.live/knowledge-hub/research/engineered-microbes-api-manufacturing-pharma

Week 2 HW: DNA Read, Write, and Edit

HOMEWORK

Part 1: Benchling & In-silico Gel Art

Below are some screenshots from the steps followed to create a basic pattern:

Step 1: The sequence is imported from the webpage to Benchling.

Step 2: The digest function is shown as a test with EcoRI as the chosen restriction enzyme.

Step 3: The process is repeated using enzymes requested in the homework, and the result is the following:

Step 4: The process is repeated now using different enzymes requested in the homework to create a different pattern. In this case, the pattern might look like a series of barcodes, using the enzymes shown in figure 7. Since they cut in different sites, they create a lot of short DNA fragments which scatter across the gel, givin the impression of a barcode. This enzyme behavior could be used as a biomarker perhaps in, although this idea needs further development:

Part 3: DNA Design Challenge

3.1. Choose your protein

In this case, I chose the Transcription Initiation Factor 3 (TIF-3) encoded in the gene infC, which is a relatively short protein that is involed in the translation process (Gutu et al., 2013), (Arenz & Wilson, 2016). Modifying the structure of this protein may be crucial to combat antibiotic resistance.

By going to UniProt, the aminoacid sequence is the following:

//

tr|A0A8S0FV27|A0A8S0FV27_ECOLX Translation initiation factor IF-3 OS=Escherichia coli OX=562 GN=infC PE=3 SV=1 MSLREALEKAEEAGVDLVEISPNAEPPVCRIMDYGKFLYEKSKSSKEQKKKQKVIQVKEI KFRPGTDEGDYQVKLRSLIRFLEEGDKAKITLRFRGREMAHQQIGMEVLNRVKDDLLRRT GSGRILPNEDRRPPDDHGARS

3.2 and 3.3: Reverse translation and codon optimization

Going bak to Benchling, the AA sequence was imported and the function back translate was used to obtain the DNA sequence which is already optimized given the figures below:

// DNA optimized sequence: ATGAGTTTACGTGAAGCACTGGAAAAAGCGGAAGAAGCCGGTGTTGATCTGGTCGAAATCAGTCCTAATGCAGAACCCCCGGTGTGCCGTATCATGGACTATGGCAAATTCCTCTACGAGAAATCTAAAAGCTCAAAGGAACAAAAAAAGAAACAGAAGGTTATTCAGGTCAAAGAGATTAAGTTTCGACCGGGGACTGACGAAGGAGACTATCAAGTGAAACTTCGCTCCTTGATTCGCTTCCTGGAAGAGGGGGATAAAGCGAAAATTACCCTGCGCTTTCGCGGCAGAGAGATGGCCCACCAGCAGATCGGCATGGAAGTATTGAACCGTGTGAAAGATGACTTACTGCGTCGCACGGGTAGCGGTCGTATACTGCCAAACGAGGATCGCCGGCCGCCGGATGATCATGGCGCTCGGTCG

The organism selected for this protein is E. Coli due to its wide use in biotechnology. The codons had to be optimized due to the fact that the cellular machinery may differ from one bacteria to another. This means that a bacteria other than E. Coli might express this protein at a different rate and intensity. In this case, the protein was obtained from E. Coli based on UniProt, but since E. Coli contains different strands, codon optimization still would have to be performed.

3.4 and 3.5: Production technologies and alignment

I would use host cells since the chosen protein is from a bacteria en E. Coli is a common microorganism used for these purposes. This technique has a much lower cost thant using cell-free systems in which all the cellular components have to be supplied.

The alignment is shown in the following figures

Part 4: Prepare a Twist DNA Sequence Order

Step 1: DNA Sequence

The same DNA linear sequence was already obtained in Part no. 3.

Step 2: Building the chasis

The parts were initially searched in iGEM, but the website shut down. Due to this, the parts provided in the homework were used.

The chasis now looks like this:

Step 4: Ordering in Twist

The process is shown in the figures below:

Step 5: Creating the plasmid

The process is shown in the figures below:

Finally, the plasmid is shown below:

Part 5: DNA Read/Edit/Write

5.1.1: What DNA would you want to sequence (e.g., read) and why?

I would like to analyze DNA from insects such as flies, since many species act as vectors for infectious diseases. By sequencing their DNA, I could identify genetic elements associated with viral transmission, pathogen resistance, or susceptibility. This information could help improve disease monitoring and vector control strategies.

5.1.2: In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why?

I would use second-generation sequencing technologies such as Illumina sequencing due to their high throughput, accuracy, and cost-effectiveness. Illumina sequencing is particularly efficient for short DNA fragments and allows the parallel sequencing of millions of reads, making it ideal for large-scale genomic analysis of insect populations.

5.2.1: What DNA would you want to synthesize (e.g., write) and why?

I would like to synthesize bacterial DNA initially because bacterial genomes are less complex than eukaryotic genomes, which makes them more manageable in terms of cost and laboratory procedures. This would allow me to gain experience with gene design and expression systems before working with more complex organisms.

5.2.2: What technology or technologies would you use to perform this DNA synthesis and why?

I would use common routes such as solid-phase phosphoramidite chemical DNA synthesis combined with gene assembly techniques. These methods allow precise synthesis of short oligonucleotides, which can then be assembled into longer DNA constructs. It is widely used, reliable, and scalable for constructing bacterial genes or plasmids.

5.3.1: What DNA would you want to edit and why?

I would edit DNA from mammalian cells, focusing on genes involved in the immune response. By modifying specific regulatory or coding sequences, it may be possible to enhance resistance to infectious diseases or better understand the mechanisms underlying autoimmune disorders. However, such research would need to be conducted carefully and ethically due to the potential implications of editing mammalian genomes.

PRE-LECTURE NOTES

Homework questions from Prof. Jacobson

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 error rate of polymerase is approximately 1 in a million nucleotides. Considering the human genome length of approximately 3.2 billion base pairs, or 6.4 billion nucleotides in a diploid cell, there would be roughly 6,400 errors per molecule of DNA, and half that number in a haploid cell. This implies a significant chance for defects or mutations to occur and potentially be passed down to offspring. However, biology has developed multiple mechanisms over the past century that increase the fidelity of DNA replication. For instance, MutS-1 is a protein shown to bind to mismatched DNA sequences. This mechanism therefore acts as an additional layer that improves the fidelity of de novo DNA synthesis (Carr et al., 2004).

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?

There are approximately 20ⁿ ways to code a human protein, where n represents the length of the protein. For instance, a typical protein may consist of 300 amino acids. Therefore, there are 20³⁰⁰ possible ways, which corresponds to an extremely large number of potential coding sequences (Alberts et al., 2002). Some of the reasons these codes do not work in practice include:

• Codon usage bias: The prevalence of a codon is related to its translation efficiency; some codons are translated faster than others. This impacts protein expression levels and availability (Chakravarty, 2026).

• Protein structure: Since proteins fold co-translationally, changes in codon usage can alter the timing of folding events, affecting protein structure and function (Moss et al., 2024).

Homework questions from Dr. LeProust

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

Currently, oligo synthesis is most commonly performed using phosphoramidite nucleosides as building blocks. This process consists of four main chemical reactions: detritylation, coupling, capping, and oxidation (Kosuri & Church, 2014).

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

The main challenge in synthesizing long oligonucleotides using standard phosphoramidite chemistry lies in cumulative yield loss and error accumulation. Unwanted reactions, such as depurination during detritylation, and incomplete removal of protecting groups can leave gaps in the oligo backbone, reducing overall yield. In addition, single-base deletions are the predominant errors caused by inefficiencies during these reaction steps.

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

Manufacturing an oligo of this length is highly prone to errors due to several factors. First, oligo concentrations obtained from a selected pool after processing are often quite low, reducing assembly efficiency. Second, when synthesizing large numbers of oligos, overlapping coding regions may introduce assembly errors at scale. Finally, significantly higher costs are required to produce the large number of strands necessary for successful gene assembly.

Homework question from George Church

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

The ten essential amino acids in most animals are:

Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, Valine, and sometimes Cysteine or Tyrosine, depending on species-specific metabolic capabilities (Hou and Wu, 2018).

Understanding the Lysine Contingency as a bioengineered constraint, the dependence of animals on multiple essential amino acids further strengthens this strategy. This dependency enables researchers to implement safer in vivo containment systems, as organisms lacking access to these amino acids are unable to survive outside controlled environments (Shivni, 2023).

References

Carr, A. M., Lambert, S., & Replication Stress Group. (2004). Mismatch repair proteins and DNA replication fidelity. Nucleic Acids Research, 32(20), e162. https://academic.oup.com/nar/article/32/20/e162/1115791

Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., & Walter, P. (2002). Molecular Biology of the Cell (4th ed.). Garland Science. https://www.ncbi.nlm.nih.gov/books/NBK26830/

Chakravarty, A. (2026). What is codon bias? GoldBio. https://www.goldbio.com/blogs/articles/what-is-codon-bias

Moss, A. J., et al. (2024). Codon usage and protein folding dynamics. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC11227313/

Kosuri, S., & Church, G. M. (2014). Large-scale de novo DNA synthesis. Nature Methods, 11, 499–507. https://www.nature.com/articles/nmeth.2918

Hou, Y., & Wu, G. (2018). Nutritionally essential amino acids in animals. Advances in Nutrition, 9(6), 849–858. https://doi.org/10.1093/advances/nmy054

Shivni, R. (2023). A pioneer of the multiplex frontier. The Scientist. https://www.the-scientist.com/a-pioneer-of-the-multiplex-frontier-71132

Week 3 HW: Lab Automation

Assignment no. 1: Python Script for Opentrons Artwork

The code used was the following to create a simple swirl pattenr with four different colors:

from opentrons import types
import math

metadata = {
    'author': 'Jean Colmenares',
    'protocolName': 'Agar Swirl Pattern - 4 Colors',
    'description': 'Swirl pattern with four colors per branch',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

well_colors = {
    'A1': 'Red',
    'B1': 'Green',
    'C1': 'Orange',
    'D1': 'Blue'
}

def run(protocol):

    tips_20ul = protocol.load_labware(
        'opentrons_96_tiprack_20ul',
        TIP_RACK_DECK_SLOT,
        'Opentrons 20uL Tips'
    )

    pipette_20ul = protocol.load_instrument(
        "p20_single_gen2",
        "right",
        [tips_20ul]
    )

    temperature_module = protocol.load_module(
        'temperature module gen2',
        COLORS_DECK_SLOT
    )

    temperature_plate = temperature_module.load_labware(
        'opentrons_96_aluminumblock_generic_pcr_strip_200ul',
        'Cold Plate'
    )

    color_plate = temperature_plate

    agar_plate = protocol.load_labware(
        'htgaa_agar_plate',
        AGAR_DECK_SLOT,
        'Agar Plate'
    )

    center_location = agar_plate['A1'].top()

    pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

    # ------------------------------------------------------------------
    # Helper functions
    # ------------------------------------------------------------------

    def location_of_color(color_string):
        for well, color in well_colors.items():
            if color.lower() == color_string.lower():
                return color_plate[well]
        raise ValueError(f"No well found with color {color_string}")

    def dispense_and_detach(pipette, volume, location):
        above_location = location.move(types.Point(z=location.point.z + 5))
        pipette.move_to(above_location)
        pipette.dispense(volume, location)
        pipette.move_to(above_location)

     # ------------------------------------------------------------------
    # SWIRL PATTERN — BIG + FIXED COLOR PER BRANCH (P20 SAFE)
    # ------------------------------------------------------------------

    DROP_VOLUME = 3

    branches = 4
    points_per_branch = 24

    radius_start = 3
    radius_step = 1.6
    angle_step = math.pi/9

    branch_colors = ['Red', 'Green', 'Orange', 'Blue']

    for branch in range(branches):

        base_angle = branch * (2*math.pi/branches)
        color = branch_colors[branch]
        source = location_of_color(color)

        for i in range(points_per_branch):

            pipette_20ul.pick_up_tip()

            pipette_20ul.aspirate(DROP_VOLUME, source.bottom(1))

            angle = base_angle + i * angle_step
            radius = radius_start + i * radius_step

            x = radius * math.cos(angle)
            y = radius * math.sin(angle)

            loc = center_location.move(types.Point(x=x, y=y, z=0))
            dispense_and_detach(pipette_20ul, DROP_VOLUME, loc)

            pipette_20ul.drop_tip()

The pattern is shown below:

Assignment no.2: Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

Automated Strain Construction for Biosynthetic Pathway Screening in Yeast Astolfi et al., 2025

In this study, the researchers programmed a Hamilton Microlab VANTAGE liquid-handling robot (a high-end automation platform, not an Opentrons) to integrate with additional on- and off-deck hardware (e.g., thermocyclers, plate sealers, colony pickers) via its central arm. Together with custom software and a user interface developed in the Hamilton VENUS environment, this system automated key steps in yeast strain construction such as transformation setup, heat-shock, washing, and plating.

This automated workflow achieved a throughput of up to ~2,000 transformations per week, enabling high-throughput construction and screening of libraries of engineered yeast strains. As a proof of concept, the team applied the system to screen gene variants within a biosynthetic pathway for the plant alkaloid precursor verazine. They identified several genes that significantly increased pathway product titers, demonstrating the utility of automated strain construction for rapid pathway discovery and optimization.

Assignment no. 3: Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details.

For my first idea, using a liquid-handling robot like the ones at Opentrons would be a crucial step in figuring out the right enzyme concentration for dye breakdown.