Week 1 HW: Principles and Practices

HW 1

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.

   A biological engineering application I am interested in developing is biological haptic actuators. I envision a future where one can fabricate haptic systems driven by living cells to mimic touch sensations. Through some external stimulus, this device could output vibrations to mimic touch. The scenario I am presenting replaces electromechanical systems with biologically powered interfaces.

   In current fabrication pipelines, the output is structural (eg. 3D Printing). To add interactivity, components need to be added at a later stage (microcontrollers + sensors + motors). This co-location of sensing, computation and actuation is built-in in biological systems. Furthermore, as devices are integrating with the user (we are currently at the wearable stage) current fabricated objects may be considered “clunky” whereas purely biological interfaces can be streamlined. Tools such as AlphaFold make it easier for users to author and design proteins and I am curious to see if it is possible to fabricate a biological actuator. If touch is not feasible I am open to exploring other output modalities such as color.

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.

   Sustainability, Safety and Agency are the biggest considerations regarding my selected application or tool. Current haptic devices are external objects to the user. Safety considerations should consider where the device is located such as handheld vs wearable. Constant interaction with the skin can potentially induce irritations to the user. Furthermore, once the device is no longer usable, policy considerations should also prioritize methods to safely dispose and recycle such systems. Throwing away objects that use biological systems to drive outputs can be hazardous to the ecosystem.

   One trend in interfaces is the movement beyond wearables to systems that interface directly into the user. In the case of implantables/consumables, extra precaution is needed prior to deploying such systems at a larger scale. Haptic devices (through electro muscle stimulation) and AI systems introduce novel interaction paradigms where the user does not have agency over the outputs of the interaction. The introduction of biological systems will also have agency ramifications in the context of interaction.

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?

   Action 1: I will focus on biolab/makerspace leadership for Action 1. The action that this actor should take is rigorous safety onboarding for new members and ethical considerations when designing biological technologies. After onboarding, any new projects/prototypes need to undergo an IRB-like approval process. My proposal is to make a communication channel for all biolab spaces, in which for one to pursue a project in lab A, it must be approved by someone in an external lab B. Purpose: As biolab spaces and computational tools such as AlphaFold are becoming more accessible to the general public it is important that the onboarding process communicates ethical considerations and the safety of handling biomaterials. Similar to personal fabrication (makerspaces etc.) users can fabricate objects ranging from fidget spinners to weapons without institutional/external approvals. Necessary governing policies need to be enacted that balances the need to rapid prototype and safety. Currently, regulation is done internally which may bias what gets built. Additionally a degree of familiarity with the staff can lead to less “scruntinization”. Design: Bio makerspaces are unlike academia where there are no external auditors. For this to work, all multiple maker-spaces need to buy-in. Oftentimes these maker-spaces are run by volunteers and this approval process adds additional labor. The user interested in the project could pay a small fee (similar to college admissions) to incentivize the volunteers to read through the application. Assumptions: I am assuming that the approval process is efficient and that the small fee is enough to incentivize the external reviewers to read through the project. I am also assuming that a penalizing mechanism is not necessary.

   Action 2: Federal Regulators are my actor for Action 2 to tackle the issue of user agency. This action would be a law that enforces that bio-interfaces must be removable non-invasibly. Purpose: The purpose of this action is to ensure that the user intent aligns with the output of the device and prevent unintended consequences. By allowing users to remove the device (similar to a wearable), the user can decide when they are ok with interacting with biological interfaces. This law would discourage implantable interfaces. Open-sourceness also prevents vendor lock-in and allows users to “debug” the circuitry. Design: To make this work, regulators would need buy-in from other government agencies and the general population. Additionally, the corporations that manufacture the device must also comply with the law.
   Assumptions: I am assuming that the zeitgeist is in favor of using this bio-interface. Social norms will dictate whether or not the general population will agree with this action. If the utility of a discrete implantable system exceeds the risk and creates a significant advantage, then there may not be an incentive to pass this law. Risks of Failure and Success: If the cost to implement the implant is lower than the wearable companies will push back on the way this law is written. Furthermore, by focusing this law to discourage implants, if this device is successful then there is a gap in how to dispose of the system.

Action 3: Academic researchers establish mandatory dual-use risk registries for biological interface projects. Purpose: The purpose of this registry is for researchers to prevent researchers from developing bio-weapons. The registry serves purposes. The first is for the researcher to reflect through the design process. The second is to allow external audits. Design: I envision an online portal where prior to starting a research project, the researcher will fill out a form. When designing bio-experiments, the researcher must disclose all funding sources and collaboration to ensure that no bad actors are involved. Additionally high-risk design choices (eg. non-conventional, unexplored) must be disclosed. In the context of haptic devices, actuation/vibration above some threshold should raise flags since the body can only perceive a certain amount. Assumptions: The environment that the researchers operate in will be conducive to this change. Researchers will cooperate with each other (even in a world where funding is tight). Risks of Failure and Success: In a high pressure publish or perish environment, this adds an additional bureaucratic layer they must go through. Researchers may underreport to perform experiments faster. If this is successful, external actors (non academic) may access the open source registry and reverse engineer successful designs for malicious use.

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. I would prioritize option 1. By making biohacker makerspaces more accessible, it has made it easier for any individual to prototype. Unlike academia, and corporations the people that utilize these spaces are hobbyists. It is difficult to regulate activities when people do it for “fun”. Furthermore, friendships are formed when frequenting a location. By adding a proposal step and asking approval from other makerspaces, an informal approval process is created. Even though this increases prototyping time, it ensures an unbiased review. The relevant audience would be other hackerspace leadership and small partnerships could be formed.

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. I am new to biology (last time I did this was in highschool) so I am not familiar with the mechanisms and thus may have proposed something not feasible. One reflection I have is that my answers (and slides) reflect an anthropocentric view. The ethical perspective of non-humans and even the biological system that would realize these systems are not explored. If I had that perspective I would answer these questions differently. A governance action that I would have proposed is to compare the benefits of using biology as a substitute for an electromechanical system (at least from a government perspective). The would state law that that if an equal design works electromechanically then a biology-driven device should not be used.

AI Usage:

“Critique the below response: “

I then iteratively update my answer by adding details.

Assignment

Professor 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?

   Error Rate: 1e-6. There is 3.2gbp (3.2e9). The error rate is much smaller (a factor of 1e17) compared to the length of the human genome. It is essentially near 0 error and that is how biology handles that discrepancy by doing a proofreading step.

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 don’t work to code for the protein of interest?

   The average human protein is 1036 bp. With 3bp in a codon for an amino acid this would yield around 345 amino acids. There are 4 possible sequences, so 4^{345} is the number of ways to code DNA.

Dr.LeProust

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

   Coupling with phosphoramidite, Capping unreacted sites, Oxidation, Deblock. Repeat until done.

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

   I’m assuming error rates in the process? Error is fine but as you increase the length the rate of error can be “felt” more.

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

   The chip dimension is limited? It is also an imperfect process (there is an error rate) and deformations may result in a non-sharp clean peak.

George Church

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

   arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine From Google, the lysine contingency is a reference to Jurassic Park. The dinosaurs at the Park are unable to produce lysine and thus need to consume lysine supplements to stay alive. This prevents the dinosaurs from escaping the park. Since it is an essential amino acid, it can not be synthesized fast enough and would need external supplements. The plan in Jurassic Park doesn’t work since all animals can not produce lysine fast enough and need to consume it.

Week 2 HW: DNA Read, Write, & Edit

Part 1: Benchling & In-Silico Gel Art

Using Benchling, I imported the Lambda DNA sequence and simulated restriction enzyme digestions with EcoRI, HindIII, BamHI, KpnI, EcoRV, SacI, and SalI. The goal was to design a gel art pattern inspired by Paul Vanouse’s Latent Figure Protocol.

The design depicts a figure in a rocky victory pose with a crying face, using 5 lanes of selected enzyme combinations to form the image.

Full virtual digest — all 7 enzymes (reference):

Gel art design — victory pose with crying face:

The final design uses the following lanes:

• Lane 1: Lambda_NEB - BamHI
• Lane 2: Lambda_NEB - EcoRV
• Lane 3: Lambda_NEB - KpnI
• Lane 4: Lambda_NEB - EcoRV
• Lane 5: Lambda_NEB - BamHI

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

We performed the wet-lab restriction digest and gel electrophoresis experiment designed in Part 1, following the Gel Art protocol. Photos of the gel were taken and will be uploaded shortly.

In progress — wet lab gel photos to be uploaded from Discord.

Part 3: DNA Design Challenge

3.1 Protein of Interest

I selected beta-casein (Bos taurus), a milk protein. Accession: UniProt P02666

3.2 Reverse Translation

The protein sequence was reverse translated to DNA using SMS2 Reverse Translate:

atgaaagtgctgattctggcgtgcctggtggcgctggcgctggcgcgcgaactggaagaa
ctgaacgtgccgggcgaaattgtggaaagcctgagcagcagcgaagaaagcattacccgc
attaacaaaaaaattgaaaaatttcagagcgaagaacagcagcagaccgaagatgaactg
caggataaaattcatccgtttgcgcagacccagagcctggtgtatccgtttccgggcccg
attccgaacagcctgccgcagaacattccgccgctgacccagaccccggtggtggtgccg
ccgtttctgcagccggaagtgatgggcgtgagcaaagtgaaagaagcgatggcgccgaaa
cataaagaaatgccgtttccgaaatatccggtggaaccgtttaccgaaagccagagcctg
accctgaccgatgtggaaaacctgcatctgccgctgccgctgctgcagagctggatgcat
cagccgcatcagccgctgccgccgaccgtgatgtttccgccgcagagcgtgctgagcctg
agccagagcaaagtgctgccggtgccgcagaaagcggtgccgtatccgcagcgcgatatg
ccgattcaggcgtttctgctgtatcaggaaccggtgctgggcccggtgcgcggcccgttt
ccgattattgtg

3.3 Codon Optimization

The reverse translated sequence was codon optimized for E. coli expression using VectorBuilder Codon Optimization:

ATGAAAGTGCTGATTCTGGCATGCCTGGTGGCGCTGGCGCTGGCACGTGAACTGGAAGAACTGAATGTTCCGGGCGAAATTGTGGAAAGCCTGAGCAGCAGCGAAGAAAGCATTACCCGCATTAATAAAAAAATTGAAAAATTTCAGAGCGAAGAACAGCAGCAGACCGAAGATGAACTGCAGGATAAAATTCATCCGTTTGCGCAGACCCAGAGCCTGGTGTACCCGTTTCCGGGCCCGATTCCGAACAGCCTGCCGCAGAACATTCCGCCGCTGACCCAGACCCCGGTGGTGGTGCCGCCGTTTCTGCAGCCGGAAGTGATGGGCGTCAGCAAAGTGAAAGAAGCCATGGCGCCGAAACACAAAGAAATGCCGTTTCCGAAATATCCGGTGGAACCGTTTACCGAATCACAGTCACTGACCCTGACCGATGTGGAAAACCTGCACCTGCCGCTGCCGCTGCTGCAGTCGTGGATGCACCAGCCGCACCAGCCGCTGCCGCCGACCGTCATGTTTCCGCCGCAGAGCGTGCTGAGCCTGTCACAGAGCAAAGTGCTGCCGGTGCCGCAGAAAGCGGTGCCGTATCCGCAGCGTGACATGCCGATTCAGGCGTTCCTGCTGTATCAGGAACCGGTGCTGGGCCCGGTTCGTGGCCCGTTTCCGATTATTGTG

Codon optimization translates a DNA sequence to match the codon preferences of a target organism. Beta-casein is naturally expressed in bovines (Bos taurus), but here I optimized for E. coli since it is a different organism with different preferred codon usage. By remapping the codons, we maximize translational efficiency and protein yield in the new host.

3.4 Protein Production Technologies

With the codon-optimized sequence, the protein can be produced via two approaches:

• Cell-dependent: The codon-optimized DNA is introduced into E. coli cells (e.g. via a plasmid). The cell’s own transcription and translation machinery reads the sequence and produces the protein.
• Cell-free: The necessary cellular components (ribosomes, tRNA, polymerases, etc.) are extracted and used in vitro to transcribe and translate the DNA into protein, without needing a living cell.

3.5 Transcriptional Protein Diversity (Optional)

Skipped.

Part 4: Twist DNA Synthesis Order

Following the Twist tutorial, I built an expression cassette in Benchling containing a promoter, RBS, start codon, codon-optimized coding sequence, 7x His tag, stop codon, and terminator. The linear sequence was then uploaded to Twist to prepare a clonal gene order, and the resulting construct was imported back into Benchling to verify the plasmid design.

Step 1 — Linear expression cassette in Benchling (924 bp):

Step 2 — Uploaded to Twist (clonal gene order):

Step 3 — Final circular plasmid verified in Benchling (3145 bp, pTwist Amp High Copy):

Part 5: DNA Read/Write/Edit

5.1 DNA Read

In progress.

5.2 DNA Write

In progress.

5.3 DNA Edit

In progress.

Week 3 HW: Opentrons & Lab Automation

Assignment 1: Python Script for Opentrons Artwork

I designed an Indonesian cloud pattern called Megamendung using the Opentrons pipetting robot. The pattern uses two colors — red (mRFP1) and green (Azurite) — arranged as interlocking arcs to replicate the traditional Javanese batik motif.

Simulated output of the Python script:

Python script:

from opentrons import types
metadata = {    # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata
    'author': 'Aditya Retnanto',
    'protocolName': 'Megamendung',
    'description': 'Indonesian Cloud Pattern',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

##############################################################################
###   Robot deck setup constants - don't change these
##############################################################################
TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'
well_colors = {
    'A1' : 'Red',
    'B1' : 'Green',
    'C1' : 'Orange'
}

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
    ##############################################################################

    # 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.
        """
        assert(isinstance(volume, (int, float)))
        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)

    dispense_amount = 0.5
    mrfp1_points = [(-4, 16),(4, 16),(-16, 12),(-8, 12),(8, 12),(16, 12),(-24, 4),(-12, 4),(-4, 4),(4, 4),(12, 4),(24, 4),(-24, -4),(24, -4),(-16, -12),(-8, -12),(8, -12),(16, -12),(-4, -16),(4, -16)]
    azurite_points = [(0, 16),(-12, 12),(12, 12),(-20, 8),(-4, 8),(4, 8),(20, 8),(-28, 0),(-8, 0),(8, 0),(28, 0),(-20, -8),(20, -8),(-12, -12),(12, -12),(0, -16)]

    pipette_20ul.pick_up_tip()
    total_aspiration = len(mrfp1_points) * dispense_amount
    pipette_20ul.aspirate(total_aspiration, location_of_color('Red'))
    for i in range(len(mrfp1_points)):
        adjusted_location = center_location.move(types.Point(x=mrfp1_points[i][0], y=mrfp1_points[i][1]))
        dispense_and_detach(pipette_20ul, dispense_amount, adjusted_location)
    pipette_20ul.drop_tip()

    pipette_20ul.pick_up_tip()
    total_aspiration = len(azurite_points) * dispense_amount
    pipette_20ul.aspirate(total_aspiration, location_of_color('Green'))
    for i in range(len(azurite_points)):
        adjusted_location = center_location.move(types.Point(x=azurite_points[i][0], y=azurite_points[i][1]))
        dispense_and_detach(pipette_20ul, dispense_amount, adjusted_location)
    pipette_20ul.drop_tip()

Assignment 2: Post-Lab Questions

Published Paper on Lab Automation

Automation for Final Project

Assignment 3: Final Project Ideas

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project