I’m an industrial designer currently pursuing a MDE Degree at Harvard. In my practice, i’m focused on multidisciplinary research - utilizing digital & traditional fabrication methods, computational methodologies, philosophy and critical thinking - I observe, dismantle and reconstruct the concepts I’m working with, using whimsical and subversive motives to question the ordinary and unearth speculative near-futures. I believe that design is a tool to address social , environmental & economic wickedities, advancing us towards a more holistic and responsible approach towards just being and being just.
First, describe a biological engineering application or tool you want to develop and why.
I’m curious about cellular agriculture and lab-grown meat. in this project im proposing to develop a living, light-activated scaffold that produces and spatially distributes oxygen inside a growing tissue. this is one of the needed early steps toward making a thick, steak-like cut rather than thin sheets or ground meat. Today, animal cells grown for food struggle beyond a few millimeters because oxygen and nutrients don’t diffuse well. the interior becomes starved and dies unless you add complex and expensive hardware. My project reframes that bottleneck as a biological engineering opportunity: a biofabricated “breathing” matrix that couples geometry + metabolism so that illumination drives localized oxygen generation and makes it visible and tunable. In this course’s context it would be explored as a living installation: a translucent scaffold whose oxygen field can be visualized in real time under light/dark cycles, producing both data and an intuitive, aesthetic demonstration of how engineered living materials might reduce reliance on expensive hardware in future cultivated-meat systems.
for this week’s HW assignment I’ve chosen the protein mCherry. mCherry is a protein that expresses in red flourescent light emmitance. Fusing it to another protein will enable use to discern wheter the ‘other’ protein is expressed, by visibly observing the red flourescent light. essentially, mCherry functions as a global process visualisation tool across multiple SynBio applications.
Googling mCherry Protein I arrived at the uniprot.org database where I’ve obtained the mCherry sequence:
Subsections of Homework
Week 1 HW: Principles and Practices
1. First, describe a biological engineering application or tool you want to develop and why.
I’m curious about cellular agriculture and lab-grown meat. in this project im proposing to develop a living, light-activated scaffold that produces and spatially distributes oxygen inside a growing tissue. this is one of the needed early steps toward making a thick, steak-like cut rather than thin sheets or ground meat. Today, animal cells grown for food struggle beyond a few millimeters because oxygen and nutrients don’t diffuse well. the interior becomes starved and dies unless you add complex and expensive hardware. My project reframes that bottleneck as a biological engineering opportunity: a biofabricated “breathing” matrix that couples geometry + metabolism so that illumination drives localized oxygen generation and makes it visible and tunable. In this course’s context it would be explored as a living installation: a translucent scaffold whose oxygen field can be visualized in real time under light/dark cycles, producing both data and an intuitive, aesthetic demonstration of how engineered living materials might reduce reliance on expensive hardware in future cultivated-meat systems.
2. 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.
Governance policy goal #1: ensuring Biosafety and non-malfeasance
Make sure the system can’t cause harm to people, ecosystems, or lab staff through accidental release, contamination, or unsafe handling.
Making sure every engineered organism used in the project is not viable outside of the controlled lab conditions.
Contamination monitoring and incident reporting standards for all project related activities in the wet lab.
Use ‘Low Risk’ Chassis Organisms and avoid incorporating traits that increase survivability of harmful actions.
Governance policy goal #2: maximize public benefit
directing this tool toward clear societal value—lowering barriers to safer, more resource-efficient cellular agriculture research and accelerating pathways to scalable cultivated meat.
Define ‘Constructive Use’ criteria and require study to explicitly show qualitative improvement over one of the following criteria : lower resource use, improving oxygen diffusion limits, reducing complexity of tissue cultivation, reducing cost of tissue cultivation.
Encourage standardized open-source documentation for non-sensitive aspects like negative results and measurement methods.
Ensure this tool’s benefits are broadly shared rather than concentrated, and that people retain meaningful choice and informed consent.
cultural and livelihood impacts: include early stakeholder perspectives (food cultures, labor/farming communities) to reduce the risk that “technical success” drives social harm or displacement.
if such a risk is assesed as high, co-develope a slow transition plan.
3. Describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”).
Action 1: Project specified containnment regime
Purpose: Replace generic lab norms with a project-specific containment regime so accidental release and unsafe handling are structurally less likely.
Design: A mandatory SOP covering labeling, storage, transport, and validated inactivation/disposal for every culture/run. chain-of-custody log for strains and materials.
Assumptions: Containment does not undermine biological performance.
Risks: Failure - checkbox compliance or incomplete logs, resulting in bad lab norms and culture. Success risk - issue of a compliance-overhead that will evemtually become a barrier for smaller teams unless tooling/templates reduce burden.
Action 2: Pre-registered public-benefit targets
Purpose: Ensure the work advances constructive uses by tying it to explicit, testable public-benefit goals rather than novelty.
Design: Before experiments, declare 1–2 primary public-benefit targets (reduced process complexity/reduced resource use) and define how they will be evaluated. after, conduct a brief impact evaluation including tradeoffs and limits.
Assumptions: Labs track real constraints; teams won’t optimize for non-beneficial metrics.
Risks: Failure - metric gaming or proxies that don’t translate. Success risk - the encourageing or incentivizing a ‘follower’ culture where the first metrics to have consenseus are repeated as defaults.
Action 3: stakeholder review & benefit-sharing
Purpose: Ensure “success” does not override cultural values, informed consent, or fairness in who benefits from the technology.
Design: A stakeholder checkpoint with at least two external perspectives (labor/farming and food culture/ethics) plus explicit benefit-sharing commitments (open standards, non-exclusive licensing norms, accessible documentation, and clear communication of uncertainties to support informed choice).
Assumptions: Early stakeholder engagement surfaces blind spots. benefit-sharing commitments foster trust between community and research/venture.
Risks: Failure risk -tokenism or performative consultation. Success risk - added friction slows iteration—but that is an intentional tradeoff to protect autonomy and prevent concentrated capture.
Action 4: responsible release of documentation
Purpose: Maximize reproducibility and shared learning while reducing misuse risk.
Design: Two publication layers: Open (concept, results, non-sensitive documentation) and Restricted (step-by-step replication details and other speceficities). an external mechanism decides classification of research documents. access to the Restricted layer will be granted by same mechanism.
Assumptions: Sensitive details can be identified; restriction won’t destroy scientific value.
Risks: Failure risk - misassuming the layer definitions as too open (misuse) or too closed (no benefit). Success risk - restricted knowledge becomes a chokepoint that concentrates power and limits equitable access.
Score (from 1-3 with, 1 as the best) each of your governance actions against your rubric of policy goals.
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.
I would prioritize a combined governance package aimed at two audiences:
First would be an institutional biosafety governance body (such as an IBC) and the second would focus on field-facing actors (research funders, journals, and cultivated-meat research networks). The core package is Action 1 (Containment Regime), Action 2 (Pre-registered public-benefit targets), and Action 3 (Stakeholder review & benefit sharing). I would start with Action 1 because it most directly reduces non-malfeasance risks (accidental release, unsafe handling, or unmanaged contamination). Action 2 ensures the project is oriented toward constructive use by requiring explicit evidence of benefit rather than novelty alone. Action 3 is prioritized early because success in this domain can create downstream social impacts—such as concentration of ownership or displacement pressures—so stakeholder input and benefit-sharing commitments are necessary to protect autonomy and legitimacy.
The primary trade-off is speed and ease of iteration vs. safety, accountability, and equity. These actions add overhead (documentation, evaluation, review), and if implemented too rigidly they could become barriers for smaller teams. the assumption is that this can be mitigated with lightweight templates and clear defaults. A key uncertainty is whether lab-scale proxies for oxygen distribution translate to real thick-tissue outcomes. Action 2 should make a clear and defined outline through central—pre-registering of what counts as improvement, to structured and goal-oriented claims rather than post-hoc storytelling. Overall, this prioritized set aims to make the project safe by default, oriented toward public benefit, and socially accountable if it succeeds.
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?
Polymerase makes about 1 error per 106 bases. The human genome is about 3.2×109 bp, so a genome-length copy would imply roughly ~3,200 errors. Biology closes that gap by layering proofreading and post-replication mismatch repair on top of polymerase.
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?
Because many amino acids have multiple equivalent codons, the number of possible DNA sequences encoding the same protein is huge. In practice, many variants fail because the codon choice will change translation efficiency and because sequence composition changes mRNA structure.
Dr. LeProust’s Questions:
What’s the most commonly used method for oligo synthesis currently?
The most common method is solid-phase phosphoramidite chemical synthesis. It builds DNA one nucleotide at a time on a solid support through repeated cycles (coupling, capping, oxidation, deblock).
Why is it difficult to make oligos longer than 200nt via direct synthesis?
Because each added base requires another chemical cycle, small inefficiencies and side reactions add-up over hundreds of cycles.
Why can’t you make a 2000bp gene via direct oligo synthesis?
A 2000 bp gene would require ~2000 sequential synthesis cycles, making correct full-length yield very low because errors and truncations will become significant along the process.
Prof. Church’s Question:
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 EAAs in all animals are Arginine, Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, and Valine.
To my understanding ‘The Lysine Contingency’ was a strategy in the ‘Jurassic Park’ fiction implemented by Henry Wu to disable dinosaurs’ ability to create Lysine by themselves, thus forcing them to obtain it through supplements provided by the park, or die.
Since I now know no known-animal has the ability to self-produce the EAA Lysine, this renders as complete nonsense - because the dinosaurs did not have the ability to create the Lysine in the first place. The concept of a kill-switch however, still stands valid as a bio-safety measure.
for this week’s HW assignment I’ve chosen the protein mCherry. mCherry is a protein that expresses in red flourescent light emmitance. Fusing it to another protein will enable use to discern wheter the ‘other’ protein is expressed, by visibly observing the red flourescent light. essentially, mCherry functions as a global process visualisation tool across multiple SynBio applications.
Googling mCherry Protein I arrived at the uniprot.org database where I’ve obtained the mCherry sequence:
Using the Gensmart Codon Opt Tool to optimise the above codon seq for E.coli. I chose to opt for E.coli because it is my understanding that this organism serves as a common platform for SynBio uses, and beacuse i’m new to the field, I rather stick to common practices to solidify my understanding when taking first steps in prot-design.
Releying on my understanding of ‘The Central Dogma’, one ’technology’ to produce the protein from the above sequence is the protein creation process that happens inside the Rybozome which is located in a cell’s nucleus. The Rybozome (which is also called R-RNA) is the site that ’takes-in’ M-RNA after it has transcribed a DNA sequence and undergone some editing to the original transcribing. The Rybozome has 2 parts. M-RNA sits in the small part, and gets read by the Rybozome which translates the codons it reads in the M-RNA, and ‘calls’ for T-RNAs to arrive at the big part of the Rybozome. Each T-RNA ‘holds’ and Amino Acid, and when ‘called’ by a respective codon sequence, it arrives at the Rybozome to ‘hand over’ that amino acid. Amino Acids bind to one another in the Rybozome’s bigger part and start forming a chain. This process repeats until the Rybozome ‘finishes’ reading the entire sequence, resulting in a chain of amino acids that will leave the Rybozome and continue to other processes that will eventually result in the aminos folding into a 3D structure that we call a protein.
Putting my optimized codon seq into Benchling with the additional sequences provided, I have created a share link to the Benchling file.
Following the steps in the Twist instructions part, I was able to export my Benchling file in .fasta format, upload it to the Twist platform, choose pTwist Amp High Copy Vector and download the .gb file. reuploaded into benchling I can see the resulting expressions cassette! Whohoo (:
What DNA would you want to sequence (e.g., read) and why?
Within the given context, of being introduced to Synbio, I am inclined towards sequencing DNA of organisms and proteins that have something to do with processes of photosynthesis, CO2 sequestration and or flavor and aroma enhancement. The former two have versatile applications in the emerging field of bio-chemical energy production, while the latter have more focused applications in the field of cultivated lab meat, both of them I find highly impactful and worthwhile endeavors to pursue.
In particular, I can propose to read the DNA of the RuBisCO enzyme, that binds a CO2 molecule to a RuBP molecule and creates two 3-carbon molecules. This is a part of the larger photosynthesis process where an organism is converting light energy into sugars and CO2 is being ‘stored’ in sugars. At the same time the photosynthetic process could also be tapped into, converting free electrons created in the process to electrical energy. Enhancing the processes underlying photosynthesis means potentially improving electrical energy yield and carbon sequestration, two much needed capabilities in our time.
In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why?
For reading RuBisCO genes, from my understanding, I would go for the Sanger reading process. It’s considered high accuracy, and fit for sequences that are on the order of ~700bp.
Is your method first-, second- or third-generation or other? How so?
Sanger is considered a first-gen method due to the fact that it reads the sequence one-by-one. ‘Next-Gen Methods’ tend to read arrays in parallel making them faster and more cost-effective.
What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.
A Sanger reaction will typically require the reaction-medium, a template DNA (could be a purified PCR or a plasmid), and a primer.
If using a Purified PCR, the primers used for the PCR process would work effectively as primers for the Sanger process as well.
Each primer will read roughly ~700bp so for reading longer sequences you would need to tile primers one after the other. If the sequence is in the right size, you could sequence it in 2 ‘runs’ using a forward reading primer and a reverse reading primer and notice where they overlap.
What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)?
Lets say I am performing the Sanger reaction with a purified PCR. I would take the Sanger mix (the medium in which the Sanger reaction occurs), my purified RuBisCO PCRs, and the primer I used for the PCR creation itself. Based on the size of the PCR (in bp units) I will decide how many primers I need because Sanger reads around ~700bp. Preferably the best results are achieved either with a sequence length that is suitable for a single primer use or dual-primer use, in which case I will utilize both a forward reading primer and a reverse reading primer. ‘base calling’, or identification of ATCG nucleotides is done by attaching a fluorescent ddNTP after bases occasionally. The ddNTP stops the sequence’s extension, serving as a ‘cap’ of sorts. While copying the sequence many times, the RNA polymerase will attach the ddNTP label multiple times, effectively creating many copies of the DNA in all possible lengths. These ddNTP fluorescent caps will serve as a labels that could be seen after the entire batch had been separated by length in an electrophoresis process. A sorting algorithm is then deployed, identifying the different colors and lengths of each sequence that shows up, and translating the colors back to bases. so essentially cutting dna and attaching colored labels to cuts, ordering the cuts by size, translating color + size results back to bases sequence using software.
What is the output of your chosen sequencing technology?
The output of the Sanger process is the chromatogram, a color-coded graph that shows peaks where the respective base had been identified along the length of the sequence, and the base-called sequence that is derived from it.
What DNA would you want to synthesize (e.g., write) and why?
Going back to the first question in this assignment, in the given context, I am inclined towards modifying DNA of organisms and proteins that have something to do with processes of photosynthesis, CO2 sequestration and or flavor and aroma enhancement. The former two have versatile applications in the emerging field of bio-chemical energy production, while the latter have more focused applications in the field of cultivated lab meat, both of them I find highly impactful and worthwhile endeavors to pursue.
In particular, I can propose to write the DNA of the RuBisCO enzyme, enhancing it’s ability to bind CO2 molecules to RuBP molecules more effectively. Enhancing the processes underlying photosynthesis means potentially improving electrical energy yield and carbon sequestration,.
What technology or technologies would you use to perform this DNA synthesis and why? How does your technology of choice edit DNA? What are the essential steps?
I believe that for modifying a DNA sequence I would order oligos of the chosen sequence, perform a fragment replacement on the specific point in the sequence I assume will result in enhancing the desired trait (In my case is improving the ability of RuBisCO enzyme to bind CO2 molecules to RuBP molecule), assemble all fragments together in assembly process (Gibson?), and then sequence the entire dna to verify that I’ve assembled it correctly.
What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing?
First I will need to make sure I have the proper assessment tools to determine success or failure. If the RuBisCO enzyme is binding CO2 molecules, it makes sense to creating a testing environment where I could measure rate of of CO2 dissappearance for example. I would then proceed to select sites for the mutation to happen (multiple sites or a specific target site, depending on available prior knowledge.). The next step would be to create primers that target these specific sites and change some bases, or if a larger chain needs to be replaced, I would create entire custome fragments. After that I would assemble the modifyied DNA, Sequence it to make sure I’ve assembled the intended sequence, and test it. Required inputs would include: template palsmids containing a PCR product of RuBisCO or the gene itself, primers or entire fragments for replacing, polymerase for duplicating the sequence, and assembly enzymes to ‘stich’ the new sequence together. If going with a plasmid, I will need the backbone itself, host cells to insert the plasmid to, and a sequencing technology to verify my work (Sanger was mentioned earlier but it depends on the actual planned sequence legnth in bp units)
What are the limitations of your editing methods (if any) in terms of efficiency or precision?
The polymerase process may introduce errors in copying the sequence. This is also true for assembly of large fragments as well, they could introduce unintended changes during assembly, this tends to happen if you try to assemble a bigger fragment count, repetitive fragments, or very long ones.
References:
Adams, J. (2008) DNA sequencing technologies. Nature Education 1(1):193
In the first lab we were oriented into lab work and norms. Got familiar with the concept of pippetting and introduced several different pippettes that will be helpful with transferring different liquid volumes.
Creating the gel: 1 part 50X TAE Electrophoresis 49 part deionized H2O 3g LE Agarose Pouring the gel into the well-molds: Designing the gel-run results in the web interface (It’s a space-invader holding a heart!): Preparing the pcr tubes with restriction enzymes, dna, CutSmart buffer & water:
This week’s lab was about getting familiar with cuttind-edge lab automation tools. We were introduced to the Opentron, which to me was a close relative to 3d printing hardware and other gantry based fabrication method. It runs on a python script indicating coordinates for the working head to go-to, and has a pump and a motor where you would imagine the filament extruder motor and the heating element to be in a standard FDM 3d printer. Really enjoyed using this cool device.
Subsections of Labs
Week 1 Lab: Pipetting
In the first lab we were oriented into lab work and norms.
Got familiar with the concept of pippetting and introduced several different pippettes that will be helpful with transferring different liquid volumes.
Lab 2: Gel Running
Creating the gel:
1 part 50X TAE Electrophoresis
49 part deionized H2O
3g LE Agarose
Pouring the gel into the well-molds:
Designing the gel-run results in the web interface (It’s a space-invader holding a heart!):
Preparing the pcr tubes with restriction enzymes, dna, CutSmart buffer & water:
Incubating the PCR tubes in the Thermocycler:
Injecting restriction enzymes into wells in the gel
Running the gel
Here we are!
Week 3 Lab: Opentron Gel Art
This week’s lab was about getting familiar with cuttind-edge lab automation tools.
We were introduced to the Opentron, which to me was a close relative to 3d printing hardware and other gantry based fabrication method.
It runs on a python script indicating coordinates for the working head to go-to, and has a pump and a motor where you would imagine the filament extruder motor and the heating element to be in a standard FDM 3d printer. Really enjoyed using this cool device.
Thanks to Ronan Donovan’s awesome online tool, we could bypass the scripting using a simple UI that translated our graphics into the code itself, very similar to what a slicer software does for an STL file to a 3D printer or what a CAM engine does for a STEP file for CNC operations.
