Week 1 HW: Principles and Practices

Biological Engineering Application

1. Description:

My idea is to develop a biosensor for heavy metal detection using engineered Escherichia coli bacteria as a chassis. Lead, cadmium, and mercury are the most problematic heavy metals, accumulating in organisms and slowly killing them. The tool would involve genetically modifying E. coli to incorporate metal-responsive genetic circuits that detect the presence of heavy metals and respond by producing an easily observable response. Right now I am considering green fluorescent protein (GFP) to be the best fit. When exposed to contaminated water, the bacteria would “light up” proportionally to the metal concentration, allowing for quick detection via a simple handheld fluorometer. Heavy metal pollution from industrial runoff, mining, and urban waste is a major global issue, leading to contaminated drinking water that causes neurological damage, kidney failure, and ecosystem disruption in affected communities. Traditional detection methods, like atomic absorption spectrometry, are expensive, lab-based, and time-consuming, often delaying response to pollution events. In contrast, biosensors are low-cost, portable, and provide real-time results, enabling faster interventions in polluted areas, such as rivers or municipal water supplies in developing regions. Iit promotes sustainable monitoring and could integrate with community science initiatives to empower local environmental stewardship.

2. Governance/Policy Goals

To ensure this biosensor contributes to an ethical future, the overarching goal is to promote responsible innovation that prioritizes non-malfeasance—preventing harm to human health, ecosystems, and society—while fostering beneficial uses. This adapts the synthetic genomics framework mentioned, emphasizing safety and security alongside equity to address potential disparities in access and impact. I break this into three specific sub-goals: Biosafety Sub-Goal: Minimize environmental and health risks from the release or unintended spread of engineered organisms. This includes preventing ecological disruption, such as engineered bacteria outcompeting native microbes or transferring genes, which could alter biodiversity in water systems. Policies should require containment strategies to ensure the biosensor is used in controlled settings without persistent environmental persistence. Biosecurity Sub-Goal: Prevent misuse or dual-use applications that could enable harm, like weaponizing metal-detection circuits for bioterrorism or creating biosensors that inadvertently aid in evading pollution regulations. This promotes secure handling of genetic designs to avoid exploitation by malicious actors, drawing on broader SynBio governance to safeguard public trust. Equity Sub-Goal: Ensure fair access and distribution of the technology, particularly for underserved communities facing water pollution. This involves policies that avoid exacerbating global inequalities, such as patent barriers limiting deployment in low-income countries, and instead encourage open-source designs or subsidies to promote inclusive benefits and autonomy in environmental monitoring.

3. Potential Governance Actions

Action 1: Mandatory Biosafety Certification for Biosensor Deployment (Regulatory Rule by Federal Regulators like the EPA or FDA)

Purpose: Currently, environmental biosensors fall under general biotech regulations like the Coordinated Framework for Biotechnology, but lack specific mandates for field-use certification. The proposed change requires pre-approval testing for ecological safety before commercial or research deployment, similar to EPA pesticide registrations, to prevent accidental harm. Design: Regulators would develop a certification process involving lab and field trials assessing containment, gene stability, and non-toxicity. Actors include federal agencies (implementers), companies/academics (applicants who must fund tests), and expert panels (approvers). Funding could come from application fees, with international harmonization via bodies like the OECD. Assumptions: Assumes regulators have sufficient expertise in SynBio and that testing protocols accurately predict real-world impacts; uncertainties include long-term ecological effects in diverse water environments. Risks of Failure & “Success”: Failure could occur if bureaucracy delays innovation, stifling biosensor adoption in pollution hotspots. “Success” might unintendedly create over-reliance on certified tools, marginalizing non-certified community innovations, or lead to complacency in broader pollution prevention.

Action 2: Grant Incentives for Equity-Focused Biosensor Development

Purpose: Today, funding for SynBio often prioritizes commercial viability over equity, leading to tech concentrated in wealthy nations. The proposal introduces grants rewarding projects with open-access designs in polluted developing regions, analogous to financial incentives for green tech under the Paris Agreement. Design: Organizations would allocate funds via competitive calls, requiring proposals to include equity metrics like tech transfer to low-income areas. Actors: Funders (providers), researchers/companies (opt-in applicants), and NGOs (implementers for distribution). Approval involves peer review emphasizing ethical impact. Assumptions: Assumes grantees will genuinely prioritize equity without gaming the system; uncertainties around measuring “equitable” outcomes in diverse cultural contexts. Risks of Failure & “Success”: Failure if funds are insufficient or misallocated, perpetuating inequities. “Success” could flood markets with subsidized tools, undercutting local innovations or creating dependency on international aid, potentially ignoring region-specific pollution needs.

Action 3: Integration of Genetic Kill Switches in Biosensor Designs

Purpose: Current SynBio designs sometimes lack built-in safeguards, risking uncontrolled spread. The change mandates researchers to embed “kill switches”—genetic circuits that deactivate the organism after use or under certain conditions (e.g., absence of a lab-provided nutrient)—inspired by 3D printing’s software locks for safety. Design: Researchers would incorporate switches during engineering, sharing protocols via open repositories like Addgene. Actors: Academics (implementers who opt-in for ethical best practices), funders (requiring it for grants), and journals (enforcing via publication guidelines). No major new funding needed, but training workshops could accelerate adoption. Assumptions: Assumes kill switches are foolproof and won’t evolve away; uncertainties include efficacy in variable field conditions like polluted water pH changes. Risks of Failure & “Success”: Failure if switches malfunction, allowing escape and ecological harm. “Success” might give a false sense of security, encouraging riskier deployments, or raise biosecurity concerns if switches are hacked for malicious persistence.

4. Score

5. The most important governance option

Based on the scoring, I would prioritize a combination of Action 2 and Action 3 (Integration of Genetic Kill Switches), with Action 1 as a supportive but secondary measure. This hybrid approach balances prevention, response, and promotion of beneficial uses without excessive burdens. Action 2 excels in equity and constructive applications, addressing the critical need to make biosensors accessible in pollution-affected developing regions, while fostering innovation through incentives rather than mandates. Action 3 complements this by providing strong biosafety and biosecurity, offering technical safeguard that integrates seamlessly into development workflows. Together, they minimize risks like environmental harm. Action 1, while effective for prevention, scores poorly on burdens, feasibility for small actors, and not impeding research, making it better as an optional escalation for high-risk commercial applications rather than a blanket requirement. Trade-offs considered: This combo trades some regulatory rigor (Action 1’s strength) for flexibility and lower costs, potentially allowing minor incidents if voluntary adoption lags, but it avoids stifling research in a field like synthetic biology where rapid iteration is key. It also prioritizes equity over uniform enforcement, which might mean uneven global standards but better real-world impact in vulnerable areas.

Homework Questions from 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?
   • Polymerase error rate: 1:10^6
   • The human genome has 4.2 billion base pairs, which means that polymerase makes 3200 errors at each celll division.
   • In order to deal with this high error rate, cells have systems made of multiple proteins that correct those errors, although they are not perfect.
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?
   • Human protein: ~1036bp –> ~345 amino acids W
   • Different codons can enconde for the same AA, but not all these sequences are equally efficcinet. Depending on the organism, a codon might be better for correctly and fast/slow enough being translated into an amino acid.

Homework Questions from Dr. LeProust:

1. What’s the most commonly used method for oligo synthesis currently?
   • The solid-phase phosphoramidite chemical synthesis
2. Why is it difficult to make oligos longer than 200nt via direct synthesis?
   • error accumulation and chemical limitations
3. Why can’t you make a 2000bp gene via direct oligo synthesis?
   • mailny due to error accumulation

Homework Question from George Church:

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

The 10 essential amino acids that animals cannot synthesize on their own and must obtain through their diet are: Arginine, Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, Valine

The “Lysine Contingency” refers to the fictional failsafe mechanism in Michael Crichton’s Jurassic Park, where resurrected dinosaurs were genetically engineered to lack the ability to produce lysine, one of these essential amino acids. The idea was that without lab-supplied lysine supplements, the dinosaurs would die, preventing them from surviving if they escaped containment. Lysine is an essential amino acid for all animals which reinforces why this contingency is fundamentally flawed as a control measure. In reality, no animal can synthesize lysine endogenously—they all rely on dietary sources like plants, insects, or meat, which are abundant in natural ecosystems. If the engineered dinosaurs escaped, they could simply consume lysine-rich foods in the wild (as they do in the story), rendering the dependency moot. It highlights a clever narrative device but poor fictional science, as it overlooks basic nutritional biology and ecology. A more effective contingency might target a non-essential amino acid or something truly unique to the lab environment.

Week 2 Homework: DNA Read, Write & Edit

1. Gel Art:

vs

The digestion results:

3.1. Choose your protein.

Human ubiquitin is one of the most highly conserved proteins across eukaryotic species, with an identical amino acid sequence in organisms ranging from yeast to humans. It plays a central role in the ubiquitin-proteasome system, where it tags damaged or unnecessary proteins for degradation, regulating critical cellular processes such as cell cycle progression, DNA repair, signal transduction, and protein quality control. Protein sequence - 76 amino acids sp|P0CG48|UBC_HUMAN Polyubiquitin-C OS=Homo sapiens OX=9606 GN=UBC PE=1 SV=2 MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG

3.2. Reverse Translation:

ATGCAGATCTTCGTGAAGACCCTGACCGGCAAGACCATCACTCTGGAGGTGGAGCCCAGTGACACCATCGAGAACGTGAAGGCCAAGATCCAGGACAAGGAGGGCATCCCTCCTGACCAGCAGAGGCTGATCTTTGCCGGCAAGCAGCTGGAAGATGGCCGCACCCTGTCTGACTACAACATCCAGAAGGAGTCCACCCTGCACCTGGTGCTGCGTCTGCGTGGCGGC

3.3. Codon optimization:

Different organisms have distinct preferences for which codons they use for each amino acid, due to differences in tRNA abundance and translation efficiency. The native human sequence may contain codons that are rare in other hosts, leading to slower translation and ribosomal stalling, which increase the risk of misfolding. I decided to optimize for Escherichia coli K-12, which is fast-growing, inexpensive, well-characterized, and widely used for high-yield protein expression in research and industry. Using Benchling codon optimization tool, I obtained:

ATGCAGATCTTTGTGAAAACCCTGACCGGTAAAACCATTACCCTGGAAGTGGAGCCGAGCGATACCATTGAAAACGTGAAAGCGAAAATTCAGGATAAAGAAGGCATTCCGCCGGATCAGCAGCGCCTGATTTTTGCCGGCAAACAGCTGGAAGATGGTCGTACCCTGAGCGACTATAACATTCAGAAAGAAAGCACCTTACATCTGGTGCTGCGTCTGCGTGGTGGT

3.4. You have a sequence! Now what?

The optimized DNA sequence can be synthesized and cloned into a vector for protein production in E.coli K-12:

The DNA is inserted into a plasmid and transported into E. coli cells. Host RNA polymerase transcribes the DNA into mRNA. Ribosomes then translate the mRNA into the polypeptide chain using tRNAs, amino acids, and energy from GTP/ATP. The protein folds , and cells are lysed to purify it.

3.5. How does it work in nature/biological systems?

How a single gene codes for multiple proteins at the transcriptional level: One common mechanism is splicing, where a single pre-mRNA transcript is processed in different ways to include or exclude exons, producing multiple mature mRNA isoforms from the same gene. These isoforms are translated into distinct proteins with different functions. (Ubiquitin genes themselves primarily produce identical monomers via polyubiquitin precursors or fusions, with cleavage occurring post-translationally; alternative splicing is more prominent in other human genes, e.g., for diversity in receptors or enzymes.) Alignment of DNA, transcribed RNA, and translated Protein:

Important:

• in the genome there are multiple genes that code for ubiquitin (ribosomal fusion genes and polyubiquitin genes), due to the necessity of upregulating the production during periods of increased metabolic stress;
• in general, ubiquitin genes do not contain introns, most likely due to the need of quick production and low change for errors to occur.
• ubiquitin suffers post translation modifications
• ubiquitination is the process by which E1, E2 and E3 enzymes mark misfolded proteins or danaturated ones for protein degradation by attaching the damaged protein to ubiquitin as a substrate
• E1 activates ubiquitin
• E2 transports ubiquitin to the target protein
• E3 facilitates ubiquity-substrate binding
• the E1 enzyme activates the ubiquitin

4.2

Complete sequence (promoter, RBS, Start codon, coding sequence, 7xHis Tag, end codon, terminator)

4.3, 4.4, 4,5, 4,6 Results:

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

i)I would be enthralled to sequence the genes responsible for ribonucleic vaults. A deeper understanding of how the MVPs (major histocompatibility proteins) are used to create literal containers used for cellular transportation would help us better understand how to create intricate structures far more complex than what evolution could achieve in the coming millions of years.

ii) I would the Third-Generation sequencing. Why? Unlike second-generation (Next-Generation Sequencing/NGS) platforms like Illumina, which require the sheer repetition of “sequencing by synthesis” on amplified clusters, ONT performs single-molecule, real-time sequencing. & It does not require PCR amplification to create a signal, meaning it bypasses the “vicious” cycle of amplification biases that can sometimes lead to errors in GC-rich regions of the genome.

5.2 DNA Write:

i) A genetic circuit designed for the real-time detection of microplastic degradation products in aquatic ecosystems.

The sheer volume of plastic entering our oceans has created a vicious cycle where macro-plastics break down into microscopic particles that enter the food chain, eventually bearing the brunt of their toxicological effects on human health. The design would be a modular genetic circuit that responds to Terephthalic Acid (TPA), a primary breakdown product of PET plastics.

• The Sensor: A specific transcription factor (TpaR) that remains secretive and inactive until it binds to TPA.
• The Amplifier: Once TpaR is activated, it triggers a “leaky” expression filter to ensure high sensitivity.
• The Reporter: A high-intensity chromogenic protein (like amilCP, which turns dark blue) that is easily visible to the naked eye, even when swathed in murky environmental water samples. ii)
• Synthesis Technology: Silicon-based Synthesis I would choose this technology because it uses a silicon platform to act as a solid support for the chemical reactions. Traditional plastic plates are inefficient; by moving to silicon, we can rebuke the wastefulness of older methods, reducing reagent use by over 99%.
• Scalability: It can synthesize 9,600 genes on a single chip Length Constraints: As the DNA strand grows longer, the efficiency of adding each subsequent base drops slightly.

5.3 DNA Edit:

(i) The Edit: Enabling Nitrogen Fixation in Cereals Currently, only legumes can naturally convert atmospheric nitrogen into a usable form via a secretive symbiotic relationship with specialized bacteria. Most cereal crops lack this ability, forcing farmers into a vicious cycle of applying synthetic nitrogen fertilizers. The planet’s soil and waterways bear the brunt of this practice, leading to massive nutrient runoff and greenhouse gas emissions.

I would want to edit the Symbiosis Receptor-Like Kinase (SYMRK) and Nod Factor Receptor (NFR) genes in wheat. These edits would “re-tune” the plant’s root receptors to recognize the signaling molecules of nitrogen-fixing bacteria. This change would rebuke the notion that high-yield farming must be ecologically destructive, as the crops would essentially produce their own “green” fertilizer.

(ii) The Technology: Prime Editing To achieve these precise changes without causing accidental damage to the rest of the genome, I would use Prime Editing (PE).

While standard CRISPR-Cas9 is effective for “breaking” genes, Prime Editing is a “search-and-replace” tool that can rewrite specific DNA letters with manifold precision. This is essential for plants, where the genome conceals delicate regulatory networks that can be easily disrupted by the “clumsy” DNA breaks of older technologies.

How it Edits DNA (Essential Steps): Targeting: The Prime Editor—a fusion protein consisting of a Cas9 nickase and a Reverse Transcriptase (RT)—is guided to the specific root receptor gene.

Nicking: Instead of cutting both strands of DNA (which is a “vicious” event for a cell), it creates a small nick in only one strand.

Reverse Transcription: The pegRNA (prime editing guide RNA) provides a template. The RT enzyme “reads” this RNA and synthesizes a new strand of DNA that contains the desired edit.

Flap Competition: The newly synthesized “edited” DNA flap competes with the original unedited DNA flap.

Incorporation: Through natural cellular repair, the old flap is removed, and the new, edited sequence is permanently integrated into the genome.

Preparation and Input The sheer complexity of Prime Editing requires meticulous preparation before a single cell is touched.

Design Steps: We must use software to design the pegRNA. This includes choosing a Primer Binding Site (PBS) (which tells the editor where to start) and an RT template (which contains the actual “fix” for the nitrogen receptor).

Inputs:

The Prime Editor Construct: Usually delivered as a plasmid or mRNA encoding the Cas9-RT fusion protein.

The pegRNA: The custom RNA sequence that “searches and replaces.”

Delivery System: For plants, we often use Agrobacterium-mediated transformation or Biolistics (a “gene gun”) to shoot these components into the plant tissue.

Target Cells: Embryogenic callus cells from the wheat plant, which can be grown back into a whole, fertile plant.

Limitations: Efficiency and Precision Although Prime Editing is highly precise, it is not yet perfect:

Efficiency: In many plant species, the editing efficiency is still quite low (often <10%). Getting the “search-and-replace” to actually stick across millions of cells remains a bottleneck.

Size Constraints: Because the Prime Editor protein is so large, it is difficult to pack into some delivery vehicles.

Target Range: The editor must be near a specific sequence called a PAM site. If the receptor gene we want to edit isn’t near a PAM, the tool is effectively “blind” to that location.

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project