Week 1 HW: Principles and Practices

Question #1

During my undergraduate thesis project I developed a whole-cell bacterial biosensor for the high-sensitivity quantification of glyphosate, the most widely used herbicide globally. The project consists of an engineered strain of Agrobacterium tumefaciens as a biological chassis. The tool works by leveraging the natural phn operon (for degradation of phosphonates), regulated by the PhnF repressor and a promoter sensitive to it, which I have repurposed into a genetic circuit where the presence of glyphosate triggers the expression of a Green Fluorescent Protein (GFP). I am curious about this because glyphosate is essential for modern agriculture and its environmental accumulation and potential health risks are a growing concern. Current detection methods like HPLC-MS are expensive, centralized, and require complex sample preparation. My goal is to create a tool that allows for decentralized, low-cost environmental monitoring, enabling farmers and regulatory agencies to quantify herbicide levels directly in the field with high specificity and sensitivity.

Question #2

To ensure this biosensor contributes to an ethical future,

• Goal #1: Focus on Bioavailable Quantification over Total Concentration. The aim is to shift the regulatory paradigm from measuring total chemical presence to biological impact.

  • Sub-goal 1a: Establish a standardized metric for bioavailable glyphosate, defining how the bacterial response correlates to actual ecological and human health risks.
  • Sub-goal 1b: Validate the biosensor against traditional HPLC-MS methods to demonstrate that ‘biological sensing’ provides a more accurate representation of environmental toxicity in soil and water.

• Goal #2: Democratization through Open-Source Accessibility. The goal is to break the monopoly of centralized high-cost laboratories on environmental monitoring

  • Sub-goal 2a: Develop an Open-Source Laboratory Framework, providing free access to the genetic circuits (PhnF-based) and calibration protocols so local public health labs can replicate the sensor.
  • Sub-goal 2b: Reduce the cost-per-test to a fraction of an HPLC run, ensuring that low-resource communities can perform their own independent environmental audits

• Goal #3: Ensuring Environmental Health Equity. This goal focuses on the right of every community to know the state of their surroundings.

  • Sub-goal 3a: Integrate the biosensor into Citizen Science programs, empowering rural and marginalized populations to generate their own data regarding herbicide exposure.
  • Sub-goal 3b: Advocate for the legal recognition of biosensor-derived data as ’early-warning’ evidence for regulatory intervention in contaminated areas.

• Goal #4: Responsible Data Governance and Sovereignty. Protecting the information generated is as important as the detection itself.

  • Sub-goal 4a: Implement Data Anonymization and Privacy standards to ensure that small-scale farmers are not unfairly penalized or their lands devalued by the mapping of glyphosate levels.
  • Sub-goal 4b: Establish community-owned data repositories, preventing private corporations from harvesting environmental data for profit or surveillance.

Question #3

• Action 1: Creation of a “Bioavailable Standard” Certification

Type: New Requirement / Regulatory Rule

Actor: Federal Environmental Agencies (e.g., EPA, SENASA) and Standard-Setting Organizations (ISO).

Purpose: Currently, legislation only recognizes total glyphosate concentration via HPLC. I propose a new regulatory category for “Biological Bioavailability.” This changes the legal requirement from just “how much is there” to “how much is actively affecting the microbiome and health.”

Design: Regulators must approve the Agrobacterium biosensor as a valid “pre-screening” tool. If the biosensor detects high bioavailability, it triggers a mandatory, more detailed environmental audit.

Assumptions: I am assuming that the correlation between the GFP signal in my biosensor and the toxicological impact on other species is consistent across different soil types.

Risks of Failure & Success: Failure: If the biosensor is too sensitive (false positives), it could lead to unnecessary legal bans on farming. Unintended Success: If it becomes the gold standard, it might lead to companies designing herbicides that are “invisible” to this specific bacterial strain but still toxic to other organisms.

• Action 2: Open-Source “Bio-Foundry” Kits for Local Labs

Type: Financial Incentive / Technical Strategy

Actor: Academic Researchers, NGOs, and Government Science Grants.

Purpose: Instead of each lab trying to “reinvent” the sensor, we propose an open-source kit that includes the lyophilized (freeze-dried) bacteria and the standardized PhnF genetic circuits. This moves the tech from a single thesis in Rosario to any community lab in the world.

Design: Grant agencies would fund the production of these “starter kits.” Local labs “opt-in” by signing a peer-to-peer agreement to share their environmental data in a public, transparent repository.

Assumptions: I assume that local labs have the basic equipment (like a fluorometer or a simple dark box with a camera) to read the GFP signal from the bacteria.

Risks of Failure & Success: Failure: Without proper training, local labs might misinterpret results, leading to “bad data” that damages the project’s reputation. Unintended Success: If everyone uses it, the sheer volume of data could overwhelm regulatory agencies, making it impossible for them to act on every reported contamination site.

• Action 3: “Community Data Shield” Protocol

Type: Policy / Rule

Actor: Lawmakers and Citizen Science Alliances.

Purpose: To prevent the data from being used against the people who collect it. I propose a legal framework that anonymizes the specific location of a “positive” glyphosate test when shared publicly, unless the community decides otherwise.

Design: The software used to upload the biosensor results must include a “privacy-by-design” layer. This would require the approval of data protection authorities to ensure it meets GDPR-like standards for biological information.

Assumptions: It assumes that the benefit of knowing a region is contaminated outweighs the risk of individual farms being identified and potentially devalued.

Risks of Failure & Success: Failure: Lack of transparency could make the data useless for holding specific polluters accountable. Unintended Success: If the data is too well-protected and anonymized, it might prevent scientists from finding the exact “source” of a contamination plume in a river or aquifer.

Question #4

Question #5

Final Recommendation and Prioritization Strategy Target Audience: National Environmental Agencies (e.g., SENASA, EPA) and International Public Health Organizations (WHO/FAO).

• Recommendation: Based on the scoring matrix, I prioritize a hybrid governance model that combines Open-Source “Bio-Foundry” Kits (Action 2) with the Community Data Shield (Action 3). While the formal Certification of Bioavailability (Action 1) is a critical long-term goal for regulatory science, the immediate ethical priority is to democratize environmental monitoring. By providing low-cost, open-source kits based on my Agrobacterium biosensor research, we can empower local laboratories to bypass the financial barriers of HPLC-MS. This technical decentralization must be legally protected by a “Data Shield” framework to ensure that the resulting information serves to protect public health and the environment, rather than being used for corporate surveillance or land devaluation.

• Trade-offs and Justifications: The primary trade-off considered was Regulatory Authority vs. Social Accessibility. Choosing Action 1 would have provided more “legal weight” to the results, but it would have restricted the technology to high-end, expensive labs, effectively excluding the communities most affected by glyphosate use. I decided to sacrifice immediate regulatory formalization in favor of Global Equity. Another trade-off involves Data Transparency vs. Privacy. By prioritizing the “Data Shield,” we might slow down the ability of central governments to identify exact contamination sources, but we gain the essential trust of farmers and local citizens who might otherwise fear using the tool.

• Assumptions and Uncertainties:

  • Technical Robustness (Assumption): I assume that the genetic circuits (PhnF/GFP) developed in my thesis can be successfully stabilized in a “Bio-Foundry” kit format (lyophilized) without losing the sensitivity required to measure bioavailable fractions in diverse soil types.

  • Community Participation (Uncertainty): It remains uncertain whether local health labs will have the baseline technical capacity (e.g., basic fluorescence detection) to implement these open-source protocols without extensive on-site training.

  • Legal Recognition (Uncertainty): There is an uncertainty regarding how long it will take for “Citizen Science” data to be accepted as valid evidence in environmental litigation against large-scale agrochemical misuse.

Week #2 Lecture prep’

Homework Questions from Professor Jacobson

1. The error rate of the DNA polymerase is 1:10^{7. The human genome has roughly 3x10}9 bp without a proofreading or repair system, the polymerase would commit arround 300 mistakes during DNA replication in the human genome. Biology deals with it by having different repair systems like MMR, NER and hologous restart of the replication joint. Also the polymerase has different activities that improve the error rate.
2. For a human protein that has arroun 300-400 AA, we could make 10^150 different combinations. There are different ways for an organism to produce a certain protein, like codon bias, mRNA secondary structure, GC content, etc

Homework Questions from Dr. LeProust

1. The most common method for oligo syinthesis at the moment is phosphoramidite syntesis, made of four steps.
2. The difficulty lies in the yield and accumulated errors of the chemical process, by the time you reach aprox 200 nt , the yield of perfect sequences is so low that it is not practical.
3. Because of the low efficiency of the process, you could use Hierarchical assembly

Homework Question from George Church

1. The 10 essential amionacids for animals are: F, V, T, W, I, M, H, R, L and K. Regarding the lysine contingency, in reality all animals live in a natural multy contingency because they depend on all of this AA´s for the development of new proteins and they only can gather them from another sources.