HW1 – Class Assignment: Principles and Practices

Biological engineering application

I propose developing a low-cost, paper-based, semi-quantitative biosensing strip to monitor microbial activity in irrigation water and substrate leachates, using ATP as a proxy for system hygiene.

The application is grounded in intensive greenhouse agriculture, such as tomato and pepper production under plastic. This context is exemplified by the agricultural ecosystem of Almería (southern Spain), where water reuse, fertigation, and high cropping intensity increase disease pressure and make early detection of system-level risk critical.

Rather than identifying specific pathogens or pests, the tool provides early warning signals of deteriorating water or substrate conditions, enabling preventive action before visible plant symptoms appear. The design prioritizes robustness, affordability, and on-site usability, aligning with synbio-inspired and DIY approaches suitable for real-world deployment.

Governance and policy goals

Goal 1: Prevent harm through misuse or over-interpretation

• Avoid positioning the tool as a diagnostic for specific pathogens or pests
• Reduce the risk of false confidence leading to delayed or inappropriate interventions

Goal 2: Protect the environment and agricultural systems

• Enable earlier, targeted interventions that reduce excessive chemical treatments
• Support more sustainable water reuse and fertigation practices

Goal 3: Promote equitable and responsible access

• Ensure affordability and usability for small and medium growers
• Avoid governance mechanisms that exclude low-resource users

Governance actions

Option 1 — Safe-by-design technical constraints (Primary)

Purpose
Current practices rely on infrequent lab analyses or reactive treatment once symptoms appear. This option embeds governance directly into the technology by limiting outputs to semi-quantitative threshold signals (e.g. green / yellow / red).

Design

• Paper-based strips with dried enzymatic reagents
• Visual, band-based outputs instead of numerical values
• Explicit labeling of scope and limitations

Assumptions

• Decision-making benefits more from trend awareness than precision
• Users prefer simplicity and robustness over laboratory-grade accuracy

Risks of failure or “success”

• Users may still over-interpret results
• Excessive simplification could mask edge cases

Option 2 — Contextual baselining and trend-based interpretation (Primary)

Purpose
Replace universal “safe ATP thresholds” with local baselines, reflecting real operating conditions.

Design

• Initial baseline measurements per greenhouse or irrigation system
• Emphasis on comparing trends over time rather than absolute values
• Open documentation and guidance

Assumptions

• Baselines are sufficiently stable to be meaningful
• Users will follow basic setup protocols

Risks

• Incorrect baseline establishment
• Reduced comparability across sites

Option 3 — Soft governance via guidance and training (Secondary)

Purpose
Ensure responsible interpretation without creating regulatory barriers.

Design

• Short, practical guidelines
• Decision trees linked to color bands
• Explicit framing as complementary to existing IPM and scouting practices

Assumptions

• Guidance can be delivered through cooperatives or local hubs
• Users are willing to engage at a minimal level

Risks

• Added friction to adoption
• Uneven implementation

Scoring of governance options

(1 = best)

Prioritization and recommendation

Based on the scoring and qualitative assessment above, I would prioritize a combination of Option 1 (safe-by-design technical constraints) and Option 2 (contextual baselining and trend-based interpretation).

This choice reflects a deliberate decision to embed governance directly into the design of the tool, rather than relying on external rules or idealized user behavior. By constraining what the tool can claim and how its outputs can be interpreted, the risk of misuse or over-confidence is reduced at the point of use.

This approach is informed by the intended deployment context. In intensive greenhouse agriculture—such as the ecosystem of Almería—most growers are older, risk-averse, and skeptical of complex technologies. Trust is built through simplicity and reliability, often mediated by agricultural technicians or cooperatives, rather than through technical sophistication.

Option 1 embraces intentional imprecision: semi-quantitative, color-based outputs trade analytical resolution for robustness, interpretability, and ethical restraint. Option 2 complements this by favoring local baselines and trend awareness over universal thresholds, acknowledging the inherent variability of biological and agricultural systems.

Option 3 (training and guidance) is kept secondary. Heavy training or compliance requirements would likely slow adoption and conflict with the exploratory, DIY ethos central to HTGAA. Instead, lightweight guidance delivered through trusted intermediaries—such as technicians or cooperatives like La Caña or UNICA—offers a more realistic pathway.

While the solution is validated locally, the underlying challenge—designing biological tools that are both empowering and responsibly constrained—is global. The broader lesson is that, in applied synthetic biology, governance often works best when it is quiet, embedded, and shaped by context.

Ethical reflection

A central ethical concern is the risk of over-reliance on simplified biological signals in complex agro-ecological systems. While democratizing biosensing can empower growers, it may also lead to misinterpretation if uncertainty is not clearly communicated. Designing for semi-quantitative outputs, local baselines, and transparency about limitations helps balance empowerment with responsibility.