Week 1 HW: Principles and Practices

Week 1: Principles & Practices- Class Assignment

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.

Lactate Biosensor Tattoo for competition swimmers!

• I propose developing a semi-permanent, waterproof biosensor tattoo that detects lactate levels in athletes during pool training. The system would rely on engineered biological circuits that respond to lactate and trigger a visible fluorescent or colorimetric signal, functioning as a traffic-light-style, semi-quantitative indicator of physiological stress.
• The idea is connected to course topics such as genetic circuit design and fluorescent protein signaling. Lactate would act as the biological input, while the output would be a color change generated by chromoproteins or fluorescent reporters, similar to the chromophore and genetic circuit.
• This tool doesn’t pretend to replace clinical blood tests or provide precise measurements. Instead, it will support athletic training by providing real-time visual feedback, reducing invasive blood sampling, and minimizing medical waste, such as needles and collection tubes.
• This idea is inspired by my personal experience as a competitive swimmer, where lactate monitoring required repeated finger pricks during intense training sessions. I am particularly interested in exploring how biological sensing circuits and fluorescence-based outputs could be adapted to function under demanding conditions such as exercise, pool conditions, and temperature variation.

Biology pipeline of the application (circuit-inspired sensing)

Swimmer (physiological lactate production):

→ Input: Lactate diffusion into the tattoo microenvironment → Sensing module: Lactate-responsive biological circuit → Signal transduction: Activation of chromoprotein / fluorescent reporter → Output: Visual color scale (green/yellow/red)

Visual diagram

Created in https://BioRender.com

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.

Governance / Policy Goals:

For the present idea and to ensure that the lactate biosensor tattoo contributes to an ethical and responsible future, I propose the following governance goals:

1. Goal 1: Protect Athlete Health and Prevent Harm (Non-maleficence)

• Sub-goals:
• Make sure that biosensor results are clearly communicated as semi-quantitative training indicators, not medical diagnoses (do not replace the traditional lab test).
• Prevent misinterpretation by athletes or coaches that could lead to overtraining or injury.
• Ensure that biosensor tattoos are biocompatible, with non-toxic materials, and safe.
• Required informed consent for younger athletes.

2. Goal 2: Prevent Environmental and Biological Risks

• Sub-goals:
• Avoid environmental release of engineered biological components by using encapsulated or cell-free sensing systems.
• Ensure biodegradability or safe disposal of tattoo materials.
• Follow Ecuadorian biosafety regulations regarding GMOs and synthetic biology applications.

3. Goal 3: Promote Equitable and Responsible Use

• Sub-goals:
• Acknowledge that early versions of the biosensor tattoo will likely be expensive and limited to pilot programs or elite training centers.
• Explore pathways for future cost reduction through industrial scaling and partnerships with public institutions.
• Encourage transparent communication about accessibility limitations during early deployment stages.

This goal particularly recognizes that initial implementations of the technology will likely be costly, requiring regulatory approval and industrial production to become broadly accessible.

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.)

• a. Purpose: What is done now and what changes are you proposing?
• b. Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc)
• c. Assumptions: What could you have wrong (incorrect assumptions, uncertainties)?
• d. Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

Governance Actions:

• Before describing the governance actions, it is important to mention that the project is proposed as a pilot to be tested with competitive swimmers from Concentración Deportiva de Pichincha (Quito, Ecuador). The project is framed within local ethical, legal, and institutional constraints, particularly Ecuador’s restrictive regulations regarding genetically modified organisms (GMOs), and prioritizes athlete safety, non-malfeasance, and responsible innovation.
• To guarantee that, the lactate biosensor tattoo contributes to an ethical and socially responsible future. I propose the following governance actions, involving a mix of technical, institutional, and regulatory approaches, and different actors

Action 1: Technical Safety-by-Design for a Non-Invasive Biosensor Tattoo:

• (Actor: device developers + regulatory agencies + academic labs)
• Purpose: Currently, lactate monitoring in competitive swimming relies on repeated invasive blood sampling, which generates medical waste and causes discomfort to athletes. This action proposes a semi-quantitative, non-invasive biosensor tattoo as a complementary training tool that reduces harm while not replacing clinical diagnostics.
• Design:
  • The biosensor is designed as a semi-permanent, waterproof tattoo that detects lactate accumulation and translates it into a visual color-scale output (green–yellow–red).
  • The biological sensing circuit is conceptually inspired by synthetic biology, which signals pathways but does not to release or replicate living organisms in the environment.
  • Design responsibilities would fall primarily on academic researchers, with oversight from institutional ethics committees and sports medicine professionals.
  • The visual output (chromoprotein or fluorescent reporter) is intentionally semi-quantitative, reducing the risk of overinterpretation.
• Assumptions:
  1. That lactate can be detected reliably through accessible physiological fluids without requiring invasive blood access as sweat.
  2. That fluorescent or chromogenic reporters can remain stable under water exposure, physical stress, and temperature variation.
  3. That athletes and coaches will correctly understand the limitations of the signal.
• Risks of Failure & “Success”

1. Failure could occur if lactate detection is inaccurate or unstable, leading to misleading feedback.
2. A successful outcome could unintentionally encourage overreliance on the tool, even though it is not clinically precise; it would be a good suggestion on how swimmers manage the lactate during intense training.
3. To mitigate this, clear labeling and training would be required to frame the tattoo strictly as a training aid, not a diagnostic device.

Action 2: Institutional Oversight and Ethical Use in Sports Contexts

• (Actors: Swimming National Federation (FENA), Ministerio del Deporte, etc)
• Purpose: Currently, limited governance frameworks are addressing the ethical use of biosensors in athletic training, particularly in developing countries. This action aims to prevent misuse or surveillance of athletes through physiological monitoring technologies, while ensuring the protection of biometric data generated by the biosensor tattoo.
• Design:
  • Implementation would require approval from national sports institutions (Federación Ecuatoriana de Natación, Ministerio del Deporte) and review by local bioethics committees.
  • Participation by athletes would be voluntary, with informed consent emphasizing data limits and privacy.
  • Data generated by the biosensor would be locally interpreted and not digitally transmitted, minimizing privacy risks.
• Assumptions:
  • That sports institutions will prioritize athlete wellbeing over performance pressure.
  • Visual-only feedback reduces the risks of secondary data use or surveillance.
  • Athletes feel empowered to decline participation without negative consequences.
• Risks of Failure & Success
  • Failure could happen if coaches or institutions pressure athletes to adopt the technology for performance surveillance, or if biosensor results are treated as substitutes for clinical laboratory testing.
  • Even in “success”, widespread adoption could normalize continuous biometric monitoring, raising concerns about autonomy and consent.
  • This highlights the need for explicit governance rules limiting use to training and research contexts.

Action 3: Regulatory Alignment with Ecuadorian Bioethics and Biosafety Frameworks (Actors: Ministerio de Salud (MSP), Agencia Nacional de Regulación, Control y Vigilancia Sanitaria (ARCSA), Corte Constitucional del Ecuador (Constitutional Court of Ecuador)- Constitution of 2008)

• Purpose: Ecuador maintains strict constitutional and legal constraints on GMOs, and biotechnology advances medical devices. This action aims to ensure that the project remains compliant with national bioethical principles while enabling responsible research innovation.
• Design:
  • The project is framed as a biosensing device, not a GMO deployment.
  • Any biological components would be designed to be non-replicative, contained, and biodegradable, avoiding environmental release.
  • Oversight would involve academic institutions, national ethics frameworks (MSP, ARCSA, and Constitution of Ecuador-2008), and alignment with international guidance (WHO biosafety principles).
• Assumptions:
  • That conceptual designs inspired by synthetic biology can be ethically discussed and evaluated at a governance level without requiring immediate deployment of genetically modified organisms (GMOs), particularly when the proposed application relies on non-living or enzyme-based sensing components.
  • Ecuadorian bioethics and regulatory frameworks can support the development of a highly controlled, small-scale pilot project for a biosensor intended for athletic training, after a long-term rigorous regulatory process, safety validation, and ethical review in coordination with national institutions such as MSP & ARSCA.
• Risks of Failure & Success
  • Regulatory ambiguity could slow or prevent approval even at the pilot level.
  • Conversely, “success” could provoke future pressure to commercialize without sufficient regulatory adaptation.
  • This underscores the importance of early governance discussions, even for speculative designs.

4. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

Table 1: Scoring action table

(1 = best, 3 = weakest)

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.

Prioritized Governance Approach:

Based on the scoring in Table 1, the most effective governance strategy for this project is a combination of Action 1 (Technical Safety-by-Design for a Non-Invasive Biosensor Tattoo) and Action 2 (Institutional Oversight and Ethical Use in Sports Contexts).

• Action 1: It’s prioritized because it directly protects athlete health and environmental safety by embedding biocompatibility, containment, and safe disposal into the technical design of the biosensor tattoo. This approach minimizes physical harm and reduces reliance on invasive lactate testing while remaining feasible within the Ecuadorian research context, where early-stage pilot projects must demonstrate safety before scaling.
• Action 2: This is prioritized too by addressing ethical risks related to data misuse. Institutional oversight through sports federations and bioethics committees ensures informed consent, limits performance surveillance, and protects athlete autonomy. This is particularly important in elite sports environments, where power imbalances between athletes and institutions may exist.
• For action 3 is not that prioritized in the early stage of the project, even though, in the long term, it remains relevant for future scaling once safety, ethical use, and institutional trust are established.

This combined approach is recommended primarily for local sports institutions and research actors in Ecuador, such as the Federación Ecuatoriana de Natación (FENA) and Ministerio del Deporte, balancing innovation with athlete protection under existing bioethical and regulatory frameworks. Also, by the supported international academic collaboration. Key uncertainties include institutional commitment and the long-term performance of the biosensor under real training conditions.

Reflection section.-

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.

• This first week made me reflect on how biology is not only a technical field but also deeply connected to ethics, society, and human experience. Although I already had a background in bioethics and biosafety from my undergraduate studies (mostly focused on GMOs, plant biotechnology, and laboratory practices), this class helped me think about ethics in a broader context, especially for emerging technologies such as biosensors, where regulatory frameworks are not always clearly defined, particularly in developed countries like Ecuador.

• One concern I realized is that for projects like this, it is sometimes unclear which national institutions should regulate them, especially when they fall between biomedical devices and sports technology. This highlighted the importance of having clear governance pathways and interdisciplinary oversight.

• To address this concern, I believe governance actions such as institutional bioethics review, informed consent, and collaboration between sports organizations and academic researchers are essential, especially during early pilot stages. These steps can help ensure that innovation remains centered on wellbeing, responsibility, and trust.

• What I also appreciated greatly about this week’s classes was the diversity of student backgrounds. There were not only scientists, but also economists, artists, psychologists, and others. It was inspiring to see how different perspectives came together around biology and innovation, reminding me that responsible science benefits from interdisciplinary thinking.

• This assignment was challenging for me at first. I began with many ideas and felt overwhelmed thinking about everything that could go wrong. Eventually, I grounded my project in my personal experience as a competitive swimmer and realized that even conceptual ideas can have real-world relevance. One thing that helped me a lot was creating the SWOT analysis, which helped me visualize both the potential and the limitations of my proposal.

Thanks for reading, for pre-lecture part, please read week 2- homework section. For more information, you can access my notion in week 1 homework:

Resources/reviewed information

• General idea of Lactate biosensors: Electrochemical Tattoo Biosensors for Real-Time Noninvasive Lactate Monitoring in Human Perspiration
• Governance Actions
• Action 1:
  • Can an Incremental Step Test Be Used for Maximal Lactate Steady State Determination in Swimming? Clues for Practice
  • Blood Lactate Concentration and Clearance in Elite Swimmers During Competition
  • Water-Soluble Single-Benzene Chromophores: Excited State Dynamics and Fluorescence Detection
• Action 2:
  • Ethics, Nanobiosensors and Elite Sport: The Need for a New Governance Framework
• Action 3: Spanish documents Biosecurity and normatives from Ecuadorian Institutions
• https://www.investigacionsalud.gob.ec/webs/intranet/wp-content/uploads/2022/09/M-BS-001-ed-02-Manual-de-Bioseguridad.pdf
• https://www.salud.gob.ec/wp-content/uploads/downloads/2013/09/reglamento_sobre_el_material_genético_septiembre_2013.pdf

Week 2 HW: DNA Read, Write, and Edit

Prelecture Homework:

In preparation for Week 2’s lecture on “DNA Read, Write, and Edit," please review these materials:

• Lecture 2 slides as posted below.
• The associated papers that are referenced in those slides.
• In addition, answer these questions in each faculty member’s section:

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?

The biological machinery of copying DNA (polymerase) has an error rate of approximately 1 mistake per 10⁶ bases during replication when proofreading is active. (slide 8). This error is a variation based on the error rate, from 10^{3 to 10}8. Compared to the length of the human genome, which is about 3.2 billion base pairs (≈3.2 × 10⁹ bp). This means that even with this high fidelity, thousands of errors could theoretically occur each time a genome is copied. (slide 10).

Biology addresses this discrepancy through multiple layers of error correction, including:

1. Post-replication mismatch repair systems (such as MutS-based repair). (slide 14)
2. Polymerase proofreading via 3′–5′ exonuclease activity.
3. Additional cellular DNA repair pathways.

These mechanisms dramatically reduce the effective mutation rate, allowing organisms to maintain genomic stability despite the enormous size of their genomes.

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?

An average human protein requires approximately 1036 base pairs of DNA. (slide 6). It’s because the genetic code is degenerate. The majority of amino acids are encoded by multiple codons, which theoretically encode the same protein. However, in practice, not all of these sequences work well. Some reasons are:

• Codon predominance: cells prefer certain codons over others, affecting translation efficiency. (slide 34)
• GC content: extreme GC or AT richness can cause instability or poor expression. (slide 39)
• Secondary DNA/RNA structures: some sequences fold in ways that interfere with transcription or translation.

These constraints mean that although many DNA sequences could encode the same protein, only a small subset is biologically practical and manufacturable.

Homework Questions from Dr. LeProust:

1. What’s the most commonly used method for oligo synthesis currently? The most widely used method today is solid-phase phosphoramidite chemical synthesis, which was originally developed by Caruthers. (slide 10-11). In this approach, nucleotides are added one by one on a solid support through repeated cycles of coupling, capping, oxidation, and deprotection. This is the standard chemistry behind modern automated DNA synthesizers and high-throughput platforms, as reviewed on slides.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis? Because each nucleotide addition is imperfect. Even with very high coupling efficiencies, small errors accumulate with every cycle in PCR. As length increases, the fraction of full-length, error-free molecules drops sharply. You also get more truncated products and substitutions, making purification harder and lowering overall yield. Practically, this limits reliable direct synthesis to ~150–200 nucleotides. (slides 36-39)

3. Why can’t you make a 2000bp gene via direct oligo synthesis? Because of the numbers of steps, if there is a 2000bp gene, the synthesis will take around 2000 steps. And at that scale, it’s probably to appear more chemical errors, full-length products become extremely rare, and the purity of the product will collapse. (slides 25-29).

To avoid synthesizing long genes directly, the standard strategy is: Use shorter bp (60-200nt) → assemble them enzymatically (PCR or gene assembly) → verify the final gene.

In result, the assembly reduces error and makes long genes.

Homework Question from George Church: [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”?

Essential aminoacid in animals

1. Isoleucine, 2. Leucine, 3. Lysine, 4. Histidine, 5. Methionine, 6. Threonine, 7. Valine, 8. Arginine, 9. Tryptophan, 10. Phenylalanine Those amino acids are considered essential because animals cannot synthesize them de novo and must obtain them from dietary sources.

Usually, lysine is limited in plant-based diets and many agricultural feeds. So, lysine contingency highlights how biological systems, including humans and livestock, depend heavily on external lysine availability for protein synthesis, growth, and health. Because lysine cannot be synthesized by animals, entire food chains rely on microorganisms and plants capable of producing it.

In conclusion of 3 prelecture activities, those changed my view of genetic coding as not only an informational system but also an ecological dependency network. As well, to understand the limitations and how technology advances for creating solutions and continue researching.

Thanks for reading. For more information, there is my Notion webpage with the homework Notion prelecture week 2

Week 3 HW: Lab Automation

Week 3: Lab Automation

Part 1: Phyton Code & Agar Design

Documentation:

• For the first part of the Lab Automation assignment, I worked with Opentrons Python code using Google Colab. During this process, I used ChatGPT primarily as a debugging and learning aid. It helps me resolve execution errors, install missing packages (via pip), and understand how to structure the notebook so the design can be visualized correctly.
• Because the shared notebook relies on Opentrons hardware-specific functions (such as load_labware), the code was adapted to allow local visualization without a physical robot. My draft version originally included labware definitions intended for real laboratory execution, but these were temporarily removed to enable Plotly-based visualization.
  • If you are interested in reading my code, please enter the following link: https://colab.research.google.com/drive/18Pb0JAgtB5Sv8v3VHhfop3mpF-nUiMp8?usp=drive_link
• The agar design was inspired by the ducks from Spirited Away (Studio Ghibli), based on my own drawing, combined with online references.

The final pixel-art layout was generated using the Opentrons Art Generator and can be viewed here: https://opentrons-art.rcdonovan.com/?id=5s7w0mpt758a7af

Code Building Pipeline:

To make the workflow clearer, the notebook was divided into three logical blocks:

flowchart TD
    A[OpentronsMock Definition] --> B[Main Protocol Code]
    B --> C[Visualization with Plotly]

• Block 1: Defines the virtual Opentrons environment and data recording
• Block 2: Executes the dispensing logic and color mapping
• Block 3: Displays the final agar pixel-art model

1. Opentrons Mock Definition: This block defines a mock version of the Opentrons protocol (OpentronsMock). Its purpose is to simulate robot behavior and record dispensing coordinates, enabling visualization without physical hardware. This block also sets up Plotly for graphical rendering.

2. Main Protocol Code: This is the core of the script, where:

• Color sources are assigned
• Coordinate points are paired with each fluorescent protein
• The virtual pipette iterates through each point set
• Dispensing actions are simulated

For visualization purposes, hardware-specific commands (such as load_labware) were removed in this version. The original draft protocol made for real robot execution is documented separately in “draft” inside the code.

3. Visualization: This final block executes the protocol and renders the design using Plotly. Here, all recorded coordinates are plotted, allowing inspection of:

• Spatial accuracy
• Color placement
• Overall agar pattern

This step is essential to verify that the design prints correctly before transferring it to a real Opentrons workflow. As well, the final result of the visualization is in the next image:

Part 2: Post-Lab Questions

Part 2.1: Research Paper automation application:

Scientific paper: “Technical upgrade of an open-source liquid handler to support bacterial colony screening” Available in: https://pmc.ncbi.nlm.nih.gov/articles/PMC10315574/

General view: This paper presents COPICK, a technical modification of the open-source Opentrons OT-2 liquid handling robot to automate bacterial colony screening. Colony picking is traditionally a labor-intensive bottleneck in genetic engineering workflows, especially when screening large numbers of variants generated by high-throughput DNA assembly. While commercial colony pickers exist, their high cost limits accessibility for smaller laboratories. COPICK addresses this limitation by integrating image acquisition and artificial intelligence into an affordable OT-2 platform.

The system combines a mounted USB camera with a Detectron2-based panoptic segmentation model to identify bacterial colonies directly from Petri dish images. The inference engine processes raw images, performs pixel- and object-level classification, and maps detected colony coordinates into the physical space of the robot. The OT-2 pipette then autonomously selects colonies based on user-defined criteria such as size, color, or fluorescence intensity. This integration enables on-board automated colony selection without the need for expensive commercial equipment.

Findings:

• Benchmark experiments performed with E. coli and P. putida demonstrated reliable performance across different screening scenarios (raw picking, color-based selection, and fluorescence-based cherry picking).
• COPICK achieved a raw performance of 73% over total screened colonies, increasing to 82% when considering only pickable colonies.
• The system showed high sensitivity (92%) and acceptable precision (78%), validating its potential as a cost-effective automation tool.
• Even if the classification errors existed in the model, the study suggests that performance could further improve using next-generation segmentation models such as SAM.

Why is it a novel application? I found this paper interesting, with a novel application for biology. First, COPICK reduces human bias and variability in colony selection by replacing manual visual inspection with algorithm-based inference. Also, the integration of AI-driven image segmentation with robotic actuation creates a reproducible, scalable workflow for microbial screening. And this approach democratizes high-throughput synthetic biology by making automated colony picking accessible to smaller laboratories, expanding the reach of biofoundry-style workflows.

Figures: Figure 3 from (Del Olmo Lianes et al., 2023), It shows the workflow diagram of the paper

Figure 7 (Del Olmo Lianes et al., 2023) shows the results, including the performance metrics that validate the assays.

Part 2.2: Application of Automation in Final Project:

Idea: Automated Screening of Lactate Biosensor Constructs using Cell-Free Systems

This idea comes from my W1 homework, where I propose to create a waterproof lactate biosensor tattoo for competition swimmers. I want to automate the screening of genetic lactate biosensor variants using cell-free protein synthesis (CFPS) in a 96-well plate. This will help with the optimization before proving it in vivo. Automation will be used to:

• Screen multiple lactate-responsive genetic constructs
• Test different lactate concentrations
• Quantify fluorescence output
• Select the best-performing biosensor variants

Flowchart

flowchart TD
    A[Automated Workflow] --> B[Dispense CFPS master mix into 96-well plate]
    B --> C[Add biosensor DNA variants]
    C --> D[Apply lactate gradient 0–20 mM]
    D --> E[Incubate at 37 °C]
    E --> F[Measure fluorescence]
    F --> G[Analyze response curves]

The goal is to identify the most sensitive and dynamic lactate-responsive construct.

Possible pseudocode

Disclaimer: this mini pseudocode was created with IA’s help– ChatGPT 5.2

for construct in constructs:
for lactate_concentration in gradient:
dispense_CFPS_mix()
add_construct(construct)
add_lactate(lactate_concentration)
seal_plate()
incubate(37, hours=3)
measure_fluorescence()

This idea was inspired by: (Jia et al., 2013); (Ghaffari et al., 2021); (Schmiedeknecht et al., 2022)

Part 3: Slides for final project:

My ideas for the project are: Main Idea: waterproof lactate biosensor tattoo for competition swimmers

• I propose developing a semi-permanent, waterproof biosensor tattoo that detects lactate levels in athletes during pool training. The system would rely on engineered biological circuits that respond to lactate and trigger a visible fluorescent or colorimetric signal, functioning as a traffic-light-style, semi-quantitative indicator of physiological stress.

• The idea is connected to course topics such as genetic circuit design and fluorescent protein signaling. Lactate would act as the biological input, while the output would be a color change generated by chromoproteins or fluorescent reporters, similar to the chromophore and genetic circuit.

• Second idea: based on the toehold switch in biosensors: mRNA of biofilm formation on kitchen elements, the idea is to create a biosensor that detects biofilm formation in kitchen surfaces or utensils before it matures, like a pH paper or a device

• Third idea: Creating a Biopatch of Metformin, where the delivery of metformin is better, also targeting Type 2 diabetes patients and patients with gastrointestinal intolerance to oral metformin

Link for final project slides: Final project slides ideas look for: 2026-a-ana-gomez | or Biopunk (updated!)

Weekly reflection:

This week was especially enjoyable because I got to design agar art in silico, which felt like a creative way to engage with lab automation concepts. While looking for a research paper, I was reminded of a researcher whose work uses algorithms from a different biological angle (using math algorithms to scan spheres that are attached to cells and visualize where the cancer cells are), and that made me realize how many areas of biology could benefit from automation in the future. I also noticed that my project ideas have been changing as I learn more about the course topics, which feels like part of the learning process itself. Overall, this week helped me reflect on how my interests are evolving, and it motivated me to keep exploring new perspectives and projects as I continue in the course.

Thank you for reading my weekly assignment! If you are interested in reading my Notion website, please enter the following link: https://www.notion.so/Assignment-Week-3-31125717f670808db22fc0687c7f7b19?source=copy_link

References and resources:

Part 2: Del Olmo Lianes, I., Yubero, P., Gómez-Luengo, Á., Nogales, J., & Espeso, D. R. (2023). Technical upgrade of an open-source liquid handler to support bacterial colony screening. Frontiers in bioengineering and biotechnology, 11, 1202836. https://doi.org/10.3389/fbioe.2023.1202836

Ghaffari, R., Yang, D. S., Kim, J., Mansour, A., Wright, J. A., Jr, Model, J. B., Wright, D. E., Rogers, J. A., & Ray, T. R. (2021). State of Sweat: Emerging Wearable Systems for Real-Time, Noninvasive Sweat Sensing and Analytics. ACS sensors, 6(8), 2787–2801. https://doi.org/10.1021/acssensors.1c01133

Jia, W., Bandodkar, A. J., Valdés-Ramírez, G., Windmiller, J. R., Yang, Z., Ramírez, J., Chan, G., & Wang, J. (2013). Electrochemical Tattoo Biosensors for Real-Time Noninvasive Lactate Monitoring in Human Perspiration. Analytical Chemistry, 85(14), 6553-6560. https://doi.org/10.1021/ac401573r

Schmiedeknecht, K., Kaufmann, A., Bauer, S., & Solis, F. V. (2022). L-lactate as an indicator for cellular metabolic status: An easy and cost-effective colorimetric L-lactate assay. PLoS ONE, 17(7), e0271818. https://doi.org/10.1371/journal.pone.0271818

Additional paper

Peñaherrera-Pazmiño, A. B., Isa-Jara, R. F., Hincapié-Arias, E., Gómez, S., Belgorosky, D., Agüero, E. I., Tellado, M., Eiján, A. M., Lerner, B., & Pérez, M. (2024). AQSA—Algorithm for Automatic Quantification of Spheres Derived from Cancer Cells in Microfluidic Devices. Journal of Imaging, 10(11), 295. https://doi.org/10.3390/jimaging10110295

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project