Week 1 HW: Principles and Practices
I am interested in developing an engineered probiotic system designed to release the lactase enzyme on demand in the human gut for individuals with lactose intolerance. This project is entirely in silico, combining concepts from synthetic biology, microbiome modeling, and systems biology without any wet-lab implementation.
The system would simulate a probiotic chassis such as Lactobacillus or Bifidobacterium, equipped with virtual genetic circuits inspired by lactose metabolism. These circuits would model regulatory control of lactase expression based on local lactose concentration, using logic-gate–like behavior and feedback mechanisms. Enzyme production would increase when lactose is present and decrease once lactose is depleted, allowing adaptive and resource-efficient regulation.
Note
Lactose intolerance is one of the most common digestive disorders globally, caused by reduced or absent lactase activity in adulthood. It affects a large proportion of the world’s population, particularly in Africa, Asia, and South America, leading to gastrointestinal discomfort and dietary restrictions. Addressing this condition highlights a real, widespread health challenge that benefits from innovative and accessible solutions. (Lactose Intolerance - NIDDK, 2024); image reference
Because this project represents an early, in-silico design phase, its governance goals focus on the responsible framing, communication, and interpretation of computational results rather than regulation of a finalized biological product.
1. Ensuring Ethical Transparency
In silico models can appear highly convincing, even though they rely on simplifying assumptions. Without transparency, such simulations may be mistakenly interpreted as real biological proof, reused incorrectly by others, or generate unjustified confidence in safety or effectiveness. To prevent these risks, the project emphasizes:
- Clear documentation of all modeling assumptions, including chosen parameters (e.g., lactose concentration thresholds, promoter sensitivity), simulation boundaries, and known limitations.
- Explicit disclosure of the speculative nature of the work, clarifying potential real-world implications while emphasizing that the model does not represent a validated or deployable probiotic system.
2. Maintaining Scientific Integrity
Although the conceptual model may function optimally in simulation, real biological systems often behave unpredictably due to environmental variability and biological complexity. To maintain scientific integrity, it is essential to:
- Avoid overstating the effectiveness or safety of real-world probiotics based solely on computational results, and clearly distinguish between theoretical design and experimentally validated outcomes.
3. Considering Public Health and Safety
Since biological behavior cannot be predicted with complete accuracy, the project addresses public health and safety by:
- Highlighting potential risks of physical implementation, such as disruption of gut microbiome balance or unintended metabolic effects.
- Including scenario-based analyses to explore possible unexpected consequences for gut microbiome health under different simulated conditions.
| PURPOSE | DESIGN | ASSUMPTIONS | RISKS OF FAILURE & “SUCCESS” |
|---|---|---|---|
| Providing mandatory transparency and documentation standards for in-silico biological models (by academic researchers, journals, funding bodies) | Require structured documentation sections describing modeling assumptions, parameter choices, simulation constraints, and known limitations of the model | Clear and standardized documentation reduces misuse, misinterpretation, and overconfidence in simulation results | Documentation may be superficial, misunderstood, or ignored by users |
| Providing ethical claim-limitation guidelines for computational synthetic biology projects (by bioethics committees, academic institutions) | Encourage explicit labeling of projects as Conceptual, Exploratory, or Pre-experimental, and require clear statements that simulation outcomes do not constitute clinical or biological proof | Clear framing of claims improves scientific integrity, responsible communication, and public trust in synthetic biology research | Guidelines may be ignored outside formal academic or publishing contexts; excessive caution may slow translation of promising concepts into experimental research |
| Recommending scenario-based risk modeling as a design requirement (by researchers, synthetic biology educators) | Integrate scenario analysis into in-silico projects, exploring possible unintended outcomes such as microbiome imbalance, excessive enzyme expression, or metabolic side effects if the system were physically implemented | Early anticipation of risks improves downstream design decisions and promotes responsible innovation | Scenario analysis may oversimplify complex biological interactions |
| Action / Policy Goal | Ensuring Ethical Transparency | Maintaining Scientific Integrity | Considering Public Health and Safety |
|---|---|---|---|
| Providing Mandatory Transparency & Documentation Standards for In-Silico Biological Models | 1 | 2 | 3 |
| Providing Ethical Claim-Limitation Guidelines for Computational Synthetic Biology Projects | 2 | 1 | 2 |
| Recommending Scenario-Based Risk Modeling as a Design Requirement | 3 | 2 | 1 |
From my perspective, scenario-based risk modeling can be prioritized over the other governance options, because all three approaches address public health and safety either directly or indirectly. Scenario-based analysis explicitly explores what could go wrong if an in-silico model were physically implemented, making it the most direct mechanism for anticipating risks to gut microbiome balance or unintended metabolic effects. However, maintaining scientific integrity also plays a critical indirect role in protecting public health: by avoiding overclaiming the safety or effectiveness of a purely conceptual model, the transition from simulation to real-world application becomes more cautious, accurate, and oriented toward appropriate experimental validation, thereby reducing the likelihood of harmful misinterpretations. Similarly, ensuring ethical transparency through clear and accurate documentation of modeling assumptions, parameters, and limitations improves how the model is interpreted and reused by others, helping prevent incorrect applications that could ultimately pose health risks.
Sources:
- Emerging biotechnologies: Technology, choice and the public good. (n.d.). Nuffield Council on Bioethics. Retrieved February 9, 2026, from https://www.nuffieldbioethics.org/publication/emerging-biotechnologies-technology-choice-and-the-public-good/
- Gingold-Belfer, R., Levy, S., Layfer, O., Pakanaev, L., Niv, Y., Dickman, R., & Perets, T. T. (2020). Use of a Novel Probiotic Formulation to Alleviate Lactose Intolerance Symptoms-a Pilot Study. Probiotics and Antimicrobial Proteins, 12(1), 112–118. https://doi.org/10.1007/s12602-018-9507-7
- iGEM Responsibility. (n.d.). Retrieved February 9, 2026, from https://responsibility.igem.org/
- Khalil, A. S., & Collins, J. J. (2010). Synthetic biology: Applications come of age. Nature Reviews Genetics, 11(5), 367–379. https://doi.org/10.1038/nrg2775
- Lactose Intolerance—NIDDK. (2024, January 30). National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/digestive-diseases/lactose-intolerance
Assignment (Week 2 Lecture Prep):
Homework Questions from Professor Jacobson:
- Error rate and genome context • From the slide N°= 8 , DNA polymerase has an error rate of ~1 in 10⁶ bases. • With the human genome of ~3 × 10⁹ bp, this would result in ~3,000 errors per replication without repair. • Biology reduces this discrepancy with proofreading activity of DNA polymerase (3′→5′ exonuclease) and post-replication mismatch repair like MutS, NER, BER…, which collectively reduce the final error rate to ~1 in 10⁹–10¹⁰.
- Human protein: ~1036 bp (~345 amino acids), With ~3 codons per amino acid on average, the number of possible DNA sequences for an average human protein is ~3³⁴⁵ (~10¹⁶⁴ possible sequences). Not all sequences work in practice because of Mutations: Insertions, deletions, transitions, and transversions that can introduce frameshifts or premature stop codons, making the protein non-functional. Also, there are some mechanism of regulations that make some Sequences creating unwanted secondary structures in mRNA, affect splicing, or introduce cryptic signals that disrupt translation.
Homework Questions from Dr. LeProust:
Most commonly used method for oligo synthesis Today, almost all synthetic DNA is made using phosphoramidite solid-phase synthesis. This method adds one nucleotide at a time on a solid support and is reliable, efficient, and easy to automate, which is why it became the standard for modern DNA synthesizers. https://biolabmix.ru/en/info/detail/oligonucleotide-synthesis/#:~:text=The%20most%20common%20approach%20to,for%20example%2C%20by%20attaching%20fluorophores.
Why it’s hard to make oligos longer than ~200 nt Each step in chemical DNA synthesis is very efficient but not perfect, so small errors happen every time a base is added. As the oligo gets longer, these errors pile up, and beyond about 200 nucleotides it becomes very difficult to get a clean, full-length sequence. https://pubs.rsc.org/en/content/articlepdf/2025/sc/d4sc06958g
Why you can’t directly synthesize a 2000 bp gene Making a 2000-base gene in one piece would accumulate too many chemical errors and damaged bases to be useful. Instead, companies synthesize short oligos and then assemble them enzymatically, followed by cloning and sequence checking to make sure the gene is correct. https://www.pnas.org/doi/10.1073/pnas.2237126100#:~:text=The%20broader%20implications%20of%20the,without%20multiple%20repair/selection%20steps.
Homework Question from George Church:
All animals require the same 10 essential amino acids because they cannot synthesize them and must obtain them from their diet. These are: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arginine (arginine is essential for all animals and conditionally essential in adult humans). The “lysine contingency” refers to the idea that lysine is often the limiting essential amino acid in plant-based diets, especially those dominated by cereals like wheat, rice, or maize. Since animals cannot make lysine, their growth and health are directly constrained by how much lysine is available in their food. So knowing that all animals share the same essential amino acid requirements makes lysine’s importance stand out even more. It shows that lysine is not just nutritionally important but evolutionarily critical.