Week 1 Lab: Pipetting

Individual Final Project

Group Final Project

Week 1 HW: Principles and Practices

1. The Biological Engineering Tool I Want to Develop: The Gut-Longevity Diagnostic Platform Emerging research firmly establishes the gut microbiome as a key modulator of systemic inflammation, metabolic health, and even the rate of biological aging. The metabolic outputs of our gut bacteria—particularly short-chain fatty acids (SCFAs) like butyrate—are directly linked to immune regulation, insulin sensitivity, and cellular repair pathways. I propose developing a diagnostic platform to functionally map this ecosystem and provide actionable insights for promoting healthspan.

The Gut-Longevity Diagnostic is an at-home testing system that moves beyond static genomic sequencing. A user sample is exposed to a standardized panel of prebiotic substrates within a disposable cartridge containing engineered biosensors. These sensors measure the real-time, functional metabolic output—the specific SCFAs and gases produced by the user’s unique microbial community. A validated algorithm interprets this dynamic functional profile against longitudinal health data, generating a personalized, food-based nutritional prescription designed to steer the microbiome towards an anti-inflammatory, metabolic, and pro-longevity phenotype.

The goal is to transform gut health from a vague concept into a measurable, modifiable pillar of preventative medicine, providing a science-backed tool for conscious dietary choices aimed at healthy aging.

2. Governance & Policy Goals for an Ethical Future The primary goal is Beneficence and Non-Maleficence in Preventative Health: ensuring the tool delivers real health benefits while rigorously preventing harm across diverse populations.

Sub-goal 1.1: Ensure Clinical Validity and Safety. The algorithm’s nutritional recommendations must be grounded in robust clinical evidence to avoid harm. Incorrect advice could exacerbate conditions like metabolic syndrome or IBD. Governance must mandate rigorous validation against health outcomes, not just correlation.

Sub-goal 1.2: Prevent Biological Data Exploitation. Gut microbiome data is highly personal predictive information. Governance must establish it as a protected health entity, preventing its use by insurers or employers for discrimination or by third parties for unauthorized manipulation (e.g., targeted advertising for unhealthy foods).

Sub-goal 1.3: Architect for Equitable Access from Inception. To avoid exacerbating health disparities, the technology’s design and business model must prioritize accessibility. Governance should incentivize affordable, scalable solutions suitable for integration into public health initiatives for aging populations.

3. Three Potential Governance Actions Action 1: A “Functional Diagnostic” Pre-Market Framework.

Purpose: Create a new regulatory pathway for tools that provide functional health analysis and dietary advice, distinct from medical devices or supplements.

Design: A consortium of regulatory agencies, microbiologists, and nutrition scientists defines evidence tiers for claims. Developers must achieve a given tier before marketing.

Assumptions: Regulators can adapt quickly, and predefined evidence standards will accelerate, not hinder, responsible innovation.

Risks: Over-standardization could stifle novel approaches. Success could create a two-tier system where only well-funded entities achieve the highest validation tiers.

Action 2: A Microbiome Data Commons with Granular User Control.

Purpose: Shift data ownership from corporations to individuals by creating a user-controlled, interoperable data repository.

Design: A non-profit or public entity develops the open-source platform. Users hold encryption keys, granting time-limited, specific access to researchers or apps via a “data wallet.”

Assumptions: Users will manage their keys responsibly. Researchers will participate despite more complex data access procedures.

Risks: Increased platform complexity and liability. If poorly adopted, it could fragment the data ecosystem further.

Action 3: Public-Private Development of a Core, Open-Source Algorithm.

Purpose: Ensure the core science remains transparent and auditable, preventing proprietary “black boxes” from dominating a public health field.

Design: A government-funded research center develops and validates a base algorithm using diverse, ethically-sourced data. Commercial entities build applications on this audited core.

Assumptions: An open-source model can achieve and maintain clinical-grade accuracy. Public funding will be sustained.

Risks: The core model could become outdated without continuous public investment. Commercial forks could deviate from safety guidelines. 4. Scoring Governance Actions Against Policy Goals (Scoring: 1 = Best, 3 = Worst, n/a = Not Applicable)

5. My Recommendation & Trade-Offs To a National Institute on Aging or Public Health Agency:

I recommend prioritizing Action 3 (Open-Source Core) supported by Action 1 (Diagnostic Framework). This combination best serves long-term public health goals.

Why This Combination? Developing a publicly-audited core algorithm (Action 3) is a strategic investment that ensures foundational science remains a public good, fosters innovation on a level playing field, and directly enables equitable access by lowering the cost of entry. A clear, tiered Action 1 framework provides the necessary guardrails for safety and validity without prematurely stifling innovation around this open core.

The Trade-Off: This approach consciously accepts that early commercial market development may be less lucrative, potentially slowing initial private investment. However, it prioritizes long-term ecosystem health, scientific transparency, and equitable dissemination over short-term market capture by a few entities.

The Uncertainty: The major unknown is whether a publicly maintained model can match the rapid iteration pace of well-funded private labs. This requires a commitment to sustainable, competitive funding for the public core development team.

6. Personal Ethical Reflection This week’s work crystallized a critical ethical tension: the gap between personalized health technology and population-level health justice. A tool optimized for “longevity” risks being calibrated using data from affluent, already-healthy cohorts, potentially pathologizing normal variations in gut flora from underrepresented groups or labeling their traditional diets as “suboptimal.” This risks a new form of biomedical marginalization.

Therefore, a non-negotiable governance action must be mandatory diversity and inclusion in foundational research. Any public funding or regulatory approval for such platforms should require that the training datasets and validation cohorts are representative of global genetic, dietary, and socioeconomic diversity. This is not merely an ethical imperative but a scientific one, ensuring the resulting tools are robust, generalizable, and truly serve the goal of healthspan for all.

Week 4: Protein Design

Protein Design I

Questions from Shuguang Zhang

Question 1: How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)

Answer: In 500 grams of meat, you consume approximately 8.2 × 10²³ molecules of amino acids. This refined calculation uses a standard protein content of 30% for cooked meat and an adjusted average molecular weight of 110 Daltons per amino acid residue.

Step-by-Step Refined Calculation

1. Calculate Total Protein Mass
   Raw meat typically contains about 20–22% protein, but 500 g of cooked lean meat (like chicken breast or steak) averages roughly 30% protein due to water loss during cooking.
   Protein mass = 500 g × 0.30 = 150 g.

2. Adjust for Amino Acid Residue Weight
   The average weight of the 20 free amino acids is ~100 Da, but when they link to form protein chains, they lose a water molecule (18 Da). Biochemists use an adjusted average of 110 Da per residue in a protein chain.

3. Convert Mass to Daltons
   Using the conversion factor where 1 g = 6.022 × 10²³ Da (Avogadro’s number):
   Total mass in Daltons = 150 g × (6.022 × 10²³ Da/g) = 9.033 × 10²⁵ Da.

4. Calculate Total Molecules
   Divide the total mass in Daltons by the average mass of a single amino acid residue:
   Number of molecules = (9.033 × 10²⁵ Da) / (110 Da/residue) = 8.21 × 10²³ residues.

Comparison of Meat Types (500 g cooked)

The specific amino acid count fluctuates depending on the lean‑to‑fat ratio of the meat:

Question 2: Why do humans eat beef but do not become a cow, eat fish but do not become fish?

Answer:
Humans do not “become” the animals they eat because the body does not absorb tissues whole; it reduces them to universal chemical precursors before rebuilding them according to a strictly human genetic blueprint.

1. The Principle of Molecular Deconstruction (Digestion)

Proteolytic Breakdown: Enzymes like pepsin and trypsin hydrolyze the peptide bonds of animal proteins, reducing them into their smallest constituents: amino acids.
Universal Currency: At the molecular level, a leucine molecule from a cow is chemically identical to a leucine molecule in a human. By the time these nutrients reach the bloodstream, all species-specific “identity” has been stripped away.

2. Genetic Blueprint and Protein Synthesis (The “Human” Template)

Once absorbed, these universal amino acids enter the cellular amino acid pool. The reason we rebuild these into human muscle rather than bovine muscle lies in our DNA.
The Instruction Manual: Your DNA contains the specific sequences (templates) for human proteins. Through the processes of transcription and translation, your ribosomes use the “imported” amino acids to assemble exclusively human proteins (like human collagen or human hemoglobin).
Biological Identity: The body’s metabolic pathways are genetically “hard-coded.” Even if you consume fish DNA, your cells lack the biological machinery to incorporate foreign genetic material into your own genome; instead, foreign DNA is broken down into harmless nucleotides and recycled as raw material.

3. Evolutionary Barrier and Immune Guarding

From an evolutionary standpoint, the ability to maintain a stable biological identity while consuming diverse organic matter is a survival necessity.
Immune Surveillance: The human immune system is designed to recognize and destroy “non-self” proteins. If a cow protein were to enter the bloodstream intact, the body would treat it as a pathogen (an allergen or invader) rather than a building block.
Metabolic Adaptability: Humans are physiologically omnivorous, meaning our systems are specialized in extracting energy from varied sources without compromising our own structural integrity.

Summary

In short: We eat cow structure, but we follow human instructions.