Week 1 HW: Principles and Practices

My Homework

A Logic-Gated Bispecific Nanobody Approach to Overcome Immune Resistance in Multiple Myeloma

1. First, describe a biological engineering application or tool you want to develop and why.

Multiple myeloma is a cancer of plasma cells that develop and persist within the bone marrow, where disease progression is strongly shaped by interactions with the immune microenvironment. Therapies which are targeting CD38, a protein highly expressed on myeloma cells, have significantly improved patient outcomes. However, many patients either fail to respond or relapse after initial treatment. This resistance is increasingly understood to arise not only from changes within tumor cells, but also from therapy-induced immune dysfunction, including impaired natural killer cell activity, T-cell exhaustion, expansion of suppressive myeloid cells, and altered CD38 expression on both malignant and healthy immune cells.

To address these limitations, I aim to develop a hypothetical protein-engineering–based immunotherapy model centered on a logic-gated bispecific nanobody. This construct links a CD38-targeting nanobody with a CD3-targeting nanobody which will recruit T cells and incorporates a protease-cleavable linker that is selectively activated within the myeloma bone marrow microenvironment. By functioning as a biological “logic gate,” the therapy remains inactive in healthy tissues and becomes activated only in the presence of tumor-associated proteases, thereby limiting off-target toxicity and unwanted immune activation.

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.

Goal 1 - Prevent unintended harm arising from excessive or misdirected immune activation.

  Minimize off-target toxicity + Control immune overactivation

Goal 2 Ensure that advanced protein-engineering tools are not misused or deployed without sufficient oversight.

  Rigorous preclinical validation standards + Misuse prevention and containment.

Goal 3 Equity and Fair Access

   Equitable clinical development + Affordability and accessibility 

3. Next, describe at least three different potential governance “actions” by considering the four aspects

Action 1: Regulatory Requirement for Immune-System–Specific Safety Evaluation

(Actor: Federal regulators, e.g., FDA / ICMR) Type: New requirement / rule

Purpose

What is done now: Current approval pathways for antibody therapies primarily evaluate toxicity, pharmacokinetics, and tumor response, with limited focus on long-term immune system remodeling.

Proposed change: Require that logic-gated or immune-activating biologics undergo mandatory immune-microenvironment profiling, including effects on T cells, NK cells, and suppressive myeloid populations, before advanced clinical trials.

Design

Regulators mandate additional preclinical and early clinical immune profiling

Developers must submit data on immune exhaustion, cytokine signaling, and reversibility of immune activation

Similar to aviation safety redundancy checks, where systems must be tested under multiple failure conditions

Assumptions

Assumes immune dysregulation is a major driver of long-term harm and treatment failure

Assumes reliable models exist to predict immune behavior in humans

Risks of Failure & “Success”

Failure: Increased regulatory burden may slow innovation or disadvantage smaller labs

“Success” risk: Over-standardization may discourage unconventional but beneficial immunotherapy designs.

Action 2: Incentivizing Built-In Safety Mechanisms in Protein Engineering

(Actors: Funding agencies, academic researchers, biotech companies) Type: Incentive + technical strategy

Purpose

What is done now: Safety features (e.g., kill-switches, conditional activation) are often optional and not prioritized during early design.

Proposed change: Actively incentivize the inclusion of logic gates, protease-cleavable linkers, and reversible activation mechanisms in immune-engineered therapeutics.

Design

Grant agencies prioritize funding for projects with intrinsic safety features

Journals and conferences highlight “safety-by-design” approaches

Analogous to financial systems, where circuit breakers are built in to prevent cascading failures

Assumptions

Assumes safety mechanisms do not significantly reduce therapeutic efficacy

Assumes incentives meaningfully shape research and development priorities

Risks of Failure & “Success”

Failure: Added complexity may reduce reproducibility or increase manufacturing costs

“Success” risk: Overconfidence in built-in safeguards may reduce vigilance in clinical monitoring.

Action 3: Equity-Oriented Access and Deployment Oversight

(Actors: Governments, insurers, healthcare systems) Type: Policy incentive + access governance

Purpose

What is done now: Advanced biologics are often expensive and initially accessible only at specialized centers.

Proposed change: Establish governance mechanisms that ensure logic-gated precision immunotherapies do not exacerbate healthcare inequities.

Design

Public funding tied to commitments for broad clinical access

Value-based pricing linked to durability of response

Similar to drone regulation, where powerful tools are allowed but usage is restricted to approved, accountable contexts

Assumptions

Assumes early policy intervention can influence long-term pricing and access

Assumes healthcare systems can support equitable distribution

Risks of Failure & “Success”

Failure: Policies may be ignored or weakened under market pressure

“Success” risk: Reduced profit margins could decrease private-sector investment in innovation.

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.

I will prioritize a combination of Option 2 (Safety-by-Design Incentives) and Option 1 (Regulatory Immune-Specific Safety Requirements). While Option 3 (Equity-Oriented Access Oversight) is critical for the long term, it is less relevant to the immediate design phase and can be addressed as the therapy moves closer to clinical deployment. Option 2 is my primary choice because it embeds safety directly into the protein engineering process—using tools like logic gates and protease-cleavable linkers to prevent harm before it starts. This approach encourages innovation rather than slowing it down. I will support this with targeted elements of Option 1, specifically mandates to prevent “runaway” immune activation, acting as a necessary safety net for high-risk applications. I would like to propose this recommendation to the FDA (U.S. Food and Drug Administration), EMA (European Medicines Agency), and biotech industry leaders to help them establish “Safety-by-Design” standards for next-generation immunotherapies and ensure these powerful tools are developed responsibly.

Homework Questions and their Answers

Homework Questions from Professor Jacobson

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? Answer Error Rate of Polymerase Nature’s machinery for biological DNA synthesis, which involves an error-correcting polymerase, has an error rate of approximately 1 in 106 (one in a million). This is significantly more accurate than chemical synthesis methods, which have an error rate around 1 in 103. Comparison to the Human Genome The human genome consists of approximately (3 billion) base pairs. • The Discrepancy: If the polymerase were the only line of defense, an error rate of acting on a genome of base pairs would result in approximately 3,200 errors per genome replication. Size pf the genome is ~3.2 Gbp (3.2 × 10⁹ base pairs) If the error rate is 1 per 10⁶ bases, and the genome is 3.2 × 10⁹ bases, then: (3.2×10^9)/106 =3.2×10^3

Over many divisions, this accumulation of errors would be unsustainable for the organism. How Biology Deals with the Discrepancy Biology bridges the gap between the polymerase’s raw error rate and the high fidelity required for life through multi-layered error correction mechanisms:

1. Proofreading: The polymerase enzyme itself possesses 3’-5’ proofreading exonuclease activity. This allows it to detect and remove an incorrect base immediately after adding it, before continuing synthesis.
2. Mismatch Repair: For errors that escape the polymerase’s proofreading, cells utilize a mismatch repair system (involving proteins such as MutS, MutL, and MutH). This system scans the newly synthesized DNA for mismatches, identifies the new strand (often via methylation markers in bacteria), cleaves the error-containing segment, and allows enzymes to re-synthesize the correct sequence.

These systems work together to reduce the effective error rate well below the intrinsic rate of the polymerase alone.

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?

Answer Average human protein length: 1036 bp Since proteins are encoded by triplet codons: 1036" bp"÷3≈345" amino acids"

So an average human protein is ~345 amino acids long. How many different DNA sequences could code for that protein? Because the genetic code is degenerate: 18 amino acids have multiple codons Most amino acids average ~3 synonymous codons Some have 6 (Leu, Ser, Arg) Met and Trp have only 1 A commonly used approximation is: " 3 codons per amino acid (average)"

So the total number of possible DNA sequences is approximately: 3^345

Now compute magnitude: 3^345≈10164

That is: ~10¹⁶⁴ different possible DNA sequences that all encode the same average human protein. This is astronomically large. But in practice many fail because of: A. Codon Usage Bias Cells do not use synonymous codons equally. Some codons correspond to abundant tRNAs Rare codons slow translation Excess rare codons can stall ribosomes

B. mRNA Secondary Structure GC content strongly affects: Secondary structure Minimum free energy folding Stability Too much: GC → overly stable hairpins AT → unstable transcripts Strong secondary structures near the ribosome binding site can block translation.

C. GC Content Constraints Slides show examples of: 10% GC 50% GC 90% GC Extremely high or low GC: Affects melting temperature Affects synthesis efficiency Affects PCR amplification Alters stability

D. Regulatory Sequences Hidden in the DNA Some synonymous sequences may accidentally create: Cryptic splice sites Premature polyA signals Transcription terminators Ribosome pause sites miRNA binding sites

Homework Questions from Dr. LeProust

What’s the most commonly used method for oligo synthesis currently?

Answer

The most commonly used method for oligonucleotide synthesis is solid-phase phosphoramidite synthesis. The method was developed by Caruthers in 1981 and relies on a four-step cycle that is repeated for each nucleotide added: 1. Deblocking (Detritylation): Removing the protecting group (DMT) from the 5’ end. 2. Coupling: Adding the next base (phosphoramidite) to the growing chain. 3. Capping: Blocking any unreacted sites to prevent incorrect sequences from growing. 4. Oxidation: Stabilizing the phosphate linkage. Traditionally, this is done on a solid support such as controlled pore glass (CPG). However, modern high-throughput technologies, such as the platform used by Twist Bioscience, miniaturize this process by performing the synthesis on a silicon platform.

Why is it difficult to make oligos longer than 200nt via direct synthesis?

Answer

The most commonly used method for oligonucleotide synthesis is solid-phase phosphoramidite synthesis. The method was developed by Caruthers in 1981 and relies on a four-step cycle that is repeated for each nucleotide added: 1. Deblocking (Detritylation): Removing the protecting group (DMT) from the 5’ end. 2. Coupling: Adding the next base (phosphoramidite) to the growing chain. 3. Capping: Blocking any unreacted sites to prevent incorrect sequences from growing. 4. Oxidation: Stabilizing the phosphate linkage. Traditionally, this is done on a solid support such as controlled pore glass (CPG). However, modern high-throughput technologies, such as the platform used by Twist Bioscience, miniaturize this process by performing the synthesis on a silicon platform.

Why is it difficult to make oligos longer than 200nt via direct synthesis? The main reason is cumulative coupling inefficiency and error accumulation during solid-phase phosphoramidite synthesis.

Each nucleotide addition has a small error rate. Even if coupling efficiency is very high (e.g., 99–99.5%), the yield drops exponentially with length. If coupling efficiency = 99% per step: For a 200-mer: 〖0.99〗^200≈0.13

Only ~13% full-length product remains. At 300 bases: 〖0.99〗^300≈0.05

Only ~5% full-length product remains. So most molecules become truncated. Error Accumulation Increases with Length Error rates range from 1:200 to 1:3000 per nucleotide For a 200-mer at 1:2000 error rate: 200÷2000=0.1

That means ~10% of molecules contain at least one error. For a 500-mer: 500÷2000=0.25

~25% contain at least one mutation. So longer oligos accumulate substitutions and deletions.

Side Reactions and Chemical Damage As length increases: Depurination increases Incomplete deprotection accumulates Secondary structures form on the solid support Steric hindrance reduces coupling efficiency These effects compound with each synthesis cycle.

Purification Becomes Difficult Because most products are: Truncated sequences Single-base deletions N−1, N−2 fragments

Why can’t you make a 2000bp gene via direct oligo synthesis?

Answer

We cannot directly synthesize a 2000 bp gene because:

1. Yield decreases exponentially with length
2. Error rates accumulate linearly with length
3. Chemical degradation compounds over thousands of cycles
4. Purification becomes infeasible

Homework Questions from George Church

What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

Answer

The 10 Essential Amino Acids in All Animals

1. Histidine (H)
2. Isoleucine (I)
3. Leucine (L)
4. Lysine (K)
5. Methionine (M)
6. Phenylalanine (F)
7. Threonine (T)
8. Tryptophan (W)
9. Valine (V)
10. Arginine (R) (essential in most animals, especially during development) These are considered “essential” because animals lack the metabolic pathways to synthesize them.

How This Affects the “Lysine Contingency” The “Lysine contingency” refers to a proposed biocontainment strategy where an engineered organism is made dependent on lysine (or a lysine analogue) supplied externally. If lysine is unavailable in the environment, the organism cannot survive. If we consider:

• Lysine is essential in all animals

• Animals cannot synthesize lysine

• It must come from diet (ultimately from microbes/plants)

Key Implications: Lysine Is Abundant in Biology Because it is essential:

• It is widely present in food chains

• It exists in environmental biomass

• Many microbes synthesize it Therefore, engineering dependence on natural lysine is not strong containment — lysine is common in nature.

True Containment Requires a Non-Standard Amino Acid

This work sbecause:

• The organism depends on a synthetic amino acid

• That amino acid does not exist in nature

• Survival requires human supplementation This is much stronger than lysine dependency.

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project