Week 1 HW: Principles and Practices

1. Describe a biological engineering application or tool you want to develop and why?

2. 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 A: Activation Reliability and Biological Safety

Sub Goals

Goal B: Transparency and Clinical Oversight

Sub Goals

Goal C: Misuse Prevention and Responsible Innovation

Sub Goals

3. Describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”)

Option 1: Make reliability testing mandatory for ligand-gated systems

Purpose: Right now, engineered systems are mostly evaluated for general safety. Here, since activation depends on a specific ligand, we need to make sure the system does not accidentally turn ON. So the purpose is to make reliability testing of the ON-switch compulsory.

Design: Before approval, developers must show that:

• The system stays OFF without ligand.
• It activates only above a defined ligand threshold.
• It shuts down when ligand is removed.
• Independent validation labs could verify this data.

Assumptions:

• We assume activation behavior can be measured clearly.
• We also assume lab testing reflects real biological environments.

Risks of Failure & “Success”: Failure:

• Real biology might behave differently than controlled testing.
• Testing may not capture rare edge cases.

Success:

• People may assume “certified” means perfectly safe.
• Could slow down research due to added requirements.

Option 2: Regulate access and control of the activation ligand

Purpose: Since the system only activates when a specific ligand is present, controlling that ligand is another safety layer.

Design:

• Track production and distribution of the ligand.
• Define clear usage protocols in clinical or agricultural settings.
• Limit large-scale uncontrolled exposure.

Assumptions:

• We assume ligand distribution can realistically be tracked.
• We assume exposure pathways are predictable.

Risks of Failure & “Success”:

• Environmental exposure or misuse.
• Informal production channels.

Success:

• Makes implementation more complex.
• May restrict access in low-resource settings.

Option 3: Long-term monitoring after deployment

Purpose: Even if the system looks perfect in testing, real-world biology is messy. So long-term monitoring is important.

Design:

• Create reporting systems for unexpected activation events.
• Track long-term behavior in therapies.
• Require periodic review of system performance.

Assumptions:

• We assume activation events can be detected.
• We assume institutions will report honestly.

Risks of Failure & “Success”: Failure:

• Poor reporting.
• Funding limitations.

Success:

• Privacy concerns if monitoring is too intrusive.
• Public misunderstanding of activation data.

4. 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:

5. 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 would prioritize Option 1 and Option 3 together.

Option 1 is important as prevention is better than reaction. If the ON-switch is unreliable, the whole concept becomes unsafe. So strong reliability testing is essential.

Option 3 is equally important because biological systems are complex and unpredictable. Even if testing looks perfect, real-world behavior may differ. Long-term monitoring allows correction if unexpected activation occurs.

Option 2 is useful but more secondary. It adds a safety layer, but ligand control alone cannot guarantee safety, especially if exposure pathways are hard to fully regulate.

Trade-offs considered:

• Stricter regulation may slow down innovation.
• Monitoring systems may create privacy concerns.
• Too much control could limit accessibility.

However, since ligand-gated episomal systems introduce conditional biological activation, reliability and oversight should come first.

Week 2 HW: DNA Read Write and Edit

Week 1 Lecture Prep

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?
Answer:
DNA polymerases with proofreading (like human replicative polymerases) have an intrinsic error rate of approximately 10⁻⁶ to 10⁻⁷ errors per base per replication. After mismatch repair, the final in vivo mutation rate is approximately 10⁻⁹ to 10⁻¹⁰ per base per cell division. The human genome is ~3.2 × 10⁹ base pairs. If replication occurred at 10⁻⁶ error rate without repair, it causes 3 × 10⁹ bp × 10⁻⁶ ≈ ~3,000 mutations per cell division. But observed mutation rates are closer to ~0.1–1 mutations per genome per division. Biology resolves this discrepancy by using a layered fidelity systems which includes, Base selectivity of polymerase, 3’ - 5’ exonuclease proofreading, DNA damage repair pathways and Mismatch repair (MMR) system. Together these reduce the errors.

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?
Answer:

As we know, average human protein has approximately, 1036 bp coding sequence or 345 Amino Acids (AA). And each amino acids except few is coded by multiple codons. If the average degeneracy is 3 codons per amino acid, the Total possible DNA sequences will be approximately, 3^{345 or 10}164 possible sequences, which is way large. All possible codes wont work because of few factors. One is codon usage bias, which affect translation efficiency. Second is mRNA secondary structure, where high GC and hairpins block ribosome binding, third is the Regulatory motifs which causes Cryptic splice sites in polyA signals, Fourth is Repetitive elements that cause instability in synthesis, fifth is GC content extremes which affects stability and expression and finally, Protein folding kinetics causes Codon speed influences co-translational folding.

Homework Questions from Dr. LeProust

1. What’s the most commonly used method for oligo synthesis currently?
Answer:
Phosphoramidite solid-phase chemical synthesis is the commonly used method for oligo synthesis currently. It is developed in the 1980s.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis?
Answer:
Each base addition has a 99–99.5% coupling efficiency. So, even at 99.5%, ie, after 200 cycles, (0.995)^200 which gives approximately 0.37 or gives 37% full product and errors accumulate exponentially and Also, Depurination, Incomplete deprotection and Side reactions. So purity collapses beyond ~200nt.

3. Why can’t you make a 2000bp gene via direct oligo synthesis?
Answer:
It is because (0.995)^2000 = 0.00004, which is approximately zero or it is essentially a zero full-length product. So, Genes are assembled from shorter oligos using methods like Gibson Assembly, PCR assembly and Chip-based multiplex synthesis.

Homework Questions from George Church

1. What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?
Answer:
Essential Amino Acids in Animals are:

• Histidine
• Isoleucine
• Leucine
• Lysine
• Methionine
• Phenylalanine
• Threonine
• Tryptophan
• Valine
• Arginine

Lysine contigency is about engineering organisms so that their survival depends on external lysine supply as it is Essential amino acid is not synthesized by animals and hence, It becomes a powerful control metabolite. Som If an engineered organism is made auxotrophic for lysine, it cannot survive outside controlled environments and prevents ecological escape and this strengthens synthetic biocontainment. This makes lysine a biological kill switch and can be extensively used for selection purposes.
Reference
Mandell, D., Lajoie, M., Mee, M. et al. Biocontainment of genetically modified organisms by synthetic protein design. Nature 518, 55–60 (2015). https://doi.org/10.1038/nature14121

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project