AI Cite Prompts were directly based on the homework questions provided.
Homework Questions from Professor Jacobson 1. What is the error rate of polymerase? How does this compare to the human genome length, and how does biology address the discrepancy? Polymerase error rate:
~10⁻⁵ per base without proofreading; ~10⁻⁷–10⁻⁸ with proofreading; ~10⁻⁹–10⁻¹⁰ with mismatch repair.
3.1 mCherry I was thinking about observation under UV illumination — red fluorescence seems easier to detect compared to blue or green light. It also has stronger visual impact.
Protein Sequence MVSKGEEDNMAIIKEFMRFKVHMEGSVNGHEFEIEGEGEGRPYEGTQTAKLKVTKGGPLPFAWDILSPQFMYGSKAYVKHPADIPDYLKLSFPEGFKWERVMNFEDGGVVTVTQDSSLQDGEFIYKVKLRGTNFPSDGPVMQKKTMGWEASSERMYPEDGALKGEIKQRLKLKDGGHYDAEVKTTYKAKKPVQLPGAYNVNIKLDITSHNEDYTIVEQYERAEGRHSTGGMDELYK Source: https://www.fpbase.org/protein/mcherry/
3.2 Original DNA Sequence atggtgagcaaaggcgaagaagataacatggcgattattaaagaatttatgcgctttaaa gtgcatatggaaggcagcgtgaacggccatgaatttgaaattgaaggcgaaggcgaaggc cgcccgtatgaaggcacccagaccgcgaaactgaaagtgaccaaaggcggcccgctgccg tttgcgtgggatattctgagcccgcagtttatgtatggcagcaaagcgtatgtgaaacat ccggcggatattccggattatctgaaactgagctttccggaaggctttaaatgggaacgc gtgatgaactttgaagatggcggcgtggtgaccgtgacccaggatagcagcctgcaggat ggcgaatttatttataaagtgaaactgcgcggcaccaactttccgagcgatggcccggtg atgcagaaaaaaaccatgggctgggaagcgagcagcgaacgcatgtatccggaagatggc gcgctgaaaggcgaaattaaacagcgcctgaaactgaaagatggcggccattatgatgcg gaagtgaaaaccacctataaagcgaaaaaaccggtgcagctgccgggcgcgtataacgtg aacattaaactggatattaccagccataacgaagattataccattgtggaacagtatgaa cgcgcggaaggccgccatagcaccggcggcatggatgaactgtataaa 3.3 Codon Optimization Why Optimize? The same amino acid can be encoded by multiple codons, but different organisms have different codon usage preferences (tRNA abundance, translation efficiency, mRNA structure, etc.). To allow the host to express the protein more efficiently, codon optimization is necessary.
Published paper Villanueva-Cañas et al., PLOS ONE (2021) built a multi-station SARS-CoV-2 RT-qPCR testing workflow using Opentrons OT-2 robots. The core novelty is a reusable software + station architecture that makes a complex diagnostic pipeline programmable, modular, and reproducible across setups.
Final project automation plan Project: “Living Ice Cream” A temperature-responsive dessert system with:
Subsections of Homework
Week 1 HW: Principles and Practices
AI Cite
Prompts were directly based on the homework questions provided.
Homework Questions from Professor Jacobson
1. What is the error rate of polymerase? How does this compare to the human genome length, and how does biology address the discrepancy?
Polymerase error rate: ~10⁻⁵ per base without proofreading; ~10⁻⁷–10⁻⁸ with proofreading; ~10⁻⁹–10⁻¹⁰ with mismatch repair.
Human genome size: ~3 × 10⁹ base pairs.
Biological solutions: Proofreading, mismatch repair, diploidy, and natural selection.
2. How many different DNA codes can encode an average human protein? Why don’t all of them work in practice?
Theoretical number of encodings: Due to codon degeneracy, typically 10³–10⁶+ possible sequences.
Error accumulation during synthesis and replication
Homework Questions from Dr. LeProust
3. What is the most commonly used method for oligo synthesis today?
Phosphoramidite solid-phase synthesis.
4. Why is it difficult to synthesize oligos longer than ~200 nt directly?
Each coupling step is less than 100% efficient
Errors accumulate linearly with length
Yield and purity drop exponentially
5. Why can’t a 2000 bp gene be made by direct oligo synthesis?
Error rates become prohibitive
Full-length product yield approaches zero
Long genes must be assembled from shorter oligos (e.g., Gibson assembly, PCA)
Homework Question from George Church
What are the 10 essential amino acids in all animals?
The ten essential amino acids that animals cannot synthesize de novo and must obtain from diet are:
Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, Valine, and Arginine (Arginine is conditionally essential in adults but universally essential during growth.)
How does this affect the “Lysine Contingency”?
Lysine’s essentiality reflects a deep evolutionary constraint: animals universally lost lysine biosynthesis pathways, making them metabolically dependent on external sources. This supports the “lysine contingency” as a system-level lock-in rather than an arbitrary biochemical choice. Once lysine synthesis was abandoned, translational machinery, diet, and ecological dependencies co-evolved around its availability, making reversal highly unlikely. Thus, lysine exemplifies how early metabolic decisions constrain future evolutionary trajectories.
Week 2 HW: DNA Read, Write, & Edit
3.1 mCherry
I was thinking about observation under UV illumination — red fluorescence seems easier to detect compared to blue or green light. It also has stronger visual impact.
The same amino acid can be encoded by multiple codons, but different organisms have different codon usage preferences (tRNA abundance, translation efficiency, mRNA structure, etc.). To allow the host to express the protein more efficiently, codon optimization is necessary.
Organism Selected
E. coli K-12.
It is one of the most commonly used host strains. The technical maturity and widespread adoption of this system make it highly suitable for experimental work.
(There’s unknown error for the optimization version,,,)
5.1 DNA Read
5.1(i) What DNA Would I Sequence?
A synthetic DNA library used for digital DNA data storage.
Artificially designed DNA fragments encoding digital information (text or images).
Why?
DNA here serves as an information storage medium rather than biological genetic material.
Sequencing verifies:
Whether the written digital information is preserved
Whether errors occurred during storage or amplification
The error rate (substitutions, insertions, deletions)
This is effectively a biotechnology-based data integrity check.
5.1(ii) Sequencing Technology
First-generation sequencing: Sanger sequencing.
Why?
High accuracy
Suitable for validating single fragments
Characteristics
Reads one DNA template at a time
Read length ~700–900 bp
Very high accuracy
Inputs
Template DNA
Primer
DNA polymerase
dNTPs
Fluorescently labeled ddNTPs
Core Principle
DNA synthesis is terminated at random positions using ddNTPs.
Process:
Polymerase copies the template
ddNTP incorporation stops elongation
Fragments of different lengths are produced
Fragments separated by size
Fluorescent signal read to reconstruct sequence
Output
DNA sequence
Chromatogram
Limitations
Cannot sequence thousands of fragments simultaneously
5.2 DNA Write
5.2(i) What DNA Would I Synthesize?
An expression cassette expressing mCherry in E. coli.
Includes:
Promoter
RBS
Codon-optimized mCherry CDS
His tag
Terminator
Reason:
Produces visible red fluorescence
Strong contrast under blue light
5.2(ii) Synthesis Technology
Solid-phase chemical DNA synthesis (phosphoramidite method) + gene assembly.
Steps
Design sequence computationally
Split into short oligos
Chemically synthesize oligos
Assemble via PCR or Gibson Assembly
Clone into vector
Sequence verify
Limitations
Error rate increases with length
Assembly required
Sequencing verification required
Cost scales with length
5.3 DNA Edit
5.3(i) What DNA Would I Edit?
A single-base mutation (e.g., disease-causing point mutation).
Reason:
Represents the most precise editing scenario
Relevant for therapeutic research
5.3(ii) Editing Technology
CRISPR-Cas9 + HDR repair template.
Principle
Design gRNA
Cas9 creates double-strand break
Provide donor DNA
HDR replaces base precisely
Required Inputs
gRNA
Cas9 protein or plasmid
Donor DNA
Target cells
Limitations
Low HDR efficiency
Possible off-target effects
Complex delivery
Cell-type dependent precision
Week 3 HW: AUTOMATION
1) Published paper
Villanueva-Cañas et al., PLOS ONE (2021) built a multi-station SARS-CoV-2 RT-qPCR testing workflow using Opentrons OT-2 robots. The core novelty is a reusable software + station architecture that makes a complex diagnostic pipeline programmable, modular, and reproducible across setups.
Visual shift (color / glow) near melt-adjacent temperatures
Why Ginkgo automation
I’m using Ginkgo’s autonomous / cloud-lab framing as an iteration engine for high-throughput DOE: stable automation backbone, fast experimental loops, and standardized readouts for repeated screening rounds.
What I will automate
A) “Breathing” kinetics screening (high-throughput DOE)
Goal: Find enzyme/substrate + formulation conditions that yield slow, non-violent micro-gas behavior around ~15–25°C.
