Week 1 HW: Principles and Practices

A Living Anti-Corrosion System for Ocean Infrastructure:

I propose a biologically engineered, self-healing anti-corrosion coating for offshore and ocean-energy infrastructure (tidal turbines, wave energy converters, offshore wind foundations).The system uses genetically engineered, non-pathogenic marine bacteria embedded in a sealed, porous coating. These microbes are designed to:

1. Detect early corrosion signals (pH drop, Fe²⁺ ion release)
2. Respond by precipitating protective minerals (e.g., calcium carbonate)
3. Neutralize corrosive microenvironments
4. Signal early warnings before structural failure

Governance Policy Goals To ensure this application contributes to an ethical future and prevents harm, governance should pursue the following goals:

1. Ensure safety and security: a) Prevent environmental release or misuse of engineered organisms and b) Avoid ecological disruption or biosecurity risks
2. Promote constructive and beneficial use a) Direct innovation toward public-interest infrastructure (renewable energy, climate resilience) and b)Prevent purely extractive or environmentally harmful deployment

Action 1: Mandatory Biological Containment & Kill-Switch Standards Purpose: What is done now: Chemical anti-corrosion coatings are regulated mainly for toxicity, not biological behavior.

Proposed change: Require all engineered microbes used in marine infrastructure to include:

1. Genetic kill switches
2. Nutrient dependency (cannot survive outside coating)
3. Physical encapsulation in sealed matrices
4. Maintain transparency and public trust Design : Needed - International biosafety certification for “contained-use marine bio-systems” Assumptions: Kill switches will function reliably in harsh marine environments What could be wrong:Evolutionary escape mechanisms

Action 2: Environmental impact and Community Consent framework Purpose What is done now: Environmental Impact Assessments (EIAs) often focus on physical structures, not biological agents.

Proposed change: Require Bio-Environmental Impact Assessments (Bio-EIAs) that include:

1. Long-term microbial ecosystem modeling
2. Transparent disclosure of organism function
3. Consultation with coastal and fishing communities Design: Needed- Continuous post-deployment monitoring Assumptions: Communities can meaningfully engage with technical information What could be wrong:Information asymmetry and monitoring fatigue over time

Action 3: Restricted Use Licensing (Purpose-Bound Deployment) Purpose What is done now: Biotechnologies often spread from research into unintended domains (e.g., CRISPR kits, dual-use chemicals).

Proposed change: License this technology only for defined applications:

1. Renewable energy infrastructure
2. Public maritime assets Design: Needed- Purpose-specific approval, audits of deployment sites and clear penalties for misuse Assumptions: Clear boundaries between “civil” and “non-civil” uses exist What could be wrong: Commercial influence to expand scope

Next, 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:

Based on the scores, I would prioritize Option 1 (Bio-containment standards) together with Option 3 (Impact and community consent reviews).

Option 1 comes first because safety has to be the foundation. When we introduce engineered biological systems into the ocean, the biggest risk is that something spreads, mutates, or behaves in ways we didn’t expect. Strong bio-containment rules like built-in kill switches or limits on survival outside controlled conditions help prevent accidents before they happen. Without this layer, even well-intended projects could cause long-term environmental harm.

Option 3 is equally important because it helps make sure the technology is actually used for good. Environmental impact checks and community consent force developers to think beyond the lab and consider real ocean ecosystems and the people who depend on them. This option scored highest for promoting constructive and beneficial uses because it guides innovation toward solutions that are socially and environmentally responsible, not just technically impressive.

I would not rely on Option 2 (Voluntary guidelines) on its own. While voluntary rules can encourage early innovation, they are easy to ignore and don’t offer strong protection when risks are high. For ocean systems, where damage can be difficult or impossible to reverse, voluntary measures are not enough.

Overall, combining strong safety rules with environmental and community oversight offers the most realistic and responsible way to move forward. It protects the ocean while still allowing beneficial innovation to happen.

References:

1. Jin, H., Wang, J., Tian, L., Gao, M., Zhao, J., & Ren, L. (2022). Recent advances in emerging integrated antifouling and anticorrosion coatings. Materials & Design, 213, 110307. https://doi.org/10.1016/j.matdes.2021.110307

2. Li, Y., & Ning, C. (2019). Latest research progress of marine microbiological corrosion and bio-fouling, and new approaches of marine anti-corrosion and anti-fouling. Bioactive Materials, 4, 189–195. https://doi.org/10.1016/j.bioactmat.2019.04.003

Week 2 HW: DNA - read, write and edit

Part 1- Benchling & In-silico Gel Art

Simulating Restriction Enzyme Digestion with the following Enzymes:

.EcoRI

.HindIII

.BamHI

.KpnI

.EcoRV

.SacI

.SalI

I had created a simple pattern in the style of Paul Vanouse’s Latent Figure Protocol artworks. The bands are arranged alternatively creating a horizontal alternative pattern.

Part 3: DNA Design Challenge

3.1. Choose your protein The protein I had selected is Myosin, which is a motor protein responsible for muscle contraction. It binds to the actin filaments and uses ATP to generate force. This force pulls the actin filaments inwards causes the muscle fibres to shorten and contract. I am particularly interested in understanding how myosin behaves in microgravity conditions, where mechanical loading is absent and muscle atrophy occurs rapidly. I collected the protein from human at Uniprot- Q9Y2K3

Below is the protein sequence of Myosin-15 extracted from Homo sapien

sp|Q9Y2K3|MYH15_HUMAN Myosin-15 OS=Homo sapiens OX=9606 GN=MYH15 PE=1 SV=6 MDLSDLGEAAAFLRRSEAELLLLQATALDGKKKCWIPDGENAYIEAEVKGSEDDGTVIVE TADGESLSIKEDKIQQMNPPEFEMIEDMAMLTHLNEASVLHTLKRRYGQWMIYTYSGLFC VTINPYKWLPVYQKEVMAAYKGKRRSEAPPHIFAVANNAFQDMLHNRENQSILFTGESGA GKTVNSKHIIQYFATIAAMIESRKKQGALEDQIMQANTILEAFGNAKTLRNDNSSRFGKF
IRMHFGARGMLSSVDIDIYLLEKSRVIFQQAGERNYHIFYQILSGQKELHDLLLVSANPS DFHFCSCGAVTVESLDDAEELLATEQAMDILGFLPDEKYGCYKLTGAIMHFGNMKFKQKP REEQLEADGTENADKAAFLMGINSSELVKCLIHPRIKVGNEYVTRGQTIEQVTCAVGALS KSMYERMFKWLVARINRALDAKLSRQFFIGILDITGFEILEYNSLEQLCINFTNEKLQQF FNWHMFVLEQEEYKKESIEWVSIGFGLDLQACIDLIEKPMGILSILEEECMFPKATDLTF KTKLFDNHFGKSVHLQKPKPDKKKFEAHFELVHYAGVVPYNISGWLEKNKDLLNETVVAV FQKSSNRLLASLFENYMSTDSAIPFGEKKRKKGASFQTVASLHKENLNKLMTNLKSTAPH FVRCINPNVNKIPGILDPYLVLQQLRCNGVLEGTRICREGFPNRLQYADFKQRYCILNPR TFPKSKFVSSRKAAEELLGSLEIDHTQYRFGITKVFFKAGFLGQLEAIRDERLSKVFTLF QARAQGKLMRIKFQKILEERDALILIQWNIRAFMAVKNWPWMRLFFKIKPLVKSSEVGEE VAGLKEECAQLQKALEKSEFQREELKAKQVSLTQEKNDLILQLQAEQETLANVEEQCEWL IKSKIQLEARVKELSERVEEEEEINSELTARGRKLEDECFELKKEIDDLETMLVKSEKEK RTTEHKVKNLTEEVEFLNEDISKLNRAAKVVQEAHQQTLDDLHMEEEKLSSLSKANLKLE QQVDELEGALEQERKARMNCERELHKLEGNLKLNRESMENLESSQRHLAEELRKKELELS QMNSKVENEKGLVAQLQKTVKELQTQIKDLKEKLEAERTTRAKMERERADLTQDLADLNE RLEEVGGSSLAQLEITKKQETKFQKLHRDMEEATLHFETTSASLKKRHADSLAELEGQVE NLQQVKQKLEKDKSDLQLEVDDLLTRVEQMTRAKANAEKLCTLYEERLHEATAKLDKVTQ LANDLAAQKTKLWSESGEFLRRLEEKEALINQLSREKSNFTRQIEDLRGQLEKETKSQSA LAHALQKAQRDCDLLREQYEEEQEVKAELHRTLSKVNAEMVQWRMKYENNVIQRTEDLED AKKELAIRLQEAAEAMGVANARNASLERARHQLQLELGDALSDLGKVRSAAARLDQKQLQ SGKALADWKQKHEESQALLDASQKEVQALSTELLKLKNTYEESIVGQETLRRENKNLQEE ISNLTNQVREGTKNLTEMEKVKKLIEEEKTEVQVTLEETEGALERNESKILHFQLELLEA KAELERKLSEKDEEIENFRRKQQCTIDSLQSSLDSEAKSRIEVTRLKKKMEEDLNEMELQ LSCANRQVSEATKSLGQLQIQIKDLQMQLDDSTQLNSDLKEQVAVAERRNSLLQSELEDL RSLQEQTERGRRLSEEELLEATERINLFYTQNTSLLSQKKKLEADVARMQKEAEEVVQEC QNAEEKAKKAAIEAANLSEELKKKQDTIAHLERTRENMEQTITDLQKRLAEAEQMALMGS RKQIQKLESRVRELEGELEGEIRRSAEAQRGARRLERCIKELTYQAEEDKKNLSRMQTQM DKLQLKVQNYKQQVEVAETQANQYLSKYKKQQHELNEVKERAEVAESQVNKLKIKAREFG KKVQEE

3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.

The Central Dogma gives us the framework to work backwards from a given protein sequence and infer the DNA sequence that the protein is derived from. Reverse Translate results Results for 1926 residue sequence “sp|Q9Y2K3|MYH15_HUMAN Myosin-15 OS=Homo sapiens OX=9606 GN=MYH15 PE=1 SV=6” starting “MDLSDLGEAA”

reverse translation of sp|Q9Y2K3|MYH15_HUMAN Myosin-15 OS=Homo sapiens OX=9606 GN=MYH15 PE=1 SV=6 to a 5778 base sequence of most likely codons. atggatctgagcgatctgggcgaagcggcggcgtttctgcgccgcagcgaagcggaactg ctgctgctgcaggcgaccgcgctggatggcaaaaaaaaatgctggattccggatggcgaa aacgcgtatattgaagcggaagtgaaaggcagcgaagatgatggcaccgtgattgtggaa accgcggatggcgaaagcctgagcattaaagaagataaaattcagcagatgaacccgccg gaatttgaaatgattgaagatatggcgatgctgacccatctgaacgaagcgagcgtgctg cataccctgaaacgccgctatggccagtggatgatttatacctatagcggcctgttttgc gtgaccattaacccgtataaatggctgccggtgtatcagaaagaagtgatggcggcgtat aaaggcaaacgccgcagcgaagcgccgccgcatatttttgcggtggcgaacaacgcgttt caggatatgctgcataaccgcgaaaaccagagcattctgtttaccggcgaaagcggcgcg ggcaaaaccgtgaacagcaaacatattattcagtattttgcgaccattgcggcgatgatt gaaagccgcaaaaaacagggcgcgctggaagatcagattatgcaggcgaacaccattctg gaagcgtttggcaacgcgaaaaccctgcgcaacgataacagcagccgctttggcaaattt attcgcatgcattttggcgcgcgcggcatgctgagcagcgtggatattgatatttatctg ctggaaaaaagccgcgtgatttttcagcaggcgggcgaacgcaactatcatattttttat cagattctgagcggccagaaagaactgcatgatctgctgctggtgagcgcgaacccgagc gattttcatttttgcagctgcggcgcggtgaccgtggaaagcctggatgatgcggaagaa ctgctggcgaccgaacaggcgatggatattctgggctttctgccggatgaaaaatatggc tgctataaactgaccggcgcgattatgcattttggcaacatgaaatttaaacagaaaccg cgcgaagaacagctggaagcggatggcaccgaaaacgcggataaagcggcgtttctgatg ggcattaacagcagcgaactggtgaaatgcctgattcatccgcgcattaaagtgggcaac gaatatgtgacccgcggccagaccattgaacaggtgacctgcgcggtgggcgcgctgagc aaaagcatgtatgaacgcatgtttaaatggctggtggcgcgcattaaccgcgcgctggat gcgaaactgagccgccagttttttattggcattctggatattaccggctttgaaattctg gaatataacagcctggaacagctgtgcattaactttaccaacgaaaaactgcagcagttt tttaactggcatatgtttgtgctggaacaggaagaatataaaaaagaaagcattgaatgg gtgagcattggctttggcctggatctgcaggcgtgcattgatctgattgaaaaaccgatg ggcattctgagcattctggaagaagaatgcatgtttccgaaagcgaccgatctgaccttt aaaaccaaactgtttgataaccattttggcaaaagcgtgcatctgcagaaaccgaaaccg gataaaaaaaaatttgaagcgcattttgaactggtgcattatgcgggcgtggtgccgtat aacattagcggctggctggaaaaaaacaaagatctgctgaacgaaaccgtggtggcggtg tttcagaaaagcagcaaccgcctgctggcgagcctgtttgaaaactatatgagcaccgat agcgcgattccgtttggcgaaaaaaaacgcaaaaaaggcgcgagctttcagaccgtggcg agcctgcataaagaaaacctgaacaaactgatgaccaacctgaaaagcaccgcgccgcat tttgtgcgctgcattaacccgaacgtgaacaaaattccgggcattctggatccgtatctg gtgctgcagcagctgcgctgcaacggcgtgctggaaggcacccgcatttgccgcgaaggc tttccgaaccgcctgcagtatgcggattttaaacagcgctattgcattctgaacccgcgc acctttccgaaaagcaaatttgtgagcagccgcaaagcggcggaagaactgctgggcagc ctggaaattgatcatacccagtatcgctttggcattaccaaagtgttttttaaagcgggc tttctgggccagctggaagcgattcgcgatgaacgcctgagcaaagtgtttaccctgttt caggcgcgcgcgcagggcaaactgatgcgcattaaatttcagaaaattctggaagaacgc gatgcgctgattctgattcagtggaacattcgcgcgtttatggcggtgaaaaactggccg tggatgcgcctgttttttaaaattaaaccgctggtgaaaagcagcgaagtgggcgaagaa gtggcgggcctgaaagaagaatgcgcgcagctgcagaaagcgctggaaaaaagcgaattt cagcgcgaagaactgaaagcgaaacaggtgagcctgacccaggaaaaaaacgatctgatt ctgcagctgcaggcggaacaggaaaccctggcgaacgtggaagaacagtgcgaatggctg attaaaagcaaaattcagctggaagcgcgcgtgaaagaactgagcgaacgcgtggaagaa gaagaagaaattaacagcgaactgaccgcgcgcggccgcaaactggaagatgaatgcttt gaactgaaaaaagaaattgatgatctggaaaccatgctggtgaaaagcgaaaaagaaaaa cgcaccaccgaacataaagtgaaaaacctgaccgaagaagtggaatttctgaacgaagat attagcaaactgaaccgcgcggcgaaagtggtgcaggaagcgcatcagcagaccctggat gatctgcatatggaagaagaaaaactgagcagcctgagcaaagcgaacctgaaactggaa cagcaggtggatgaactggaaggcgcgctggaacaggaacgcaaagcgcgcatgaactgc gaacgcgaactgcataaactggaaggcaacctgaaactgaaccgcgaaagcatggaaaac ctggaaagcagccagcgccatctggcggaagaactgcgcaaaaaagaactggaactgagc cagatgaacagcaaagtggaaaacgaaaaaggcctggtggcgcagctgcagaaaaccgtg aaagaactgcagacccagattaaagatctgaaagaaaaactggaagcggaacgcaccacc cgcgcgaaaatggaacgcgaacgcgcggatctgacccaggatctggcggatctgaacgaa cgcctggaagaagtgggcggcagcagcctggcgcagctggaaattaccaaaaaacaggaa accaaatttcagaaactgcatcgcgatatggaagaagcgaccctgcattttgaaaccacc agcgcgagcctgaaaaaacgccatgcggatagcctggcggaactggaaggccaggtggaa aacctgcagcaggtgaaacagaaactggaaaaagataaaagcgatctgcagctggaagtg gatgatctgctgacccgcgtggaacagatgacccgcgcgaaagcgaacgcggaaaaactg tgcaccctgtatgaagaacgcctgcatgaagcgaccgcgaaactggataaagtgacccag ctggcgaacgatctggcggcgcagaaaaccaaactgtggagcgaaagcggcgaatttctg cgccgcctggaagaaaaagaagcgctgattaaccagctgagccgcgaaaaaagcaacttt acccgccagattgaagatctgcgcggccagctggaaaaagaaaccaaaagccagagcgcg ctggcgcatgcgctgcagaaagcgcagcgcgattgcgatctgctgcgcgaacagtatgaa gaagaacaggaagtgaaagcggaactgcatcgcaccctgagcaaagtgaacgcggaaatg gtgcagtggcgcatgaaatatgaaaacaacgtgattcagcgcaccgaagatctggaagat gcgaaaaaagaactggcgattcgcctgcaggaagcggcggaagcgatgggcgtggcgaac gcgcgcaacgcgagcctggaacgcgcgcgccatcagctgcagctggaactgggcgatgcg ctgagcgatctgggcaaagtgcgcagcgcggcggcgcgcctggatcagaaacagctgcag agcggcaaagcgctggcggattggaaacagaaacatgaagaaagccaggcgctgctggat gcgagccagaaagaagtgcaggcgctgagcaccgaactgctgaaactgaaaaacacctat gaagaaagcattgtgggccaggaaaccctgcgccgcgaaaacaaaaacctgcaggaagaa attagcaacctgaccaaccaggtgcgcgaaggcaccaaaaacctgaccgaaatggaaaaa gtgaaaaaactgattgaagaagaaaaaaccgaagtgcaggtgaccctggaagaaaccgaa ggcgcgctggaacgcaacgaaagcaaaattctgcattttcagctggaactgctggaagcg aaagcggaactggaacgcaaactgagcgaaaaagatgaagaaattgaaaactttcgccgc aaacagcagtgcaccattgatagcctgcagagcagcctggatagcgaagcgaaaagccgc attgaagtgacccgcctgaaaaaaaaaatggaagaagatctgaacgaaatggaactgcag ctgagctgcgcgaaccgccaggtgagcgaagcgaccaaaagcctgggccagctgcagatt cagattaaagatctgcagatgcagctggatgatagcacccagctgaacagcgatctgaaa gaacaggtggcggtggcggaacgccgcaacagcctgctgcagagcgaactggaagatctg cgcagcctgcaggaacagaccgaacgcggccgccgcctgagcgaagaagaactgctggaa gcgaccgaacgcattaacctgttttatacccagaacaccagcctgctgagccagaaaaaa aaactggaagcggatgtggcgcgcatgcagaaagaagcggaagaagtggtgcaggaatgc cagaacgcggaagaaaaagcgaaaaaagcggcgattgaagcggcgaacctgagcgaagaa ctgaaaaaaaaacaggataccattgcgcatctggaacgcacccgcgaaaacatggaacag accattaccgatctgcagaaacgcctggcggaagcggaacagatggcgctgatgggcagc cgcaaacagattcagaaactggaaagccgcgtgcgcgaactggaaggcgaactggaaggc gaaattcgccgcagcgcggaagcgcagcgcggcgcgcgccgcctggaacgctgcattaaa gaactgacctatcaggcggaagaagataaaaaaaacctgagccgcatgcagacccagatg gataaactgcagctgaaagtgcagaactataaacagcaggtggaagtggcggaaacccag gcgaaccagtatctgagcaaatataaaaaacagcagcatgaactgaacgaagtgaaagaa cgcgcggaagtggcggaaagccaggtgaacaaactgaaaattaaagcgcgcgaatttggc aaaaaagtgcaggaagaa

I used https://www.bioinformatics.org/sms2/rev_trans.html to reverse translate the protein sequence back to the nucleotides.

3.3. Codon optimization Codon optimization is the process of changing the DNA sequence of a gene so that it is expressed more efficiently in a specific organism without changing the protein it produces. I used https://www.idtdna.com/CodonOpt to do codon optimization. I used the reverse translated sequence from 3.2 step, because I was interested in that sequence. I want to continue the process and went for codon optimizaton of the reverse translated sequence.

Myosin protein DNA-seq with codon optimization

3.4. You have a sequence! Now what?

After obtaining the myosin protein sequence from UniProt and reverse translating it into a DNA coding sequence, the gene can be optimized for expression in a suitable host such as Escherichia coli or mammalian cells. The optimized gene is inserted into an expression plasmid under a strong promoter. Once introduced into the host cells, RNA polymerase transcribes the DNA into mRNA. The ribosome then translates the mRNA into the myosin polypeptide by reading codons and assembling the corresponding amino acids. After translation, the protein folds into its functional three-dimensional structure and can be purified using affinity chromatography techniques.

Part 4: Prepare a Twist DNA Synthesis Order

I successfully logged in both Twist and benchling. I followed the steps mentioned on the week2 homework site, mapped the sequence, and then completed the annotation(4.2)

Here are my results for the step 4.2:

The image depicts the linear map of the mapped sequence

Subsequently, I uploaded the downloaded data in FASTA format to Twist. I selected the clonal genes option and uploaded the file. I then chose the pTwist Amp High Copy vector and dowloaded the resulting sequence. Later, I uploaded the downloaded data from Twist into Benchling. Resulted in creation of a ‘beautiful’ plasmid construct

Part 5: DNA Read/Write/Edit

5.1 DNA Read

(i) What DNA would you want to sequence (e.g., read) and why?

For DNA sequencing, I would focus on microalgae and fungi that have strong potential for carbon capture and air purification. Species such as Chlorella vulgaris, Spirulina platensis, and Aspergillus niger are promising because they can absorb carbon dioxide, tolerate environmental stress, and in some cases degrade pollutants.

(ii) In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why? To perform sequencing, I would use Next-Generation Sequencing (NGS), specifically short-read sequencing developed by Illumina.

Also answer the following questions:

1.Is your method first-, second- or third-generation or other? How so?

This is a second-generation sequencing technology because it performs massively parallel sequencing of millions of DNA fragments simultaneously using sequencing-by-synthesis chemistry.

2.What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.

1. DNA extraction
2. Fragmentation
3. Adapter ligation
4. PCR amplification
5. Load library onto flow cell
6. Software converts color signals → A, T, C, G (base calling)

3.What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)?

1. DNA fragments bind to flow cell
2. Bridge amplification creates clusters
3. Fluorescently labeled nucleotides are added
4. Each base emits a specific color signal
5. A camera records the fluorescence
6. Software converts color signals → A, T, C, G (base calling)

4.What is the output of your chosen sequencing technology?

1. FASTQ files (raw reads with quality scores)
2. Millions of short reads (150–300 bp)
3. After assembly → full genome sequence

5.2 DNA Write

(i) What DNA would you want to synthesize (e.g., write) and why?

For DNA synthesis, I would design a synthetic gene cassette to enhance carbon capture efficiency in microalgae. The construct would include a strong promoter, ribosome binding site, an optimized RuBisCO gene, and a terminator. Additional genes for stress tolerance or pollutant degradation could also be incorporated. The goal would be to create a genetic circuit that increases CO₂ fixation and improves survival in polluted environments.

(ii) What technology or technologies would you use to perform this DNA synthesis and why?

DNA synthesis would be performed using commercial gene synthesis services such as Twist Bioscience. The process involves digital DNA design, chemical synthesis of short oligonucleotides, assembly into a full-length gene, error correction, cloning into a plasmid, and sequence verification. Limitations include higher costs for long sequences, challenges with GC-rich regions, and potential synthesis errors. However, synthetic DNA enables precise control over gene design and optimization.

5.3 DNA Edit

(i) What DNA would you want to edit and why?

To further improve air filtration capabilities, I would edit the genomes of selected algae or fungi to enhance carbon fixation, increase pollutant tolerance, or remove metabolic bottlenecks. Genome editing could allow insertion of stronger promoters, modification of enzyme efficiency, or deletion of growth-limiting genes.

(ii) What technology or technologies would you use to perform these DNA edits and why?

The preferred editing tool would be CRISPR-Cas9. This system uses a guide RNA to direct the Cas9 enzyme to a specific DNA sequence, where it introduces a double-strand break.

Also answer the following questions:

1. How does your technology of choice edit DNA? What are the essential steps?

a.Design guide RNA (gRNA) targeting gene b.Deliver Cas9 + gRNA into cell c.Cas9 creates double-strand break d.Cell repairs break: NHEJ → knockout and HDR → precise insertion

2. What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing?

Inputs Required a.Guide RNA b.Cas9 protein or plasmid c.Donor DNA template (for HDR) d.Host cells e.Transformation method

3. What are the limitations of your editing methods (if any) in terms of efficiency or precision?

Limitations include potential off-target mutations, variable editing efficiency, and delivery challenges in certain microalgal species. Ecological and regulatory considerations must also be addressed before environmental deployment.

Conclusion By integrating DNA sequencing, synthesis, and genome editing, it is possible to design and engineer enhanced biological air filtration systems. Sequencing reveals the natural genetic toolkit of algae and fungi, synthetic DNA enables rational design of improved pathways, and CRISPR-based editing allows precise genome modifications. Together, these technologies provide a powerful framework for developing sustainable, living solutions to air pollution and climate change mitigation.

References

1.Kumar, P., Arora, K., Chanana, I., Kulshreshtha, S., Thakur, V., & Choi, K.-Y. (2023). Comparative study on conventional and microalgae-based air purifiers: Paving the way for sustainable green spaces. Journal of Environmental Chemical Engineering, 11(6), 111046. https://doi.org/10.1016/j.jece.2023.111046

2.Marycz, M., Brillowska-Dąbrowska, A., Muñoz, R., Gębicki, J., et al. (2021). A state of the art review on the use of fungi in biofiltration to remove volatile hydrophobic pollutants. Reviews in Environmental Science and Bio/Technology. https://doi.org/10.1007/s11157-021-09608-7

Week 2 pre HW: DNA Read, Write and Edit

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? Ans: DNA polymerase has an inherent error rate of approximately 1 in 10⁶ bases. Given the human genome size of about 3.2 billion base pairs, this would lead to thousands of mutations each time a cell divides if left uncorrected. To maintain genomic stability, cells use a multi-layered error-correction system. First, DNA polymerase performs immediate proofreading through its exonuclease activity. This is followed by post-replication mismatch repair (MMR) mechanisms. Together, these processes greatly enhance replication accuracy, reducing the final error rate to roughly 1 in 10⁹–10¹⁰, meaning fewer than one error typically occurs per genome duplication.

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? Ans: An average human protein (~1036 bp) can be coded by many synonymous codons because the genetic code is redundant.In practice, many of these codes fail because they can create secondary structures that block translation, contain sequences that trigger RNA cleavage, or use “rare” codons that the host cell cannot efficiently process.

Homework Questions from Dr. LeProust:

1. What’s the most commonly used method for oligo synthesis currently? Ans: The phosphoramidite method is currently the most widely used chemistry for oligonucleotide synthesis. It involves a four-step cyclic process: coupling, capping, oxidation, and deblocking—to add nucleotides one by one onto a solid support, such as Controlled Pore Glass (CPG) or silicon chips.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis? Ans: Oligonucleotide synthesis occurs by adding one nucleotide at a time, and each step has a small probability of error. As the length increases, these errors accumulate, reducing the yield of correct full-length oligos. In addition, longer oligos are more prone to incomplete reactions and strand loss during synthesis.

3. Why can’t you make a 2000bp gene via direct oligo synthesis? Ans: Directly synthesizing a 2000 bp gene is not feasible in practice. Errors accumulate with each step, resulting in low yield and incorrect sequences. Hence, long genes are constructed by assembling shorter, accurately synthesized oligos and then applying error-correction methods.

Homework Question from George Church: The question choosed by me

1. [Using Google & Prof. Church’s slide #4] What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”? Ans: Essential amino acids in animals (10):

Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, Valine, and Arginine . These amino acids cannot be synthesized by animals and must be obtained from the diet

Lysine contingency : Lysine contingency refers to the idea that animal life is fundamentally dependent on external sources of lysine because animals cannot synthesize it themselves. Since lysine is an essential amino acid and often scarce in plant-based foods, growth and survival become contingent on its availability. This makes lysine a key metabolic bottleneck shaping nutrition, agriculture, and evolutionary constraints.

My views The fact that lysine is an essential amino acid for all animals reinforces the idea of the lysine contingency—that animal life is inherently dependent on external biological systems (plants, microbes, or other animals) to supply lysine.We are all metabolically fasten to the external world, relying on a constant “supply chain” of plants and microbes to build our bodies.

Even when we think about survival in extreme environments, like a colony on Mars. Instead of trying to “fix” human genetics to make us self-sufficient which is ethically messy and biologically complex it makes far more sense to master the environment around us. By engineering hardy, high-yield, lysine-producing plants or yeast, we solve the survival puzzle without ever touching a human strand of DNA. It’s a strategy that’s not only safer and more flexible but one that respects our natural biology by simply ensuring the “bio-battery” we’ve always relied on never runs dry. At last the lysine contingency shows that human survival depends on food systems, not genetic independence. For extreme environments, engineering plants and microbes is a safer and smarter solution than changing human biology or animal biology.

Reference Jurassic Park Wiki. (n.d.). Lysine contingency. Fandom. Retrieved from https://jurassicpark.fandom.com/wiki/Lysine_contingency

Week 3 HW: lab automation

PAPER - Semiautomated Production of Cell-Free Biosensors

Journal: ACS Synthetic Biology (2025)

PMID: 40073441

Biosensors are biological systems that detect specific chemicals for example, if a substance is present, they might change color or glow. These can be used for:

1. Environmental detection (e.g., fluoride in water)
2. Health diagnostics
3. Rapid point-of-need testing

But traditionally, making lots of biosensor reactions by hand is slow and inconsistent. Different people might mix things slightly differently, which leads to variability in performance.

Instead of assembling all the biosensor reactions manually, the researchers used a robotic liquid-handling platform (like Opentrons OT-2) to semi-automate the process:

1. They wrote a protocol so the robot could prepare many reactions systematically
2. They tested this by building a full 384-well plate of biosensors that detect fluoride
3. They compared how well these robot-assembled reactions worked compared with manually assembled one

The robot-assembled biosensors worked as expected

The perks of the automated robot:OT-2

1. Using robots makes it possible to produce many biosensors quickly and reliably
2. This reduces human error when preparing them
3. It helps scale up manufacturing or testing so the sensors can be widely deployed

Idea 1: Carbon Capturing Microbial Genomics

What I would automate

Reasons for automation: Screen many strains simultaneously Maintain equal CO₂ exposure conditions Reduce pipetting variation Generate reproducible comparative data

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project