Week 1 HW: Principles and Practices

1. A biological engineering application or tool I want to develop and why:
I want to develop an engineered consortium of microorganisms for pilot-scale biomanufacturing on Mars. The microbes will be engineered for self-sufficent surival subject to the multitude of constraints of the red planet. This insitu resource utilization (ISRU) will be a key step towards the goal of the eventual colonization of Mars, by reducing the import from Earth. The current methods of ISRU, although in their rudimentary stages, rely on high energy chemical conversion process. My application aims at providing an alternative to this, and pave way for sustainable biomanufacturing away from the Earth.

2. Governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm):

Goal 1- Prevention of forward contamination: Great care must be ensured in making sure that only the right microorganisms will colonize the desired niche. Since this may very well fit into the definition of ‘forward contamination,’ a thorough conformation of the non-existence of native Martian microbes shall guide the policy decision. International collaboration is going to be of prominence, because this goal will be of no consequence if even one of the space-capable nation refuses to abide by this.
Goal 2- Address dual use concerns: It is inevitable that any microbe that has been engineered to tolerate Martian conditions would have multiple survival mechanisms that grant it an upper hand over its Earthen coutnerparts.Therefore, any type of microorganism that may even remotely prove to be pathogenic to humans must be avoided at all costs.
Goal 3- Level playing fields: Monopolies and oligopolies should be prevented to the largest possible extent, especially in the early days of the settlement plan. If such imbalanced playing fields get established, it will stiffle innovations for generations to come by restricting know-hows and resources.

3. Next, describe at least three different potential governance “actions” by considering the four aspects below:
3.1 Technical Goverenance: The policy must ensure that whoever wants to set up biomanufacturing on Mars has suitably demonstrated the presence of kill switches (auxotrophic, toxin-anti-toxin etc.) to prevent accidental release into the environment. Completely orthogonal biological systems may be used in place of kill switches, but given today’s biotechnology, the former is more likely than the latter.
Purpose: To prevent forward contamination.
Design: Genetic circuits can be embedded with toxic-anti-toxic systems like CcdB-CcdA, MazF-MazE, and hok-soc etc. Strains auxotrophic for Glucosamine-6-phosphate Synthase ((\Delta glmS)) can be used as auxotrophic chassis organisms.
Assumptions: The assumptions here would be that the strain will not bypass these kill-switches by any means, and also these kill-switches will not interefere with the organisms’ ability to synthesize the product of interest.
Risks of Failure & Success: Failure to meet these parameters may lead to forward contamination, preventing the study of ‘pristine’ Martian grounds. However, the success in this context would not be permanent and require repeated peroidic demonstrations. There is also the possibility of false trigerring of kill-switch, leading to a wasted batch of products.

3.2 Regulatory Governance: A system to inventory and track all the organisms, genetic components, and manufacturing methods becomes important. This will provide a starting point to study the evolution of the microorganisms that might arise in the future. A high degree of match to the inventoried parts can help rule out any fasle-positivity regarding native Martian microbe claims.
Purpose: To track any suspicious new microbes in the vicinity and beyond.
Design: A robust inventory software, and the adherence of the players to documentation.
Assumptions: All the players will abide by the regulations, and will not send any undocumented organisms to gain a competitive edge.
Risks of Failure & Success: Failure would mean lots of undocumented and potentially unsafe microorganisms on Mars. It would also prevent any means of studying weather Mars had evolved any microbes independent of the Earth. On the other hand, a policy that is too transparent will hinder intellectual property safeguard.

3.3 Economic Incentive Governance: For this, I envision a system of “Biosecurity Bonds.” Any entity that wants to carry out biotechnology research on Mars would need to furnish a bond of a certain amount (probably in millions of dollars). If, after a period of time, no contamination can be established, the amount is refuned. If any contamination is found, the bonded amount can be utilized to ameliorate the spread.
Purpose: To incentivize players to adhere to high standards of biosecurity.
Design: A techno-legal framework in the form of an international treaty or agreement, among all the spacce-faring nations and also similar incentives at national level.
Assumptions: None of the players will take this bond as an opportunity to “pay to pollute” and think that forfeiting the bond amount is cheaper than adhereing to the standards of biosecurity.
Risks of Failure & Success: Failure can lead to an incentiveless, haphazard business models, that would aim towards establishing monopolies for profit. If this aspect is successfully governed, then there is still the risk of wealthy corporations outcompeting the not-so-wealthy ones.

4. Score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against the rubric of policy goals.

5. 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.
Based on these parameters, I would priortize option 1, i.e, Technical Governance, and option 2, Economic Incentive Governance. Both of these would go hand in hand to cover the technical and the financial safeguards agianst the forward contamination, establishment of monopolies, and an imbalanced playing fields. However, the main trade off in not prioritizing option 2, i.e., regulatory governing would be the existence of loopholes to evade accountability. The uncertainty of non-adhering players will always remain as a looming threat in establishing a stable policy towards extraterrestrial resource utilization.

Homework questions from Dr. LeProust:

1. What’s the most commonly used method for oligo synthesis currently?
   Solid phase phosphoaramidite method is the most widely used method to synthesize oligonucleotides. Nucleoside phosphoramidites are used as the precursor molecules. It proceeds through 4 steps:

1. Detritylation: dimethoxytrityl group is removed from the 5’ end of the last nucleotide attached to the support using triacetic acid, activating the -OH group.
2. Coupling: Phosphoaramidite monomers are added along with an activator (usualy tetrazole), that protanates the phosphoaramidite. Now, the 5’ hydroxyl end of the growing chain can form a phosphite triester linkage at the 3’ phosphorous.
3. Oxidation: The unstable phosphite triester linkage is oxidized using iodine solution form a stable phosphate triester bond.
4. Capping: Once the required number of nucleotides have been synthesised using the above 3 steps, the unreacted 5’ ends are capped using an acetylation mix of acetic anhydride and N-methylimidazole. This is done to prevent wrong reactions in further cycles.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis?
   If oligos are synthesized using phosphoaramidite method, the yield follows the equation Y = $C^{n}$; where Y is the yield %, C is the coupling efficiency, and n is the number of couplings. A diakósiamer (200mer) will have 199 couplings. This implies, even with a success rate of 99%, the yield would be $0.99^{199}$, which is around 13.5%. The rest of the sequences would be truncated at random lenghts less than 200 bps.

3. Why can’t you make a 2000bp gene via direct oligo synthesis?
   Using the same equation as above, we get the yield of only 1.88 * $10^{-7}$ percent, which is as good probability as nil in order to synthesize a 2 kb gene.

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?
   DNA polymerarases are accurate with upto $10^{−6}$ mutations/bp. Since human genome is around 3.2 * $10^{9}$ bp long, it would imply 3200 mutations per generation. Biology deals with this descrepancy by having a multitude of proofreading mechanisms like 3’-5’exonuclease activity in the polymerase that cleaves incorrect nucleotides, mismatch repair post replication where a protein complexes can recognize the template strand and the newly-synthesized strand due to the presence of nicks in the latter, and cleave the ‘wrong’ base pairs. Then, DNA ligase joins the correct nulceotides.

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?
   Considering an average protein to be 375 amino acids long, and each amino acid requiring 3 codons, there can be $3^{375}$ DNA sequences for an average protein. But in reality, the number of translatable codon is limited by the properties of mRNA and the availability of tRNA. Certain DNA sequenes transcript into an mRNA that will have haripin loop, tendency to form dsRNA and other difficult-to-translate structures. And also, the translational machinery possesses a limited number of tRNA, which is the limiting factor for the number of amino acids that can be translated, and thus protein that can be synthesized.

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”?
   The nutritionally essential amino acids in all animals are: Cystine, Leucine, Lysine, Methionine, Histidine, Phenylalanin, Tyrosine, Threonine, Tryptophan, and Valine. Since lysine is already an essential amino acid, meaning, it cannot be synthesized by reptiles on their own, lysine contingency does not make any sense. It can be easily obtained by feeding on the plant matter, and the orgnaisms that feed on the plant matter, readily. The scientists of the Jurassic Park were better off in making the dinosaurs auxtorphic to certain enzymes that are very much necessary for metabolic reactions.

References:

https://www.khanacademy.org/science/biology/dna-as-the-genetic-material/dna-replication/a/dna-proofreading-and-repair
https://bionumbers.hms.harvard.edu/bionumber.aspx?s=n&v=4&id=106445
https://pmc.ncbi.nlm.nih.gov/articles/PMC4150459/
https://www.bocsci.com/resources/principles-of-phosphoramidite-reactions-in-dna-assembly.html

Write up of webpage personalization

• Feb 8, 2026: Added a profile photo
• Feb 8, 2026: Replaced template bio with my own background.
• Feb 8, 2026: Added my contacct details.
• Feb 9, 2026: Initial draft uploaded (Homework 1)
• Feb 10, 2026: Added content for the Professors’ questions
• Feb 10, 2026: Added math: true tag, tested equations.
• Feb 10, 2026: Edited all the homeworks for clarity
• Feb 10, 2026: Added References section with four sources.

Week 2 HW: DNA Read, Write, and Edit

Part 1: Benchling & In-silico Gel Art

1.1 Restriction Digestion Simulation in Benchling:

1.2 DNA Gel Art Using Automation Art:

Part 2: Laboratory Work on Gel Electrophoresis

Skipped due to lack of access to lab.

Part 3: DNA Design Challenge

Lorem Ipsum

3.1. Choose your protein.

Database Used: UniPort

tr|O33823|O33823_ACIFR Cytochrome c OS=Acidithiobacillus ferrooxidans OX=920 GN=cyc2 PE=4 SV=1 MVSSSVGFKKKRLIVALAAVGGMALSSSAWALPSFARQTGWSCAACHTSYPQLTPMGRMFKLLGFTTTNLQRQQKLQAKFGNSVGLLISRVSQFSIFLQASATNVGGGQAVFGSGNSNANASPNNNVQFPQQVSLFYAGEITPHIGSFLHITYSGGGSGTGGGGFSFDDSSIVWAHPWKLGTNNLLVTGVDVNNTPTAMDLWNTTPDWQAPFFSSDYSSWGHVPQPFIESSAGAGYPLAGVGVYGADIFGPNRANWLYADADVYTNGQGTQVNPVGGFTAAGPQGRLSGGAPYVRLAYQHDWGDWNWEVGTFGMWSSVYDNTLNNPLNNISKAGGPIDTFDDYDLDTQLQWLDTNDNNNVTIRAAWVNEQQQFGAGNIISSNSSGNLNFFNVNATYWYHDHYGIQGGYRNVWGSANPGLYTTTYTNSGSPDTSNEWIEASYLPWWNTRFSLRYVVYNKFNGVGSASSNNLGYGASAYNTLELLAWISY

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

Tool used: NCBI

AJ006456.2 Acidithiobacillus ferrooxidans cyc1, cyc2, coxA, coxB, coxC, coxD and rus genes and open reading frame TTGGCATGTCGATTTTTGGACCTCTAGTGATCACGGCCTATAATTAAACGGCATGGTTAACATGATAAAATAACGTTAGCACATAATTCTTTTCTTATGTTCGTTATTTACTTTATTGCATTTTACTGGATCGATATTCTGGCAACTATGCGCAAAATATTGATTATAAAAGCATTATAGTTATGACCATCGAGGCGATCGCGAGATGCATGGATGAGGTAGCCATGCATTTTAATGAGCGCATAAAAAGATGTTGCAAAGCATCGCGGTTTGTATTAAATAGAACGTGTGGGTATTGTTAACAACGCAACAACATTGGTTAAAGGTCGAGGCTAATTGGCATCGCGTTGTTGTGGTTTGGTGTTACCAGCCTGGCAGGAAGACCGGGCGCATGAGCGTATTTTGTTTATCTAATATGCCTGAAAGCGCATACCGCTATGGAGGGGGTTATGGTGTCATCGTCCGTTGGTTTTAAAAAGAAAAGGTTGATCGTAGCATTAGCAGCAGTTGGTGGAATGGCGTTATCTTCCAGTGCCTGGGCACTGCCATCCTTTGCGCGCCAGACCGGTTGGTCGTGCGCCGCCTGTCACACATCCTACCCGCAGTTGACGCCCATGGGCAGAATGTTCAAATTGCTCGGGTTCACGACCACAAACCTGCAACGGCAGCAGAAGCTCCAAGCCAAGTTCGGGAACAGCGTCGGTCTGCTCATATCCCGCGTGTCACAATTTTCTATCTTCCTGCAGGCCTCGGCGACCAATGTTGGTGGCGGGCAGGCGGTGTTTGGTTCTGGTAACTCTAATGCGAATGCTTCTCCCAACAATAATGTTCAGTTTCCACAACAGGTGAGCTTGTTCTATGCCGGTGAAATCACTCCGCATATCGGCTCGTTTCTGCATATCACCTACTCGGGCGGCGGCAGTGGTACCGGCGGCGGAGGATTTAGTTTTGACGACTCCAGCATTGTCTGGGCCCATCCATGGAAGTTGGGCACCAACAATCTTTTGGTTACGGGCGTAGACGTCAACAATACCCCGACTGCTATGGACTTGTGGAATACCACACCTGATTGGCAGGCACCATTTTTCTCCTCGGATTATTCGTCTTGGGGCCACGTACCTCAGCCATTCATTGAAAGTTCAGCGGGCGCGGGTTACCCATTAGCGGGTGTTGGTGTCTATGGGGCGGATATTTTTGGGCCAAACCGGGCAAACTGGCTGTACGCAGACGCCGATGTCTATACCAACGGTCAAGGAACCCAAGTCAACCCGGTTGGCGGTTTTACTGCAGCTGGCCCCCAGGGCAGGCTTTCAGGGGGCGCTCCTTATGTTCGTCTTGCCTATCAGCACGATTGGGGTGACTGGAACTGGGAGGTCGGCACCTTTGGCATGTGGTCCAGCGTGTACGATAACACCCTAAATAATCCTCTCAATAATATCAGCAAAGCAGGCGGCCCCATTGATACCTTCGATGATTATGATTTAGATACTCAGCTCCAATGGCTTGATACCAACGACAACAATAACGTGACGATCCGTGCCGCATGGGTAAACGAGCAGCAGCAATTTGGAGCGGGGAATATCATATCTTCGAACTCCTCCGGTAACTTAAATTTCTTCAATGTTAACGCCACCTACTGGTATCATGACCACTACGGCATTCAGGGCGGATACCGGAATGTGTGGGGGTCCGCTAACCCCGGTCTCTACACTACCACATACACTAATAGTGGTTCTCCAGATACCAGCAATGAATGGATAGAGGCTTCCTATCTGCCGTGGTGGAATACCCGCTTCTCCTTGCGATATGTCGTATACAACAAGTTCAATGGCGTTGGTTCGGCGTCGTCCAACAACCTTGGATATGGGGCGTCTGCGTATAACACCCTTGAACTGCTGGCCTGGATATCATACTAGGAGCCGATGCCATGACGACATACTTAAGCCAAGACCGGTTGCGCAATAAAGAGAACGACACGATGACCTATCAACATAGCAAGATGTATCAGTCGAGAACCTTCCTTCTGTTCAGCGCACTCTTGCTGGTGGCCGGGCAGGCGAGTGCTGCAGTCGGCAGCGCCGACGCGCCGGCACCATACCGCGTCTCCAGTGATTGCATGGTATGCCACGGGATGACGGGCCGTGACACGCTCTATCCGATCGTCCCCCGCCTGGCCGGACAGCATAAGAGTTATATGGAAGCGCAGTTGAAAGCGTATAAGGATCACTCGCGTGCGGATCAGAATGGCGAGATCTACATGTGGCCCGTGGCGCAAGCGCTGGACAGTGCGAAAATCACGGCGCTGGCAGATTACTTCAACGCCCAGAAGCCGCCGATGCAAAGCAGCGGCATCAAGCATGCCGGTGCGAAAGAAGGAAAGGCCATATTCAACCAAGGGGTTACCAACGAACAAATCCCTGCCTGTATGGAATGCCACGGATCGGATGGCCAAGGGGCGGGCCCGTTCCCCCGGCTGGCGGGCCAGCGTTACGGCTACATCATTCAGCAGTTGACCTACTTCCACAACGGCACACGGGTAAATACCCTGATGAACCAGATTGCGAAGAATATCACCGTGGCGCAGATGAAGGATGTGGCGGCTTATCTTTCATCGCTGTAAGCGTTGTAATTGGTCAATAGAAGTTTTCCTGGCAGGCTGAAGTTTATAAAAATGGGTCTGCCAGGCATTTGCACCGTCAGGTTTATGTGCTTCTCAAAGGAGGTAGAGGTATGGCAGCAAAAAAAGGTATGACTACGGTGCTTGTATCCGCCGTGATATGCGCGGGGGTAATTATAGGTGCCCTGGAGTGGGAAAAAGCGGTAGCCCTGCCCAATCCTTCCGGGCAGGTCATTAATGGGGTACATCATTATACGATCGATGAGTTCAACTATTATTATAAACCGGATCGCATGACCTGGCATGTCGGGGAAAAAGTGGAGTTGACGATTGATAACCGATCGCAATCAGCGCCCCCGATTGCGCATCAGTTCTCCATCGGCAGAACGCTGGTATCCCGGGACAATGGCTTTCCAAAATCACAGGCTATCGCCGTGGGATGGAAAGATAACTTCTTTGATGGTGTGCCGATTACCAGCGGGGGACAGACAGGGCCAGTACCGGCGTTTTCCGTCAGCCTCAACGGTGGACAAAAGTACACCTTCAGTTTTGTGGTGCCCAATAAGCCCGGAAAATGGGAATATGGGTGTTTTCTGCAGACGGGTCAACACTTCATGAATGGGATGCATGGTATTCTTGACATACTACCTGCTCAGGGAAGCTAATTTAGGGAGGGCATATGAACGCAGCAAAAGAAAACTTATGGAAAGCTTTCCGCGGCTTGGTGGTGGTCTGGATTATTGGCCTGGCGATTTTCGAAACGCTGATGGCCTGGGGTATCGGTAACTGGCCAATTTTGGGGAGTATTCAGGCGCATATTACCGCAGATGCCACCACATACCTGTTGTGGCAGGCCGTATTCATCTATGTGCTGGTCGGCGGTGCGATTGTATATAGCGCATTTCGTTTCCGCGCATCATCCATGTCAGACACCGCGGCGCCGGCTTATCAAAAACGGACCTGGGCGCCTTTCGTGGTGACCTGGCTGGTTTTGGCCATAGGCATCAACCTGGCAAATACCATTTATCCGGGTATGGTGGGTCTGGAACAACTTTGGGGTATCCAGTTAGATACGAAGAACCCATTGGTGATCGATGTTACCGCGCAACAGTGGAAGTGGACGTTCTCTTATCCTAAGCAGGGCGTAACGGATGTGTCACAACTGGTGGTTCCCGAGGGCCGCACCATATACTTCGTTCTGCGGACAAAGGATGTCATGCACGATTTTTGGGTGCCTGCCTGGGGTGAGAAAAAAGATGTGATCCCCAATGAAGTGCGGCACTTGTTTATTACACCCACCATGTTGGGGACAACCGCTACAAACCCCATGCTGCGTGTACAGTGTTCCTTGATTTGTGGCAACGGACATCCGTTGATGCGCGCTCCGGTGAAAGTGGTAACGCCAGCGGACTTCAAGGCTTGGGTGGCAAACAATAGCTTCTAGTAAAGCCAACGGAAGGCTTGCCAGCACCCAACGTTAAATGTACTAAGGAGTAAGTAATGGCAACTAACGAAATTCAGGAAAATGCGTTGAACAATACGGGAGTGGACAAGACCCCATTTGCGGCTAGCATGCTGTTTCCGCTGTTCCGTGCGACGCTTTGGGGACTAACCGGCTATTTTGCTGCGGCATGGATCACTGCTTTATTGCTCCACACGGTAATCGTAAACCCTTTACCCGCGACAGTGGGTTATGTGGCCGGCTTGGTCTGCTGGCTGATGGGCAGCGGTGTATGGGAGGGATGGATACGACGCGCATTTGGAGGAAAAGAAGCTCCAACTTACACGGGTATCGAACGTTATTTTCGCTTTGGTCCCGATTCAAAATCCGCAGCCGTACGCTACGTAATCTTAAATATACTAACGTTCTGCTTTGCCGGCATGGCCGCCATGGCGATCCGCATTGAACTGTTGACGCCAGACTCCACCAGTTGGTGGCTGTCAGAAATCCAGTACAACCAAACGTTCGGTATTCATGGATTGATGATGTTGTTGGGTGTGGTGGCCTCTGCCATCGTCGGCGGTGTTGGCTACTATCTTATCCCGTTGATGCTTGGCACGAGAAATGTAGTATTCCCAAAACTTCTTGGCCTAAGTTGGTGGCTTTTGCCACCGGCGACCTTCGCTGTTTTTATGAGTCCTACGACCGGTGGGTTTCAGACGGGATGGTGGGGATATCCGCCGTTGGCGCAAAACAGTGGTAGCGGTATTGTGTGGTATGTCCTCGGTGCCGCCACCATTCTTGTTGCGTCGCTACTTGGAGCCATCAATATCGCCGGAACCATGGTGTACATGCGCGCCAAGGGCATGAGCCTGGGTCGCGTTCCGATTTTTGTGTGGGGTTTATTTGCGGCAGCCACCACTCTCGTCGTAGAGTCGCCAGCAACCTATACCGGCGCGCTCATGGACTTATCCGACATGATCGCCGGATCGCATTTTTATACCGGTCCCACCGGCCACCCGTTAGCGTATCTCGATCAGTTCTGGTTTTTGTTCCATCCAGAGGTCTACGTTTTCATTCTGCCCGCTTTTGCCATATGGCTGGAGATTCTTCCTGCCGCGGCCAAGCGGCCGTTGTTTGCTAGGGGTTGGGCCATCGCCGGACTGGTTGGCGTTTCCATGTCGGGTGCAATGTCGGGTGTCCATCACTACTTCACTGCGGTGAGTGACGCGCGTATGCCCATATTCATGACCATAACGGAAACTGTATCCATTCCGACAGGGTTCATTTATTTGTCCGCCATCGGAACGATATGGGGTGGTCGTTTAAGAATTAATGCTGCGGTATTGCTCGTACTGATGGCGATGATGAACTTCCTGATCGGTGGGCTGACGGGCATATTCAATGCCGACGTTCCCGCCGACCTTCAGCTGCACAACACCTACTGGGTTATTGCGCATTTCCATATACGATGCTTTGGTGGAGTGATCTTTACCTGGATTGCCGCGCTATACTGGTGGTTTCCCAAGGTTACTGGACGGAAGATCAATGAATTTTGGGGAAAGTTTCACGCATGGTGGTCCTTCGTATTCTTCAATTGTACGTTCTTTCCCATGTTTATAGCTGGACTAGATGGAATGAACAGGAGAATTGCGATATATCTTCCTTACCTGCATGACATCAACCTGTTTATGTCTATTTCATCCTTTTTCTTGGGCGCAGGGTTTCTCATTCCGCTGGCCAATCTTTTATACAGTTGGCGCTATGGGCCAAAGGCCGAAGCTAACCCTTGGGGCAGCAACGGCCTGGAATGGCAAATAAAATCGCCAACACCGTATGTGCCATATCCAGCAGGAACGGAGCCAGAGGTTGTGGGCCCGAACGATAACTACGCGGCGGAAGCAAAAGACCCCTTTATTTGGGTGTCTACGCCCAGCAAGTAAATTAGAAGGAGTTGAACCATGACAGACAACAGTTATGCCAAGCTAATGGATCCGGCCTCGGAGCGTGCAAAAAGGGGTGCGTTCTTTTTCCTGATGCTTTTTGCAGCCATCATTTTTGCGATGTGGGACCTCGCGCGTTTTCTGTGGGGGCACTCGGTGCCCGCTACATTGAGCATGGGCGTGGGTGTTGCGCTGACTGTTCTGATGCTCGTCAGCCTGGTGCCGGTGATGACGGCCCGCAAAAAACTGGATCAGGGCGATGATGCCGGTATCGTGAGCAGTCTGGCAACCCTGATGGTGGTCTCGTTGGTGATGGCGGGTGGAATCGTCTACAACTGGACTACCTTAACCATCGGTAGTGGTTATGGCGGGATTTATGACATCACCAGCTTGTGGTTTCTGGTACATTTCGTGGCGGCCATCCTGGCGCTGCTGGCGAGTATCATGAAAATCACTCGCACTCCAGAGCGCGCGAAACGCGAGCGATGGGTGTCGTATAACGTGTTAACCTTCTGGGGCGGTGTGATTGTTCTATGGGTTGCATTTTTTATTGTTTTCTATATTGCGTAATGCAGTTTAGAAGATTCTCTAATGGAGTGAGGGTTAGATAATGGATATGTCACATTTATCGTTCGTTATCCCGTCTGGAGCTGATGATCCGACGTTTTTCTGGCTGACGGGGTACATTGGGTTTCCTGTGGTGTTTCTGAGTGCATACTTTTGGTGGGTATTAAAGGAGGCAAGCAAGGAAGATCGGCTGCGTATTCTAAAAAAGGGAGAAGACGGCGCATCTGGAAACGCATGATGTTCCACGGATGGTCGTGCGAGTACCGGGCGGCCATCCGGAGTTGTTTTGCGTTTTACTGTTGCGACGTCGTTATCCATGCTTCAAAGGAGGTAAATCATGAACAAGGAAGGCTGTTTAATTTCTCACGATGATCGCGATGATGGCGCATGGGATGGAAACATCGTGTTGATCATAGGATTATTGTGGGCTATTATTGCTCTGGGTGGCTATTATGTTACCCTTAGAGTGCTGTTTTGAGACAATTCCCCGGCTGGATAGGGCGATGAATACCATGTAGTAGCATATTAAAATGCCAGAGGGCCCGGTGATGGTTTTGTAGGGCGGCTGGTTCTACTCAGGTTAAACGTTAAGGAGAAGGGATAACTTATGTATACACAGAACACGATGAAAAAGAACTGGTATGTGACTGTTGGTGCGGCTGCGGCTCTGGCGGCAACGGTCGGCATGGGTACCGCGATGGCCGGCACGCTGGATTCCACATGGAAAGAGGCGACGCTTCCCCAAGTTAAGGCCATGCTGGAGAAAGATACCGGGAAAGTCAGTGGCGATACAGTTACCTACAGCGGCAAGACTGTACATGTGGTCGCGGCGGCCGTGCTCCCGGGATTTCCGTTCCCGAGCTTTGAAGTTCATGACAAAAAGAACCCGACCTTGGAGATTCCCGCAGGGGCAACCGTAGACGTGACCTTCATTAACACCAACAAGGGATTTGGTCATAGTTTTGACATCACTAAAAAAGGACCGCCTTATGCGGTTATGCCGGTGATTGACCCCATTGTCGCAGGAACTGGATTTAGCCCGGTCCCAAAAGACGGCAAGTTCGGATATACGGATTTCACCTGGCATCCGACGGCGGGTACTTACTACTACGTATGTCAGATACCGGGGCATGCCGCCACCGGTATGTTTGGTAAAATCATTGTCAAGTAAGTCCTGGATGGTTGTTGTCTGGGCAGCTGTGCTTTGCTAGTGTAGGTCCTGGTGGCCAGGGCAAATGGTTATCTTGCCCTGGCCATTGGTATTTATTATAAAATACGAATTTCATGTATTGCGTTATGCTTTGTATGATGTTATGAGTATGTTTGCATGCAACATATGATGATTGATCTAGTTTATTAAGCTATGGACCACGAAAACACGCTGCCTCGGTACATATATTAATTCATTCAGATAAAGTCCCAAACTCAGATATCCTGACG

3.3. Codon optimization (for E. coli)

Tool used: Vectorbuilder.com

AACTACACCCCGACCCCGGAAGATTGGCATGTGGATTTTTGGACCAGCAGCGATCACGGCCTGTAACTGAACGGCATGGTTAACATGATTAAATAACGCTAACACATTATCCTGTTCCTGTGCAGCCTGTTTACCCTGCTGCACTTTACCGGCAGTATCTTTTGGCAGCTGTGCGCCAAATATTAACTGTAAAAACATTACAGCTATGATCACCGCGGCGATCGCGAGATGCACGGCTAAGGCAGCCATGCGTTTTAATAAGCGCACAAAAAAATGCTGCAGAGCATTGCCGTGTGTATTAAATAAAATGTTTGGGTTCTGCTGACCACCCAACAACATTGGCTGAAAGTGGAAGCGAATTGGCACCGCGTGGTGGTCGTGTGGTGTTACCAGCCGGGCCGCAAAACCGGCCGTATGAGCGTTTTTTGCCTGAGCAACATGCCGGAGAGCGCATATCGCTATGGCGGCGGCTACGGTGTGATTGTGCGCTGGTTTTAAAAGGAAAAAGTGGACCGTAGCATTAGCAGCAGCTGGTGGAACGGCGTGATTTTTCAGTGCCTGGGCACCGCCATTCTGTGCGCGCCGGATCGCCTGGTGGTGCGCCGCCTGAGCCATATTCTGCCGGCAGTGGATGCGCATGGCCAGAACGTGCAGATTGCGCGCGTCCATGATCACAAACCGGCGACCGCGGCGGAAGCGCCGTCTCAGGTGCGTGAACAGCGCCGTTCCGCGCACATTCCGCGTGTAACCATTTTTTATCTGCCGGCCGGCCTGGGTGACCAGTGCTGGTGGCGCGCCGGCGGCGTGTGGTTTTGGTAACTGTAATGCGAATGCTTTAGTCAGCAGTGATGTAGCGTGAGCACCACCGGCGAACTGGTGCTGTGCCGTTAAAACCACAGCGCCTACCGCCTGGTGAGCGCGTATCATCTGCTGGGCCGTCGTCAATGGTATCGCCGTCGTCGCATTTAATTTTAACGCCTGCAGCATTGCCTGGGTCCGAGCATGGAAGTGGGCCATCAGCAGAGCTTTGGTTATGGCCGCCGTCGCCAACAGTATCCGGATTGCTACGGTCTGGTGGAATATCATACCTAACTGGCCGGCACCATTTTTCTGCTGGGCCTGTTCGTTCTGGGCCCGCGCACCAGCGCGATCCACTAAAAATTTAGCGGCCGCGGCCTGCCGATTAGCGGTTGCTGGTGCCTGTGGGGCGGTTATTTCTGGGCCAAACCGGGCAAACTGGCGGTGCGCCGCCGCCGCTGTCTGTATCAACGTTCGCGCAACCCGAGCCAGCCGGGCTGGCGCTTTTATTGCAGCTGGCCGCCGGGCCAGGCGTTTCGCGGCCGTAGCCTGTGTAGCAGCTGCCTGTCAGCGCGCCTGGGCTAACTGGAACTGGGCGGCCGCCATCTGTGGCACGTGGTTCAGCGCGTGCGCTAACATCCGAAATAAAGCAGCCAGTAATACCAGCAGAGCCGTCGTCCGCATTAATATCTGCGCTAACTGTAATTCCGCTATAGCGCCCCTATGGCCTAATACCAGCGTCAGCAGTAACGTGATGATCCGTGCCGCATGGGCAAACGCGCCGCGGCGATTTGGAGCGGCGAATATCACATTTTTGAACTGCTGCGTTAACTGAAATTCCTGCAGTGTTAACGCCATCTGCTGGTGAGCTAACCGCTGCGTCACAGCGGTCGCATTCCGGAATGCGTGGGCGTGCGTTAACCGCGTAGCCTGCACTACCATATTCATTAATAATGGTTTAGCCGCTATCAGCAGTAAATGGATCGCGGCTTTCTGAGCGCCGTGGTTGAATACCCGCTGCTGCTGGCCATTTGCCGCATTCAGCAGGTTCAGTGGCGCTGGTTTGGTGTTGTTCAGCAACCGTGGATTTGGGGCGTGTGTGTATAACATCCGTAAACCGCGGGCCTGGATATCATTCTTGGCGCGGATGCCATGACGACCTACCTGTCGCAGGATCGCCTGCGTAACAAAGAAAATGATACCATGACCTATCAGCATAGCAAAATGTATCAGAGCCGCACCTTTCTGCTGTTTAGCGCGCTGCTGCTGGTGGCGGGCCAGGCGAGCGCGGCCGTTGGTTCGGCAGATGCGCCGGCGCCGTACCGCGTCAGCAGTGATTGCATGGTGTGCCACGGCATGACCGGCCGTGATACGCTGTATCCTATTGTGCCGCGCCTGGCAGGCCAGCATAAAAGCTATATGGAAGCGCAGCTGAAAGCCTACAAAGATCACAGCCGCGCCGATCAGAACGGCGAAATTTATATGTGGCCGGTTGCGCAGGCCCTGGATAGCGCCAAAATCACCGCCCTGGCGGATTATTTCAATGCGCAGAAACCGCCGATGCAGAGCAGCGGTATTAAACATGCGGGCGCCAAAGAAGGCAAAGCCATTTTCAACCAGGGCGTGACCAATGAACAGATCCCGGCGTGCATGGAATGTCATGGTTCGGATGGCCAGGGTGCGGGTCCGTTTCCGCGCCTGGCCGGTCAGCGCTACGGTTACATTATTCAGCAGCTGACCTATTTTCATAACGGCACGCGCGTGAATACCCTGATGAACCAAATCGCGAAAAACATTACCGTCGCACAGATGAAGGATGTTGCGGCCTATCTGAGCAGCCTGTAAGCGCTGTAACTGGTGAACCGCAGCTTTCCGGGCCGCCTGAAATTCATTAAAATGGGCCTGCCGGGCATTTGCACGGTGCGCTTTATGTGCTTCTCGAAAGAAGTGGAAGTGTGGCAGCAGAAAAAAGTGTAACTGCGTTGCCTGTACCCGCCGTGATACGCCCGCGGCTAACTGTAAGTTCCGTGGAGCGGCAAAAAACGTTAACCGTGCCCGATTCTCCCGGGTCGTAGTCTGATGGGCTATATTATCATTCGCAGCATGAGCAGCACCATTATCATTAACCGTATTGCGTAACCCGGCATGAGCGGCAAAAAATGGAGCTAACGCCTGATCACCGATCGCAATCAGCGTCCGCGCCTGCGCATCAGCTCGCCGAGCGCGGAACGTTGGTATCCGGGCACTATGGCCTTTCAAAATCATCGCCTGTCGCCCTGGGATGGCAAAATTACCAGCCTGATGGTGTGCCGCCTGCCGGCCGGCGATCGTCAGGGCCAGTATCGCCGCTTTCCGAGCGCGTCGACCGTGGATAAAAGCACCCCTAGCGTGCTGTGGTGTCCGATTTCGCCGGAAAATGGCAACATGGGCGTGTTTTGTCGTCGCGTGAACACCAGTTAAATGGGCTGCATGGTGTTTCTGACTTACTACCTGCTGCGTGAAGCGAACCTGGGCCGCGCGTATGAACGTAGTAAACGTAAACTGATGGAAAGCTTTCCGCGTCTGGGCGGCGGCCTGGATTATTGGCCGGGCGATTTTCGTAATGCTGATGGCCTGGGCTATCGTTGACTGGCAAATTTTGGCGAATATAGCGGCGCGTACTATCGTCGTTGCCATCATATTCCGGTTGTGGCCGGCCGCATTCATCTGTGTGCCGGCCGTCGCTGCGATTGCATTTAACGCATCAGCTTCCCGCGCATTATTCATGTGCGCCATCGCGGCGCGGGTCTGAGCAAAACCGACCTGGGCGCGTTTCGCGGCGATCTGGCCGGCTTCGGCCATCGCCATCAGCCGGGCAAATACCATCTGAGCGGTTATGGCGGCAGCGGCACCACCCTGGGTTACCCGGTACGTTACGAAGAACCGATTGGTGATCGCTGCTATCGCGCGACCGTCGAAGTGGATGTGCTGCTGAGCTAAGCGGGCCGTAATGGCTGCGTGACCACTGGCGGCTCCCGTGGCCCGCACCATATCCTGCGAAGCGCGGATAAAGGCTGCCATGCGCGCTTTCTGGGCGCGTGTCTGGGCTAAGAAAAACGCTGTGATCCGCAGTAAAGCGCAGCGCTGGTTTATTACACCCACCATGTGGGTGATAACCGCTATAAACCGCATGCGGCCTGCACCGTGTTTCTGGATCTGTGGCAGCGCACCAGTGTGGATGCGCGCAGCGGCGAAAGCGGTAACGCGAGCGGCCTGCAGGGCCTGGGCGGCAAGCAGTAACTGCTGGTGAAACCGACCGAAGGCCTGCCGGCCCCGAACGTGAAATGCACCAAAGAATAAGTCATGGCGACGAACGAGATTCAGGAAAACGCCCTGAATAATACCGGTGTGGATAAAACCCCGTTCGCGGCGAGCATGCTGTTCCCGCTGTTCCGTGCGACCCTGTGGGGCCTGACCGGCTACTTCGCGGCGGCGTGGATTACCGCGCTGCTGCTGCATACCGTGATTGTGAATCCGCTGCCGGCGACCGTGGGTTATGTGGCGGGCCTGGTGTGCTGGCTGATGGGTAGCGGCGTGTGGGAAGGCTGGATTCGCCGCGCCTTTGGCGGCAAAGAAGCGCCGACCTACACCGGTATTGAACGTTACTTTCGCTTTGGCCCGGATAGCAAAAGCGCCGCCGTTCGCTACGTGATTCTGAATATCCTGACCTTTTGCTTTGCCGGCATGGCGGCGATGGCGATTCGTATTGAACTGCTGACGCCGGATAGCACCAGCTGGTGGCTGAGCGAGATCCAGTATAACCAGACCTTCGGCATTCATGGCCTGATGATGCTTCTGGGCGTTGTAGCGAGCGCCATTGTGGGCGGCGTGGGCTATTATCTGATACCGCTGATGCTGGGCACCCGTAATGTGGTCTTTCCGAAACTGCTGGGCCTGAGCTGGTGGCTGCTGCCGCCGGCAACCTTCGCGGTTTTTATGAGCCCGACCACCGGCGGCTTTCAAACTGGCTGGTGGGGCTATCCGCCGCTGGCGCAGAACAGCGGTAGCGGCATTGTGTGGTATGTACTGGGCGCGGCCACCATTCTGGTTGCGAGCCTGCTGGGCGCCATCAACATTGCCGGCACCATGGTGTACATGCGCGCGAAAGGCATGAGCCTGGGCCGCGTGCCGATTTTTGTGTGGGGTCTGTTTGCGGCAGCGACCACCCTGGTGGTTGAAAGCCCGGCCACCTATACCGGCGCGCTGATGGATCTGAGCGATATGATTGCGGGCAGCCATTTCTACACCGGCCCGACCGGTCACCCGCTGGCCTATCTGGATCAGTTCTGGTTTCTGTTTCACCCGGAAGTGTACGTGTTTATTCTGCCGGCCTTCGCGATTTGGCTGGAAATTCTGCCGGCCGCGGCCAAACGTCCGCTGTTTGCCCGCGGCTGGGCGATTGCCGGCCTGGTTGGTGTGAGCATGAGCGGCGCGATGAGCGGTGTGCATCACTACTTTACCGCGGTCAGCGATGCCCGCATGCCGATTTTTATGACCATCACCGAAACCGTGAGCATCCCGACCGGCTTTATTTACCTTAGCGCCATTGGCACCATCTGGGGCGGCCGCCTGCGCATTAACGCCGCGGTGCTGCTGGTGCTGATGGCGATGATGAACTTCCTGATCGGAGGCCTGACCGGCATTTTTAACGCGGACGTGCCGGCGGATCTGCAGCTGCATAATACCTACTGGGTGATTGCGCATTTTCATATCCGCTGTTTTGGCGGCGTGATCTTCACGTGGATCGCCGCCCTGTACTGGTGGTTTCCAAAAGTGACCGGTCGCAAAATCAATGAATTTTGGGGCAAATTTCATGCGTGGTGGAGCTTTGTTTTTTTTAATTGCACCTTCTTCCCGATGTTTATTGCCGGCCTGGATGGCATGAACCGCCGCATTGCGATTTACTTGCCGTACCTGCATGATATTAACCTGTTTATGAGCATTAGCTCTTTTTTCCTGGGCGCGGGCTTTCTGATTCCGCTGGCGAATCTGCTGTACAGCTGGCGCTATGGCCCGAAAGCGGAAGCCAACCCGTGGGGCAGCAATGGCCTGGAATGGCAGATTAAAAGCCCGACCCCGTATGTCCCGTATCCGGCGGGCACCGAACCGGAAGTGGTGGGTCCGAACGATAACTATGCGGCCGAAGCCAAAGATCCGTTTATTTGGGTGAGCACGCCGAGCAAATGAATTCGCCGCAGCTAAACCATGACCGATAATTCCTACGCCAAACTGATGGATCCGGCGAGCGAACGCGCCAAACGCGGCGCGTTTTTCTTTCTGATGCTGTTTGCCGCCATTATTTTTGCGATGTGGGATCTGGCGCGCTTTCTGTGGGGTCACAGCGTGCCGGCGACCCTGAGTATGGGCGTTGGCGTTGCGCTGACGGTGCTGATGCTGGTGAGCCTGGTACCGGTGATGACCGCGCGCAAAAAACTGGATCAGGGGGATGATGCGGGCATTGTGAGCAGCCTGGCAACCCTGATGGTGGTGAGCCTGGTGATGGCGGGCGGCATTGTTTACAACTGGACGACGCTGACCATTGGTAGTGGCTATGGCGGCATTTACGATATTACCAGCCTGTGGTTTCTGGTGCACTTCGTGGCAGCCATTCTGGCACTGCTGGCCTCGATCATGAAAATTACCCGCACCCCGGAACGTGCCAAACGCGAACGCTGGGTTAGCTATAACGTGCTGACCTTTTGGGGTGGTGTAATTGTGCTGTGGGTGGCCTTTTTTATTGTCTTCTACATTGCCTAATGTAGCCTGGAAGATAGCCTGATGGAATAAGGCCTGGATAACGGTTACGTGACCTTCATTGTGCGCTATCCGGTTTGGTCGTAATAAAGCGATGTGTTTCTGGCGGATGGCGTTCATTGGGTGAGCTGCGGCGTTAGCGAATGCATTCTGCTGGTCGGCATTAAAGGCGGCAAACAGGGTCGCTCTGCAGCGTATAGCAAAAAAGGCCGCCGTCGCCGTATTTGGAAACGTATGATGTTTCATGGCTGGAGCTGCGAATACCGTGCCGCCATTCGCAGCTGTTTTGCCTTTTACTGCTGCGATGTTGTGATTCATGCGAGCAAAGAAGTGAACCATGAACAGGGCCGCCTGTTTAATTTTAGTCGTTAAAGCCGCTAATGGCGTATGGGCTGGAAACACCGTGTGGATCATCGTATTATTGTGGGCTATTACTGCAGCGGCTGGCTGCTGTGTTACCCGTAAAGCGCGGTACTGCGCCAGTTTCCGGGCTGGATTGGTCGTTAAATCCCGTGCTCCTCGATTCTGAAATGCCAGCGCGCACGCTAATGGTTTTGCCGTGCGGCAGGTAGCACCCAGGTCAAACGCTGAGGTGAAGGTATTACCTATGTGTACACCGAACACGATGAAAAAGAACTGGTGTGCGACTGCTGGTGCGGTTGTGGTAGCGGCGGCAATGGCCGCCATGGATATCGTGATGGCCGCCATGCCGGCTTCCATATGGAACGTGGTGATGCAAGCCCGAGCTAAGGCCACGCAGGCGAACGCTATCGCGAAAGCCAGTGGCGCTATAGCTACCTGCAGCGTCAGGATTGCACCTGCGGCCGTGGTGGCCGTGCCCCGGGTATCAGCGTGCCGGAACTGTAAAGCTCGTAACAGAAAGAACCGGATCTGGGCGATAGTCGTCGCGGTAACCGCCGTCGTGATCTGCACTAACACCAGCAGGGCATTTGGAGCTAATTTTAACATCATTAAAAACGTACGGCGCTGTGCGGCTATGCCGGCGACTAACCACACTGCCGCCGCAACTGGATCTAACCGGGCCCGAAACGCCGCCAAGTGCGTATCTATGGCTTTCATCTGGCCAGCGATGGCGGCTACCTTTTGCTGCGTATGAGCGATACCGGCGCGTGTCGCCATCGCTACGTTTGGTAAAATCATTGCCAGGTTAGCCCGGGCTGGCTGCTGTCAGGCCAGCTGTGCTTTGCGAGCGTGGGCCCGGGCGGCCAGGGCAAATGGCTGAGCTGCCCGGGCCATTGGTATCTGCTGTAAAACACCAACTTCATGTATTGTGTGATGCTGTGCATGATGCTGTAAGTTTGCCTGCATGCGACCTATGATGATTAAAGCAGCCTGCTGTCGTATGGCCCGCGCAAACATGCGGCCAGCGTTCATATTCTGATTCATAGCGATAAAGTGCCGAACAGTGATATTCTGACG

3.4. You have a sequence! Now what?

The sequence is over 8 kb long. So, I would suggest the use of cosmids for cloning. The cosmid can be inserted into E. coli, and be cloned. Inside the E. coli, the sequence replicates, transcripts, and finally translates into protein. The protein from this gene is found on the outer membrane of Acidithiobacillus ferrooxidans. But, since signal peptides and chaperone proteins for the desired protein is missing in the sequence, my educated guess is that it will be found intracellularly, and must be extracted and purified for further investigations.

Alternatively, the cell free method PURE (Protein synthesis Using Recombinant Elements) can also be used because of its faster turn around times. The DNA template strand is incubated in the presence of specific enzymes and cell extracts. The protein obtained must be purified through affinity chormatography.

4 DNA Synthesis Order

4.1 Creating accounts on Twist and Benchling:

Done.

4.2 Parts:

Lorem Ipsum

4.2.2: Promoter

TEF1

4.2.3: RBS

RBS1

4.2.4: Start Codon

ATG

4.2.5: Codon Sequence

Temporin 1 CE A.

atgttcaccttgaagaaatccctgttgctccttttcttccttgggaccatcaacttatctctctgtgaggaagagagagacgccgatgaggaagaaagaagagatgatcccgaagaaagggctgttgaagtggaaaaacgatttgtagat ttgaaaaagattgcaaatattatcaattctatatttggaaaataaccccaaaattgtaaaacttttgaaatgaaattggaaatcatctgatgtggaatatcatttagctaaatgcatatcagatgtcttacaaaaaataaagatatcacatgcaaaaaaaaaaaa

4.2.6: 7X His Tag

CATCACCATCACCATCATCAC

4.2.7: Stop Codon

TGA

4.2.8: Terminator

PGK1

4.3 Completed Plasmid

Part 5: DNA Read/Write/Edit

5.1 DNA Read

a. What DNA would you want to sequence (e.g., read) and why?
I would love to sequence the antifreeze protein gene from Leucosporidium sp..The protein has a lot of applications in food technology, and medicine, and I would love to produce it commercially.

I b. What technology or technologies would you use to perform sequencing on your DNA and why?
I would use SMRT (Single Molecule Real Time) sequencing technology from PacBio. It can generate long reads (10-25 kb) with Q40+ accuracy. It is also best used for de novo genome assembly.

c. Is your method first-, second-, or third-generation (or other)?
It is a third generation sequencing method.

d. What is your input? How do you prepare your input (fragmentation, adapter ligation, PCR)?
i) DNA has to be extracted and must be purified to make it free from proteins and RNA. Long and unbroken molecules are considered to be ideal. Freshly exracted DNA is preferred over stored one.
ii) The DNA is enzymatically sheared into 10-25 kb long fragments
iii) SMRTbell library format is preferred for the preparation of library, where hairpin adapters are ligated to both 5’ and 3’ ends to create a circular template of DNA fragment. iv) Sequencing primers and the appropriate polymerases are added to the buffer containing the DNA. v) It is then loaded on to SMRT cell, that contain zero-mode waveguides. Each ZMW captures a single DNA molecule for sequencing.

e. List the essential steps.
Answered above.

f. How does your chosen sequencing technology decode bases (base calling)?
Each nucleotide contains a unique fluorescent labels, which get excited with a laser whenever a new base is added. The instrument then records the color and timing of each flash, which corresponds to the base that has been added.

g. What is the output?
HiFi reads, usually 10-25 kb long are obtained as output.

5.2 DNA Write

a. What DNA would you want to synthesize (e.g., write) and why?
I want to synthesize the Temporin 1 CE A gene found in frogs. It is a small peptide antimicrobial, and can be used to combat antibiotic-resistant bacteria.

b. What technology or technologies would you use to perform DNA synthesis and why?
Phosphoaramidite method, followed by Gibson assembly can be used to synthesize it.

c. Essential steps of chosen synthesis methods
See Homeowrk 1 for the steps of phosphoaramidite synthesis.
Steps of Gibson Assembly:

1. Mix the pure, synthesized fragments into the reacton mix containing 5’ exonuclease, DNA polymerase, and DNA ligase. It is essential to ensure that the synthesized fragments have 15-30 bp overalaps to prevent random ligations.

2. Incubate the samples at 50 degree celcius. This ensures that only cannonical base pairing (A=T and G≡C) occurs and non canonical bonds are prevented due to their instability at this temperature.
3. exonuclease cleaves the -OH group, polymerase adds the nucleotides, and ligase binds the sequneces together.

d. Limitations (speed, accuracy, scalability)
Phosphoaramidite method is limited by its inability to synthesise fragments longer than 200 bp, poor yields for longer fragments, and relatively higher cost per synthesized base pairs. Gibson assembly is limited by its dependency on overlaping fragments that need to be precise. The assembled sequences must be sequenced again to make sure that it is accurate and misjoins and mutations have not occured.

5.3 DNA Edit

a. What DNA would you want to edit and why?
I would edit RSL4 gene in plants, since its overexpression increases root hair length. Longer root hairs allow the plant to uptake more nutrients.

b. What technology or technologies would you use to perform DNA edits and why?
I would use CRISPR/Cas9 because it allows precise, targeted edits and can be adapted for either gene activation or promoter replacement to drive RSL4 overexpression.

c. How does your technology edit DNA?
CRISPR/Cas9 uses a guide RNA to direct the Cas9 nuclease to a specific DNA sequence, where it introduces a double‑strand break. Repair pathways or engineered activators then modify or enhance gene expression.

d. Essential steps

1. Design guide RNAs targeting the RSL4 promoter or coding region
2. Clone them into a CRISPR vector
3. Deliver the construct into plant cells
4. Select transformed cells and regenerate whole plants

e. Preparation needed (design steps)
Identify target sites in the RSL4 promoter, ensure PAM sequences are present, and design guide RNAs with minimal off‑target potential. Choose a strong promoter or CRISPR activation system to boost expression.

f. Inputs (DNA template, enzymes, plasmids, primers, guides, cells)
Inputs include the RSL4 gene sequence, Cas9 enzyme, guide RNAs, plasmid vectors with promoters, plant cells for transformation, and primers for verification PCR.

g. Limitations (efficiency, precision)
CRISPR editing efficiency can vary across plant species, and off‑target effects may occur. Regeneration of edited plants is time‑consuming, and overexpression may cause unintended growth trade‑offs.

Week 3 HW: Lab Automation

0. Opentrons Art:

Code: https://colab.research.google.com/drive/1EMIMzVtB1k32tNOAKxGJH9ZDrxwvAGkC

JSON file: Download Opentrons art JSON

Acknowledgements: This format of coding (uploading a JSON file that contains the coordinates) was inspired from https://www.youtube.com/watch?v=K5nR0eYHLEk&t=4s. Huge thanks to Alireza Hekmati.

Coding, in its entirity, was handled by Gemini version 3.0 that was in-built in Collab.

Output:

1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

The paper: Slowpoke:An Automated Golden Gate Cloning Workflow for Opentrons OT‑2 and Flex

Keywords I would use to describe it: Opentrons OT-2, automation, standradization, synthetic biology.

Summary of the paper:
The authors developed an open-source software called ‘Slowpoke’ to automate the Golden Gate assembly process. Opentrons were used to carry out bacterial transformation, GG assembly, and plating. After a few manual steps in between, Opentrons were used once again to perform cPCR. It demonstrated the feasiblity of automating GG assembly. Opentrons were used to handle liquid transfers, reaction mixtures, and parameters. By integrating pipetting, transformataion, plating, and cPCR screening into a single pipleline. The validation was carried out manually using flow cytometry with transformed yeast cells. Using Slowpoke interface along with Opentrons, the authors achieved high assembly efficiencies, over 90% with Yeast Toolkit (YTK) and 60% with Subtilis Toolkit (STK), consistent with values reported for manual Golden Gate assemblies using these toolkits. To conclude, this paper designed a tool (Slowpoke) that generates Opentrons-ready protocols in the form of CSV files, mitigating the expertise needed in coding to a great extent. However, it must be noted that human input was still necessary to collect the DNA fragments for running cPCR.

v

2. Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details.

I would like to automate the prototyping of a novel Bio In-situ Resource Utilization (Bio-ISRU) on Mars that comprises of two trophic levels. The producer level utilizes photoautotrophic organisms to convert Martian CO2 (and of course, sunlight) to produce the nutrients required for the primary consumer level. The latter would consist of a ‘biominer’- a bacteria that can precipitate, from the Martian regolith, metals- cheifly iron in the form of Fe3O4- for easier metallurgical applications.

The automation tools would be utilized in the following ways:

1. 3D printing of a photobioreactor in gyroid shape to maximize the surface area for photosynthesis. This has to be validated in Martain gravity. Probably, can be carried out on a space station using a centrifuging apparatus to mimic the higher gravitational pull on Mars compared to Low Earth Orbit (LEO)
2. Bioreactor management (i.e, addition of nutrient media etc.)
3. Sensing if the maximum biomass has been achieved, and if yes, lysing the cells so that they may be utilized by the biominers
4. Efficient mixing of the lysed biomass with the Martian Regolith at appropriate ratio to maximize the precipitation of Fe3O4 by Acidithiobacillus spp.
5. Sensing the maximum quantity of Fe3O4 precipitaed, and removing it by operating a magnetic arm to separate the magnetite.
6. Sterilization of equippment, as well as decontamination, using gamma radiation.

Pseudocode (The following was the output of Gemini 3 for the prompt: “Write a pseudocode for the following”, and the above block was pasted.):

import ginkgo_nebula_api as nebula

# Configuration Constants
MARS_GRAVITY_RPM = 24.5       # Calculated RPM for centrifuge to mimic 0.38g
BIOMASS_THRESHOLD = 0.85      # OD600 value for harvest
MINING_RATIO = 0.4            # Ratio of Lysate to Regolith
IRON_SATURATION_POINT = 0.92  # Signal from Magnetic Flux sensor

class BioISRU_System:
    def __init__(self):
        self.stage = "PRODUCER_GROWTH"
        self.centrifuge_active = True

    def run_lifecycle(self):
        # 1. GRAVITY VALIDATION
        # Maintain Mars-simulated gravity via centrifugation
        if self.centrifuge_active:
            mars.centrifuge.set_speed(MARS_GRAVITY_RPM)

        # 2. PRODUCER STAGE (Cyanobacteria)
        if self.stage == "PRODUCER_GROWTH":
            mars.pbr.inject_media(interval="6h")
            current_od = mars.pbr.read_sensor("OD600")
            
            # Check if gyroid surface area is maximized with biomass
            if current_od >= BIOMASS_THRESHOLD:
                print("Maximum biomass achieved. Initiating Lysing...")
                self.stage = "LYSING"

        # 3. LYSING & HANDOVER
        if self.stage == "LYSING":
            mars.pbr.activate_ultrasonic_lyser(duration="10m")
            # Transfer to the Miner Tank
            lysate_volume = mars.pbr.get_volume()
            mars.transfer_pump.move(to="MINER_TANK", volume=lysate_volume)
            self.stage = "BIO_MINING"

        # 4. CONSUMER STAGE (Biominers + Regolith)
        if self.stage == "BIO_MINING":
            # Automated mixing of lysed biomass and Martian Regolith
            regolith_amount = mars.transfer_pump.last_volume * MINING_RATIO
            mars.regolith_feeder.add(regolith_amount)
            
            # Efficient mixing to maximize Acidithiobacillus contact
            mars.miner_tank.start_agitation(pattern="vortex", speed="medium")
            
            # 5. SENSING & MAGNETITE HARVEST
            iron_yield = mars.miner_tank.read_sensor("MAGNETIC_FLUX")
            if iron_yield >= IRON_SATURATION_POINT:
                print("Magnetite precipitation peaked. Deploying Magnetic Arm.")
                mars.magnetic_arm.extend()
                mars.magnetic_arm.energize()
                mars.magnetic_arm.extract_to(bin="IRON_STORAGE")
                self.stage = "DECONTAMINATION"

        # 6. STERILIZATION & RESET
        if self.stage == "DECONTAMINATION":
            print("Initiating Gamma Radiation Sterilization cycle...")
            mars.gamma_source.expose(duration="30m", target="ALL_CHAMBERS")
            
            # Uplink yield data to Ginkgo Nebula for strain optimization
            nebula.upload_log(yield_data=iron_yield, efficiency=1.2)
            
            # Reset for next cycle
            self.stage = "PRODUCER_GROWTH"
            print("System Reset. Starting new ISRU cycle.")

# Initialize and Loop
isru_unit = BioISRU_System()
while True:
    isru_unit.run_lifecycle()```   

### Final Project Ideas:  
[Google Slides](https://docs.google.com/presentation/d/1FAFN4YYisOcso3CI5F3W3Z7hj6_n9D1vAhVUywQXKPU/edit?slide=id.g3ca9627a0a6_624_27#slide=id.g3ca9627a0a6_624_27)

Week 04 HW: Protein Design Part 1

Part A. Conceptual Questions

Answer any NINE of the following:

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)

Assume that the mass of other components like fat, collagen etc. are negligible compared to proteins in meat.
Average wight of 1 molecule of amino acid = 100 Dalton = 1.7 * $10^{-24}$ g
Weight of piece of meat = 5 * $10^{2}$ g
Therefore, number of amino acid molecules = (5 * $10^{2}$) / 1.7 * $10^{-24}$ = 2.94 * $10^{26}$

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

All the biomolecules found in foods get broken down into their constituent molecules via different enzymes and get resynthesized into the required biomolecules in various metabolic cycles as per the requirement of the body. This is vastly different from the synthesis of proteins through genetic translation. However, if we can encode bovine genes into the human genome, there is a possibility that some of the proteins synthesised via translation resemble that of the cow rather than human proteins. However, this is not enough for a human to become a cow because amino acids alone do not maketh species!

3. Why are there only 20 natural amino acids? Can you make other non-natural amino acids?

To answer the second part of the question, I would say yes. It is indeed possible to make non-natural amino acids.
The first part however, is interesting. This paper lists out several reasons so as to why only 20 proteogenic amino acids exists. Some of the interesting takeaways I found are:

1. Some amino acids are highly “expensive to produce”. So, if two amino acids are (almost) similar in properties, life would favour the one with less productin cost. Case in point: Leucine requires 1 ATP, but Isoleucine requires 11.
2. Some of the possible side chains, especially the aromatic ones, would make it completely insoluble in water- which is detrimental for reactions. Therefore, any amino acid would have to be at least as soluble, if not more than the least soluble amino aicd (at pH 7), Tyrosine.
3. Some other side chains like esters and anhydrides are easily hydrolysed; ketones and aldehydes are suseptible to oxidation, reduction, and nucleophilic attacks; and carbon-carbon double and triple bonds are more reacctive than their single bond counterparts. Therefore, amino acids with those side chains are best avoided.
4. Secondary structure that does not form bonds with other amino acids; molten globules (non-polar parts hidden inside) will have flexible side chains that msut be frozen into fixed positions, costing energy; and aggregated clumps of amino acids, especially beta sheets, are useless and oftentimes even toxic (amyloids). Therefore, these can’t be overly favoured.
5. Incorporating other elements beside C, H, O, N also comes with a high energy costs. S containing amino acids (methionine and cysteine) are energetically expensive than other amino acids of the similar size. Therefore, evolution didn’t favour the ones with other elements.

4. Design some new amino acids.

5. Where did amino acids come from before enzymes that make them, and before life started?

The RNA world hypothesis can explain this to an extent. If we consider the first catalysing molecules to be RNAs, then it could very well have been possible for them to catalyse the synthesis of amino acids. Clay particles arranging themeselves in such a geometry that they would catalyse the synthesis is also a proposed hypothesis. Since these are biotic synthesis, only one of the racemic form would have been favoured. If abiotic synthesis of amino acds is considered, then the Miller-Urey experiment proved that high energy discharge in the form of lightining can lead to the formation of amino acids. Some other ways for abiotic snthesis would be; undersea volcanic eruptions; and meteorite impact etc. Although in these cases, equal proportion of L and D forms are likely to be formed.

6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?

I would expect it to be left-handed helix. Since D-amino acids are the mirror images of L-amino acids, the steric and geometric parameters would flip, leading to the left-handed helix.

7. Can you discover additional helices in proteins?

Apart from $\alpha$ helices, $3_{10}$ helices, and $\pi$ helices, polyproline helices, collagen helices are known to exist. So, yes, it is possible to discover additonal helices in proteins.

8. Why are most molecular helices right-handed?

Due to the potential for steiric hindarance, biologically, either the biomolecules can all be either right-handed or left-handed helices. It is not possible for some molecules to be right-handed and some others to be left-handed. The right-handedness of proteins is due to the existence of L-amino acids, and the right-handedness of nucleic acids is due to D-sugars. Computation simulations show that L-amino acids consistently fold into stable structures than their D coutnerparts in ambient conditions. The same can be inferred for the stability of nucleic acids, although left-handed stuctures are known to exist under specific conditions.

9. Why do β-sheets tend to aggregate? What is the driving force for β-sheet aggregation?

1. Extensive hydrogen bonding
2. Hydrophobic side chains intergigitated with hydrophilic ones
3. Flat planarity as opposed to curved nature of helices
4. Strucutral complementarity of the edges, that favour inter-strucutral bond formation.

10. Why do many amyloid diseases form β-sheets?

Skipped.

11. Can you use amyloid β-sheets as materials?

Skipped.

12. Design a β-sheet motif that forms a well-ordered structure.

Skipped.

Part B. Protein Analysis and Visualization

Pick any protein with a 3D structure and answer:

1. Briefly describe the protein you selected and why you selected it.

Proline-Betaine Transporter is the protein I have selected, and it is because it acts as an osmoprotectant for bacteria under high salt concentrations.

2. Identify the amino acid sequence of your protein. How long is it? What is the most frequent amino acid?

Sequence: MLKRKKVKPI TLRDVTIIDD GKLRKAITAA SLGNAMEWFD FGVYGFVAYA LGKVFFPGAD PSVQMVAALA TFSVPFLIRP LGGLFFGMLG DKYGRQKILA ITIVIMSIST FCIGLIPSYD TIGIWAPILL LICKMAQGFS VGGEYTGASI FVAEYSPDRK RGFMGSWLDF GSIAGFVLGA GVVVLISTIV GEANFLDWGW RIPFFIALPL GIIGLYLRHA LEETPAFQQH VDKLEQGDRE GLQDGPKVSF KEIATKYWRS LLTCIGLVIA TNVTYYMLLT YMPSYLSHNL HYSEDHGVLI IIAIMIGMLF VQPVMGLLSD RFGRRPFVLL GSVALFVLAI PAFILINSNV IGLIFAGLLM LAVILNCFTG VMASTLPAMF PTHIRYSALA AAFNISVLVA GLTPTLAAWL VESSQNLMMP AYYLMVVAVV GLITGVTMKE TANRPLKGAT PAASDIQEAK EILVEHYDNI EQKIDDIDHE IADLQAKRTR LVQQHPRIDE

Length: 500

Most frequent amino acid: Leucine (L)

3. How many protein sequence homologs are there for your protein? (Hint: Use Uniprot’s BLAST tool)

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4. Does your protein belong to any protein family?

It is a transport protein.

5. Identify the structure page of your protein in RCSB. When was the structure solved? Is it a good quality structure?

Yes, the structure was solved. It is a good quality structure too.

6. Are there any other molecules in the solved structure apart from protein?

None

7. Does your protein belong to any structure classification family?

I don’t think it does.

8. Open the structure in PyMol (or similar).

• Visualize as “cartoon”, “ribbon”, “ball and stick”.
• Color by secondary structure. Does it have more helices or sheets?
• Color by residue type. Distribution of hydrophobic vs hydrophilic residues?
• Visualize surface. Does it have binding pockets?
  It has only helices.

Part C. Using ML-Based Protein Design Tools

C1. Protein Language Modeling

• Generate deep mutational scan with ESM2. Can you explain any particular pattern?
  
• (Bonus) Compare predictions to experimental scans.
  Skipped
• Latent space analysis: Place your protein in the map and explain its position.

C2. Protein Folding

• Fold your protein with ESMFold. Do predicted coordinates match original?
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• Try mutations and large sequence changes. Is structure resilient?
  Lorem Ipsum

C3. Protein Generation

• Inverse-folding with ProteinMPNN. Compare predicted vs original sequence.
  Lorem Ipsum
• Input sequence into ESMFold and compare predicted structure.
  Lorem Ipsum

Part D. Group Brainstorm on Bacteriophage Engineering

1. Choose one or two main goals (e.g., stabilize lysis protein, disrupt interaction with E. coli DnaJ).

My group consisted of 4 people: @lorem ipsum, @lorem ipsum, @lorem ipsum, and @lorem ipsum. After brainstorming, we decided to focus on stabilizing the lysis protein.

2. Write a 1-page proposal describing:

• Tools/approaches you propose using
• Why those tools might help
• Potential pitfalls
• Include schematic of pipeline

Proposal:
By: 2026a-nourelden-rihan, 2026a-ritika-saha, 2026a-rahul-yaji, 2026a-keerthana-gunaretnam

• We decided to focus on the main area of increasing the stability of the MS2 phage lysis protein L, with a possible secondary goal of reducing the dependency on host DnaJ, while still maintaining the lysis action.

• The tools AlphaFold, Clustal Omega, BLAST, ESM, and ESMFold were discussed.

• BLAST can pull out homologous lysis proteins from the databases.

• Clustal Omega can create MSAs to identify essential L48-S49 residues, and the pore-forming regions that must not be mutated.

• ESM can create mutation heatmaps, which can guide the use of ESMFold to obtain highest score foldings in mutatable regions.

• AlphaFold Multimer predicts whether the subunits of our protein can successfully create a pore in the host membrane, and also to check whether N-terminus can break the interaction with DnaJ.

• We also identified a few pitfalls, with majors ones dealing with limited training datasets, that may not be properly aligned towards creating a transmembrane lysis protein.

• Some other pitfalls include the lack of proper annotations for amurins; the possibility of an over-stable protein to form non-functional aggregates; and the vulnerability of modified protein to host proteases.

Schematic:

The detailed proposal

Week 05 HW: Protein Design Part 2

Part 1: Generate Binders with PepMLM

1. Retrieve sequence and introduce mutation: (Pasted the sequence from UniPort, deleted M at 1st position, changed A to V at 4th position.)

ATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

Structure of the native sequence- predicted vs actual:

2. Generate 4 peptides using PepMLM Colab:

3. Known binder: FLYRWLPSRRGG
4. Perplexity score: 22.5252

A note about perplexity score: A key evaluation metric for language models that measures how well a probability model predicts a sample. Lower the score, higher the confidence of the model that the output satisfies the criteria.

Part 2: Evaluate Binders with AlphaFold3

• Peptide	Binding location	ipTM score
  WRSPAVAVAHWE	None	0.28
  WRVGWVGVELKE	None	0.35
  WRSPAAXIEHKX	None	0.33
  WRVYAAXIEWGK	None	0.34

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse

Peptide Comparison Results:

• The best peptide I would chose for wet lab validation would be WRVGWVGVELKE due to its relatively high binding affinity.

Part 4: Generate Optimized Peptides with moPPIt

Parameters:

• Binder	Hemolysis	Solubility	Affinity	Motif
  SVKTKCCTTYQS	0.96447	0.916667	6.5756	0.890471
  DDTKKCSCIQTH	0.974932	0.916667	6.31426	0.914592
  ENGETFQCTKKV	0.970342	0.833333	6.04386	0.934673
  KKSKKAFVCCVC	0.963174	0.666667	8.17171	0.613892

For the very long execution time, and the computational resources this program took, the only significant advantage it has (in this particular context) over PepMLM is the motif score, since there was no option to check for the motif specificity in the Peptiverse. All the other properties of the PepMLM generated sequences (predicted using Petptiverse) and those of the moPPIt peptides are comparable.

Part B: BRD4 Drug Discovery Platform Tutorial (Optional)

• Skipped

Part C: Group Project: L-Protein Mutants

• I chose the third option- Generating random mutations in the Lysis protein while avoiding the loss of function or non sense codons.The Python script was generated solely by the Google Gemini 2.5 Flash, that is in-built in Google Colab. The prompt was:

Develop a Python program in Google Colab that processes an amino acid sequence and generates mutated versions of it based on experimental data. The program should perform the following steps:

• Prompt the user to enter an amino acid sequence.

• Load mutation data from a publicly accessible Google Sheet URL (https://docs.google.com/spreadsheets/d/11WzDDNkQDEiqbUSGV0ZCqITGctyNFpD7xnPlhsj2BhE/edit?gid=0#gid=0).

• The data contains information about amino acid changes and their associated ‘Lysis’ activity.

• Filter the mutation data to include only ‘active’ mutations (where ‘Lysis’ is not 0). Extract the ‘Original_Residue’, ‘Position’, and ‘Mutated_Residue’ from the relevant columns (e.g., ‘Amino Acid Change’ and ‘Amino Acid Position’ or a ‘Mutation’ column like ‘X###Y’).

• Create a helper function to format amino acid sequences by inserting a space after every 5 amino acids for better readability.

• Implement a function generate_random_mutation_combinations(sequence, mutation_df, num_mutations) that takes an original amino acid sequence, the filtered active mutations DataFrame, and the desired number of mutations as input.

• This function should:

  • Identify all valid mutation sites where the original residue in the sequence matches an original residue in the mutation_df.
  • Ensure that the num_mutations are applied to unique positions in the sequence. If there are fewer available unique mutation positions than num_mutations, it should apply all available unique mutations.

• Randomly select mutations from the available options for the chosen unique positions.

• Return the new mutated sequence and print the applied mutations.

• Generate Multiple Mutated Sequences: Prompt the user for the number of mutated sequences they wish to generate. For each requested sequence:

• Call the generate_random_mutation_combinations function.

• Display the generated sequence with a clear heading (e.g., ‘Sequence 1:’, ‘Sequence 2:’, etc.).

• Print both the original and the mutated sequences, using the formatting function defined in step 5.

• In a separate code block, display each generated mutated sequence individually using display() so that each sequence is easily copyable by the user.

  Python script

  The generated mutational sequences were:
  0. METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT (Original)

  1. METRFPQQSQQTPASTNRRRPFKHEDYPCQRQQRSSTLYVLIFLAFFLSKFTNQLLLSLLEAVIRTVTTLQQLLT
  2. METRFPQQSQQTLAATNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT
  3. METRFPQQSQQTPASTNRRRPFKHGGYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT

  AF2 Multimer was used to co-fold the mutant Lysis protein (METRFPQQSQQTPASTNRRRPFKHEDYPCQRQQRSSTLYVLIFLAFFLSKFTNQLLLSLLEAVIRTVTTLQQLLT) and DnaJ:

  

Cofolding was not performed for the other two sequences as my laptop started getting stuck while running the program.

The plDDT score indicates that the model is not confident about the folding of the input random mutated L protein. Overall, it suggests that the random mutation approach is very time consuming to obtain leads.

Later, cofolding was performed using Alphafold server, and the results obtained are shown below:

Week-06-hw-genetic-circuits-part-i

DNA Assembly

1. What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose?

   1. Phusion DNA Polymerase: Pyrococcus-like enzyme that contains a fused processivity-enhancing domain. It provides more than 50 gold higher fidelity than Taq polymerase.
   2. dNTPs: contains dATP, dCTP, dGTP, and dTTP that are required for extension reaction of the PCR.
   3. Buffers: MgCl2 as a cofactor for polymerase, KCl and TAPS-HCl ([tris(hydroxymethyl)methylamino]propanesulfonic acid) to maintain ionic strength and pH respectively, and beta-meracaptoethanol to maintain enzyme stability.
   4. Some other components that are provided seperately: DMSO (Dimethyl sulfoxide) to improve denaturation and primer binding, and nuclease free water as a solvent and matrix to avoid denaturation of the DNA.

2. What are some factors that determine primer annealing temperature during PCR?

   1. Primer melting temperature: annealing temperature must be set around 3 to 5 degree celcius below the lowest melting temperature. So. anything that affects melting temperature also affects annealing temperature. Melting temperature is in turn affected by GC conent and primer length. Higher the GC content, and longer the length of the primer, the higher will be the melting temperature. For short primers (<20 bps) Wallace rule can be used to find the approximate primer melting temperature:
      $$T_m (°C) = 2(A + T) + 4(G + C)$$

   2. Salt and ion concentration: monovalent cations like Na+ and K+ reduce the repulsion between two DNA strands by nutralizing the negative charge of the phosphate backbone. Mg2+ concentration, which is a cofactor for the polymerase, also increases the stability of the double helix, increasing the melting temperature.

   3. Presence of Denaturants like DMSO and Formamide: They disrupt hydrogen bonds, and reduce the melting temperature.

   4. Degenarate primers (primers that are not 100% match to the template) reduce the melting temperature, and complexity of template DNAs (Eg.; humans as opposed to bacteria) also require a higher annealing temperature to avoid ‘mispriming’.

3. There are two methods from this class that create linear fragments of DNA: PCR, and restriction enzyme digests. Compare and contrast these two methods, both in terms of protocol as well as when one may be preferable to use over the other.

   PCR vs. Restriction Enzyme Digest

When to Prefer PCR vs. Restriction Digest

4. How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning?

5. Verification of Primer Design: The success of the assembly depends on the specific architecture of the primers:

• Overlaps: Each primer must include a 20–22 bp overhang complementary to the adjoining fragment.

• Binding Region: The core binding region should be 18–22 bp.

• Melting Temperature (Tₘ): The Tₘ should be between 52–58°C.

• Pair Compatibility: Primer pairs should have Tₘ values within 5°C of each other.

• GC Content: Aim for 40–60% GC content with a GC clamp (1–2 G/C bases) at the 3′ end.

• Secondary Structure: Use software to ensure Gibbs free energy is above –10 kcal to avoid strong hairpins or dimers.

  2. Post-PCR Processing Before assembly, fragments must be cleaned and templates removed:

• DpnI Digestion: Treat PCR reactions with DpnI to eliminate the original mUAV plasmid (digests methylated DNA, preserves unmethylated PCR products).

• DNA Purification: Use a purification kit (e.g., Zymo) to remove salts and enzymes.

• Quantification: Measure DNA concentration with Nanodrop or Qubit; should be >30 ng/µL.

  3. Quality Control (Diagnostic Gel)

• Run samples on an agarose gel at 100 mV for 15 minutes.

• Verify that bands match the predicted size calculated on Benchling.

  4. Reaction Parameters

• Molar Ratio: Use a 2:1 (insert:vector) molar ratio for optimal efficiency.

• Orientation: Confirm fragments have correct 5′ → 3′ orientation with matching overlaps.

  5. How does the plasmid DNA enter the E. coli cells during transformation?
     It enters through pores in the cell wall. The pores can be created using CaCl2 treatment, followed by heat shock (mixture kept on ice bath is suddenly incubated at 42 degree celcius for 30-90 seconds). Electroporation is another method, where a high-voltage electric pulse applied for a very short duration brefily disrupts the phospholipid bilayer, and simultaneously pushes the DNA molecules through the pores.

6.1 Describe another assembly method in detail (such as Golden Gate Assembly). Explain the method in 5–7 sentences plus diagrams (either handmade or online).
Modular Cloning Method: It is a method based on Golden Gate Assembly. It utilized Type IIS restiction enzymes that cut outside their restrcition site and create non-palindromic overhangs. The final product doesn’t contatin restriciton site, preventing the enyme from double-cutting.

Steps:
Step 0: Removal of the internal recognition sites so that the enzyme being used will not cleave it internally, addition of standard 4-bp overhangs and inserting the thus-modified sequence into storage vector. This has to be done seperately for all the units of transcription, i.e., promoter, 5’ UTRs, rbs, cds, terminator.

Step 1: The components of step 0 are added into the reaction vessel, along with the destination vector, restriciton enzyme, T4 ligase, buffer, and ATP. The temperature is cylced to and fro from a higher temperature (~37 degree celcius) for cutting, and a lower temperature (~16 degree celcius) for sticking. The restriction enzyme leaves behind the specific 4-bp overhangs. The DNA ligase binds the 4-bp overhangs in the order of Promoter -> 5’ UTR -> RBS -> CDS -> Terminator in the insertion site of the destination vector, which already contains selection and screening genes.

Step 2: In case a complex metabolic pathway involving multiple genes is to be synthesised, the final desitnation vector of the step 1 is used as storage vector for the step 2, and step 1 is repeated using other genes.

(Credit: https://www.addgene.org/cloning/moclo/)

6.2 Model this assembly method with Benchling or a similar tool!
I got the following error.

Asimov Kernel

1. Construct and simulate the repressillitaor.

   • A Repository was created using the “New” button.
   • A Notebook was created using the same button to document the homework.
   • In the notebook, a blank construct was created, the repressillator found in the Demo was recreated part by part.
   • “Search bar” was used to search for the parts, and they were dragged and dropped at the desired location
   • Using the simulation option, the repressillator was simulated using the following parameters:
     • Chassis: E. coli
     • Duration: 504 hours
     • Timestep: 60 minutes
     • Transfection: Transient Transfection
   • The following output was recorded:
     • 

2. Build three of your own devices using the parts in the Characterized Bacterial Parts Repo and explain how you think the devices should function in an Electronic Notebook Entry.
   First Part: Overexpression of lactic acid

   • The nucleotide sequence of the Ldh gene was copied from NCBI, and the start sequence ATG and Stop sequence TAG were manually inserted in the “Create part” option
   • T7 promoter and terminator were used by creating new parts with the respective sequences taken from Vector Builder
   • The effect of increasing the number of copies of gene was simulated using the parameters:
     • Chassis: E. coli
     • Duration: 24 hours
     • Timestep: 10 minutes
     • Transfection: Transient Transfection.

• It was found that the more the gene copies, the higher the protein levels.
• Another interesting observation was that, the CDS must be followed by an RBS for each copy, even if they are flanked by the same promoter and terminator. Without RBS, it will not be translated and thus the protein levels stay down.

Second Part: Inducerless NOT Gate

Third Part: Inducerless OR Gate

Week-07-HW-genetic-circuits-part-ii

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

1. What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions?

   1. They can interpret a range of inputs as opposed to the 0, 1 inputs of traditional genetic circuits. This allows them to aggregate multiple signals and apply the activation fucntion to filter biological noise.
   2. Traditional circuits often require a cascade of genetic logic gates, which lead to metabolic burden and competition for substrates. By utilizing weighted interactions, IANNs can accomplish the same task using fewer biolocial components.
   3. Nonlinear descision making is a struggle for tradional genetic circuits. They struggle to take into accout the relative ratios and thresholds of a multitude of proteins simultaneously, limiting themselves to simple linear logics. However, using ReLU and sigmoid -like activation behaviours, IANNs can perform complex tasks. Eg: A cell may be engineered to apoptosize only when a commplex profile of cancer markers are met, as oppossed to the presence of some of those markers that may not be cancerous.

2. Describe a useful application for an IANN; include a detailed description of input/output behavior, as well as any limitations an IANN might face to achieve your goal.
   A useful applicaiton of IANN would be rapid plant cell response when it is infected by a pathogen.

   1. Input 1: Detection of Pathogen Associated Molecular Patterns. Chitin would be a good choice given that fungi are the most damaging plant pathogens.
   2. Input 2: Detection of Plant Stress Volatiles like Methyl Salicylate. This adds one more layer of confirmation that the plant is under attack.
   3. Input 3: Detection of Effector Proteins like Avr4 that are used by fungi to protect itself fromm plant defense mechanisms.
   4. Different weights need to be assigned for different inputs. In this case, input 3 may be given more weightage compared to input 1.
   5. The output may be in the form of a targeted release of antifungal peptide or apoptosis.

3. Draw a diagram for an intracellular multilayer perceptron where layer 1 outputs an endoribonuclease that regulates a fluorescent protein output in layer 2.
   

Assignment Part 2: Fungal Materials

1. What are some examples of existing fungal materials and what are they used for? What are their advantages and disadvantages over traditional counterparts?

   • Mycelium Leather: A sustainable alternative to traditional leather. Unlike the latter, it can be produced in as little as 5 days, and is biodegradable too. It can be treated with different chemicals to make it waterproof, weatherproof, and damage resistant. The chief disadvantage is that it is less robust than the animal-based ones and since it requires a controlled enviornment, the production costs will be on a higher side.
   • Mycellium-based composites: Organic wastes, especially agriculture wastes like wood chips, straws etc. are used as substrates to grow the fungi. The fungl mycellium holds the substrate together and the resulting material, after killing the fungi by baking, is called ‘MBC’. It finds its use in numerous fields such as packaging, insulation, construction materials etc. The primary disadvantage is that it is difficult to scale, and therefore, is not cost-effective. Some companies like Evocative, Mycoworks, and Mogu are working on MBCs.
   • Martian shelters: NASA is working on a system where the astronauts carry dormant fungi and a mould. When activated with water, the fungi grows around the mould, forming a fully functional human habitat. Prototypes have been built using Ganoderma lucidum, and have shown significant potential for water filtration, bioluminescent lighting, and self-repair. Additionally, pound for pound, mycellium-based builidng materials can outperform concrete in terms of strength.

2. What might you want to genetically engineer fungi to do and why? What are the advantages of doing synthetic biology in fungi as opposed to bacteria?
   Fungi can be better chassis organisms for genetic engineering than bacteria as they possess eukaryotic cell machinery, and are capable of post-translational modificaitons. The latter can be exploited for glycosylation of proteins, especially of antibodies; and phosphorylation and acetylation for protein modification. This, coupled with the fact that they are capable of advanced protein folding makes them the organism of choice to produce complex human proteins.

   Additionally, fungi have superior secretion capacity that is complemented by compartmentalization. They can sequester toxic intermediates in organelles like vacuoles and peroxisomes and allow the cell to secrete high concentrations of desired chemicals that would have been lethal if it were to be found in cytoplasm. Their superior secretion capacity allows them to produce chemicals in “grams per litres” concentration. Moreover, the chemicals are usually secreted outside the cell, saving us the cost of cell-disruption and simplyfying the purification.

Even the growth requirement of fungi is more robust and adaptive compared to the bacteria. They can be grown on solid substrate with minimal additions, and can tolerate acidic enviornments better.

I would love to engineer fungi to produce biological selenomelanin- a type of melanin that incorporates selenium instead of sulphur. Fungi can be engineered to utilize selenocystine for the bioproduciton of selenomelanin. Also, fungal mycellium can be engineered to produce selenomelanin to proivde superior radiation protection to be used as martian shelters.

Assignment Part 3: First DNA Twist Order

1. Review the Individual Final Project documentation guidelines. Submit this Google Form with your draft Aim 1, final project summary, HTGAA industry council selections, and shared folder for DNA designs.

   • Lorem ipsum

2. Review Part 3: DNA Design Challenge of the week 2 homework. Design at least 1 insert sequence and place it into the Benchling/Kernel/Other folder you shared in the Google Form above.

   • Lorem ipsum

3. Document the backbone vector it will be synthesized in on your website.

   • Lorem ipsum

week-09-hw-cell-free-systems

Part A: General & Lecturer-Specific Questions

General Homework Questions

1. Explain the main advantages of cell-free protein synthesis over traditional in vivo methods, specifically in terms of flexibility and control over experimental variables.

   Rapid Iteration and Throughput

• Direct Use of Linear DNA Templates
  Traditional methods require time-consuming cloning of DNA into circular plasmids before they can be inserted into a host cell.
  In CFPS, you can use raw PCR products directly as the instruction manual, allowing you to move from a genetic design to a functional protein in just a few hours.

• High-Throughput Screening Compatibility
  Because the reaction occurs in a simple liquid phase without the need for incubator space or shaking flasks, it can be easily scaled down into 96-well or 384-well plates.
  This allows robotic systems to simultaneously test hundreds of different protein variants or reaction conditions under identical parameters.

• Elimination of Cell Recovery and Lysis
  In living systems, you must wait for the culture to reach a specific density and then physically break the cells open to harvest the protein.
  CFPS skips these steps entirely because the protein is synthesized

2. Name at least two cases where cell-free expression is more beneficial than cell production.

• Production of Cytotoxic Proteins
  In traditional in vivo production, the target protein often interferes with the host cell’s survival. For example, if you are trying to produce antimicrobial peptides (AMPs) or pore-forming toxins, the protein will kill the E. coli or yeast “factory” as soon as it is expressed, leading to zero yield.
  CFPS Benefit: Since there is no living cell to keep alive, the system is indifferent to the toxicity of the product. This allows for the high-titer production of potent toxins, lytic enzymes, and other proteins that are normally “undruggable” or unproduceable in living hosts.

• Incorporation of Non-Canonical Amino Acids (ncAAs)
  If you want to create a “designer” protein with chemical properties not found in nature—such as adding a fluorescent tag, a “click-chemistry” handle, or a post-translational modification—you must use ncAAs. In a living cell, this requires complex metabolic engineering to ensure the cell doesn’t accidentally incorporate the synthetic amino acid into its own essential proteins, which would be lethal.
  CFPS Benefit: You can directly manipulate the translation machinery by adding pre-charged tRNAs and orthogonal synthetases without worrying about cross-reactivity with the host’s proteome. This provides a high degree of “chemical site-specificity,” allowing for the production of sophisticated protein-drug conjugates and advanced biomaterials.

3. Describe the main components of a cell-free expression system and explain the role of each component.
   Main Components of a Cell-Free Expression System

• The Crude Extract (The Machinery)
  The extract is the heart of the system, typically derived from cells like E. coli, wheat germ, or rabbit reticulocytes that have been physically lysed.
  Role: Provides the essential molecular “hardware” required for translation, including ribosomes, tRNAs, aminoacyl-tRNA synthetases, and translation factors. It also contains endogenous enzymes needed for energy regeneration.

• The DNA Template (The Instructions)
  This is the genetic blueprint for the protein you want to synthesize. Unlike in vivo methods, this can be a circular plasmid or a simple linear PCR product.
  Role: Contains promoter and terminator sequences that tell the machinery where to start and stop. Serves as the instruction manual for mRNA production (transcription) and subsequent protein synthesis (translation).

• Energy Regeneration System
  Protein synthesis is energetically expensive. Since the system is no longer part of a living cell, it cannot “eat” or perform cellular respiration to stay powered.
  Role: Typically consists of high-energy phosphate compounds (like phosphoenolpyruvate or creatine phosphate) and corresponding kinases. Acts as a “battery pack” to continuously regenerate ATP and GTP, which are consumed during amino acid chain assembly.

• Substrates and Cofactors (The Building Blocks)
  These are the raw materials added to the reaction buffer to facilitate biochemical reactions.

  • Amino Acids: The 20 standard building blocks or even non-canonical ones, used to assemble the protein chain.
  • Nucleotides (NTPs): Used for transcribing DNA into mRNA and as energy carriers.
  • Salts and Buffers: Magnesium ($Mg^{2+}$) and potassium ($K^{+}$) ions are strictly required for ribosome stability and enzymatic activity, while buffers maintain a stable pH.

4. Why is energy provision/regeneration critical in cell-free systems? Describe a method you could use to ensure continuous ATP supply in your cell-free experiment.
   • Energy Demand of Translation
     Each amino acid added to a growing peptide chain consumes two ATP (for tRNA charging) and two GTP (for ribosome movement), making protein synthesis one of the most energy-intensive processes in biochemistry.

• Risk of Rapid Depletion
  In a closed system without recycling, the initial ATP/GTP pool would be exhausted within minutes, stalling protein production. Accumulated phosphate byproducts can also bind magnesium ions, destabilizing the reaction.

• Enzymatic Regeneration Pathways
  We can add high-energy donor molecules (e.g., phosphoenolpyruvate or creatine phosphate) with kinases like pyruvate kinase. These enzymes recycle ADP back into ATP, acting as a biological “battery chagrer.”

• Dialysis-Based Continuous Supply
  Advanced setups use semi-permeable membranes to allow fresh nutrients and ATP to diffuse in while removing inhibitory byproducts. This maintains chemical equilibrium and enables sustained protein synthesis for days.

Source:

5. Compare prokaryotic versus eukaryotic cell-free expression systems. Choose a protein to produce in each system and explain why.
   Comparison of Prokaryotic vs. Eukaryotic Cell-Free Systems

   Example 1: Prokaryotic System (E. coli)

• Protein to Produce: Green Fluorescent Protein (GFP)
• Reasoning: GFP is small, robust, and does not require post-translational modifications to fluoresce.
• Efficiency: E. coli extracts have the highest translation rates, enabling vast quantities of GFP production within hours.
• Monitoring: Fluorescence can be tracked in real-time. GFP serves as an ideal reporter protein for testing new cell-free reaction conditions or energy regeneration strategies, since its folding is simple enough for bacterial machinery.

Example 2: Eukaryotic System (CHO Cells)

• Protein to Produce: Tissue Plasminogen Activator (tPA)
• Reasoning: tPA is a complex human enzyme used to dissolve blood clots and is difficult to produce in bacteria.
• Disulfide Bonding: Contains 17 disulfide bonds. Bacterial cytoplasm is highly reducing and fails to form these correctly. Eukaryotic extracts with microsomal membranes provide the oxidative environment and chaperones (e.g., Protein Disulfide Isomerase) for proper folding.
• Glycosylation: Requires specific sugar chains for stability and activity in the human body. Eukaryotic cell-free systems can be supplemented with microsomes (ER-derived vesicles) to perform these modifications, which are impossible in E. coli systems.

I have tried to sum up the advantages and disadvantages comparision of both expression systems here:

6. How would you design a cell-free experiment to optimize the expression of a membrane protein? Discuss the challenges and how you would address them in your setup.

The key factors required to design a cell-free experiment for membrane proteins are:

• 1. Artificial Lipid Environments
  Membrane proteins are usually hydrophobic and require a lipid bilayer to fold correctly. In cell-free systems, researchers introduce artificial lipid structures such as liposomes, nanodiscs, or microsomes to mimic the natural membrane. These environments stabilize the protein during synthesis and facilitate proper insertion and folding. The MEMPLEX platform, for example, generates thousands of lipid-protein combinations to identify optimal conditions for each membrane protein.

• 2. Controlled Chemical Interactions
  Since CFPS allows precise control over the chemical environmentm, we can independently vary lipid composition, ionic strength, redox potential, and chaperone concentrations. This enables the fine-tuning of protein-protein and protein-lipid interactions, which are critical for membrane protein stability and functionality. MEMPLEX uses machine learning to predict and optimize these combinations, accelerating the design of functional synthetic environments.

The following problems may be encountered:

• Challenge A: Protein Aggregation and Misfolding
  Hydrophobic transmembrane helices tend to clump together or stick to the tube walls without a lipid bilayer.
  Solution: Implement nanodiscs — small, uniform discoidal bilayers wrapped in membrane scaffold proteins (MSPs). Unlike liposomes, nanodiscs keep membrane proteins soluble and monomeric, making them ideal for structural studies such as Cryo-EM.

• Challenge B: Low Yields due to Resource Depletion
  Membrane protein synthesis is slower and consumes more energy than soluble protein synthesis, leading to rapid depletion of ATP and accumulation of inhibitory byproducts.
  Solution: Use the Continuous-Exchange Cell-Free (CECF) method. A dialysis membrane provides a constant supply of ATP and nutrients while removing inorganic phosphate, sustaining the reaction for complex protein folding.

• Challenge C: Maintaining Correct Orientation
  In vitro systems lack the natural “inside-outside” topology of living cells, so proteins may insert incorrectly into synthetic membranes.
  Solution: Adjust the physicochemical environment by tuning lipid ratios (e.g., phosphatidylethanolamine [PE] or phosphatidylglycerol [PG]) to encourage the Positive-Inside Rule. Supplement with purified chaperones (e.g., DnaK, GroEL) to keep proteins flexible until proper orientation is achieved.

7. Imagine you observe a low yield of your target protein in a cell-free system. Describe three possible reasons for this and suggest a troubleshooting strategy for each.

   Common Reasons for Low Protein Yield in CFPS and Fixes

• Rapid Template Degradation
  Crude extracts tend to contain nucleases that degrade linear DNA templates before transcription.
  Fixes:

  • Add nuclease inhibitors (e.g., GamS to block RecBCD).
  • Switch to circular plasmid DNA, which is more resistant to degradation, but is slower to replicate.

• Magnesium Ion ($Mg^{2+}$) Imbalance
  Magnesium stabilizes ribosomes and enzymes, but its optimal range is narrow (8–15 mM). Too little causes ribosome collapse; too much causes mRNA aggregation. ATP breakdown also sequesters magnesium mid-reaction.
  Fixes:

  • Perform magnesium titration (e.g., 2 mM increments in 96-well plates).
  • Use stronger buffers (HEPES or Tris) to maintain pH and magnesium solubility.

• Inefficient Protein Folding or Aggregation
  This is usually the main culprit when complex proteins with disulfide bonds are involved. They may misfold or aggregate at high local concentrations.
  Fixes:

  • Lower reaction temperature (e.g., from 37°C to 25–20°C) to slow synthesis and allow proper folding.
  • Add molecular chaperones (e.g., DnaK, DnaJ, GroEL/ES) to prevent aggregation and assist folding.

Kate Adamala’s Question

• Design an example of a useful synthetic minimal cell:
  • Pick a function and describe it (input/output).
  • Could this function be realized by cell-free Tx/Tl alone, without encapsulation?
  • Could this function be realized by a genetically modified natural cell?
  • Describe the desired outcome of your synthetic cell operation.
  • Design all components (membrane composition, encapsulated molecules, Tx/Tl source organism).
  • How will your synthetic cell communicate with the environment?
  • Provide experimental details (lipids, genes, measurement method).

Designing a Microsynthetic Methanogen

1. Function, Input, and Output

• Function: Acetoclastic Methanogenesis (converting acetate to methane).
• Input: Acetate (from pre-processed biomass slurry). (I thought of going through just puliverized biomass. But the engineering becomes too complex or even unviable.)
• Output: ($CH_4$) and Carbon Dioxide ($CO_2$).

2. Can this be realized by cell-free Tx/Tl alone?

• No. Methanogenesis requires a proton motive force ($\Delta p$) across a lipid bilayer. In open Tx/Tl systems, ions dissipate and energy cycles fail.

3. Can this be realized by a genetically modified natural cell?

• Yes, but inefficient. Natural methanogens are slow-growing, strictly anaerobic, and spend energy on survival. A synthetic cell directs all flux toward gas production.

4. Desired Outcome

• A stable “biocatalytic bead” added to anaerobic digesters to accelerate acetate-to-methane conversion, bypassing microbial growth limitations.

5. Component Design & System Architecture

• Membrane

  • Composition: POPC (1‑Palmitoyl‑2‑Oleoyl‑sn‑Glycero‑3‑Phosphocholine) + DPPC (1,2‑Dipalmitoyl‑sn‑Glycero‑3‑Phosphocholine) hybrid.
  • Rationale: Semi-permeable bilayer mimicking archaeal stability, compatible with bacterial Tx/Tl.

• Tx/Tl Source

  • Source: E. coli (S30 Extract).
  • Rationale: Robust protein synthesis; archaeal genes translated efficiently with optimized RBS.

• Encapsulated Cargo

  • Machinery: Ribosomes, T7 RNA Polymerase, Amino Acids, NTPs.
  • Small Molecules: Coenzyme M (HS-CoM), Coenzyme B (HS-CoB) pre-loaded for methane production.

• Communication (Permeability)

  • Substrate Entry: Acetate transporter AatP.
  • Product Exit: Methane ($CH_4$) diffuses naturally through the bilayer.

6. Experimental Details

7. Measuring Function

• Gas Chromatography (GC): Quantify methane volume and purity from headspace.
• pH Fluorescent Probes: Encapsulate Pyranine (HPTS) to detect proton movement across the membrane.
• Radioactive Labeling ($^{14}C$): Track conversion of $^{14}C$-acetate into labeled methane for definitive proof.

Peter Nguyen’s Question

• Freeze-dried cell-free systems can be incorporated into materials. Choose one field (Architecture, Textiles/Fashion, or Robotics) and propose an application:
  1. Write a one-sentence pitch:
  A “living” Martian masonry system composed of regolith bricks held together by mycellium, embedded with freeze-dried, cell-free biosensors that detect structural micro-fractures and signal repair needs via bioluminescence before catastrophic failure occurs.

  2. Explain how the idea works in detail: The core of this application is the integration of freeze-dried cell-free (FD-CF) machinery into the binder of Martian 3D-printed regolith. During the manufacturing of these mycellium-based bricks, a stabilized E. coli lysate containing the genetic instructoins for Luciferase is mixed with a porous substrate. These components remain dormant in the hyper-arid, cold Martian environment. If a structural micro-crack forms, it allows a small amount of pressurized “habitat air” (containing water vapor and localized heat) to reach the FD-CF pocket. This moisture acts as the trigger, rehydrating the system. Upon activation, the machinery translates the bioluminescent protein, causing the crack to glow brightly against the dark Martian regolith, acting as an autonomous, self-powered alarm system for astronauts.

  4. Identify the societal challenge or market need addressed. The primary challenge in Martian architecture is human safety in extreme environments. Unlike Earth, a hairline crack on Mars can lead to explosive decompression or lethal radiation exposure. Current electronic sensors require extensive wiring, constant power, and are prone to radiation interference. There is a need for passive, zero-energy monitoring systems that are lightweight to transport. By using cell-free systems, we eliminate the need to keep biological organisms alive during the 7-month space transit, providing a “just-expose-to-moisture” safety net for the first Martian colonies.

  5. Discuss how you would overcome limitations of cell-free reactions (activation, stability, one-time use).

• Activation Control To prevent accidental activation from ambient habitat humidity, the FD-CF components are encapsulated in hygroscopic wax microspheres. These spheres only melt or dissolve when exposed to the specific temperature and moisture profile of a localized structural leak, ensuring the “bio-sensor” only fires when a true breach occurs.

• Stability and Longevity Space radiation is the biggest threat to biological molecules. We will try to address this by incorporating lyoprotectants (like trehalose) and polyphenolic antioxidants into the freeze-drying mix. Whether these can stabilize the protein machinery and DNA templates in a “glassy state,” allowing them to remain viable for years in the Martian crust without denaturing, must be validated experimentally.

• One-Time Use to Repeatable Use While a single cell-free reaction is typically “one-shot,” we can think of designing the material with modular “casings.” Each brick contains hundreds of isolated micro-pockets of extract. If one pocket is used to signal a crack and the crack is then patched, the surrounding unused pockets remain dormant. This “redundancy-by-design” ensures the material provides monitoring capabilities throughout the lifespan of the building, despite the one-time-use nature of each individual biochemical reaction. However, we can expect the cost to be very high, and must be adressed suitably.

Ally Huang’s Question

• Develop a mock Genes in Space proposal using BioBits® (and optionally miniPCR® and P51 viewer):

  1. Background information (≤100 words):
     Monitoring structural integrity in space-grown “myco-architecture” is vital for long-duration missions. While mycelium-regolith composites are promising, they face structural stress from internal pressurization and radiation. BioBits®—a freeze-dried cell-free (FD-CF) system—enables biological sensing without the logistical burden of keeping cells alive. This experiment, designed for the ISS, seeks to validate whether FD-CF machinery, embedded in a fungal-mineral matrix, can undergo autonomous rehydration and protein synthesis in microgravity. Proving this confirms the feasibility of using “living” bricks that glow to warn astronauts of pressure loss or radiation spikes.

  2. Molecular/genetic target (≤30 words):
     A DNA plasmid encoding T7 RNA Polymerase and the mCherry fluorescent protein under a T7 promoter, optimized for detection via the P51 viewer and miniPCR® validation.

  3. Relation to space biology challenge (≤100 words):
     The primary challenge is the stability of biological hardware in a high-radiation, microgravity environment. Traditional sensors rely on electronics that are heavy and sensitive to galactic cosmic rays. This project tests if BioBits® can survive the “launch-to-activation” timeline while embedded in a porous, fungal-regolith matrix. Validating this on the ISS addresses the need for low-mass, zero-power safety systems. It also explores how microgravity affects the diffusion of rehydrating fluids within the unique capillary structures of desiccated mycelium, a critical factor for sensor response time in orbit.

  4. Hypothesis/research goal with reasoning (≤150 words):
     Hypothesis: Microgravity will not significantly inhibit the rehydration-induced activation of BioBits® within a mycelium-regolith matrix, and the fungal chitin will provide a protective micro-environment against ISS-level ionizing radiation.

Reasoning: In microgravity, fluid dynamics are dominated by surface tension rather than gravity-driven flow. We can hypothesize that the natural porosity of the mycelium will facilitate uniform rehydration of the FD-CF pellets via capillary action. Annd also, the molecular density of the regolith and the melanin content in the fungal cell walls should shield the DNA and ribosomes from radiation damage during their “dormant” phase. The goal will be to compare the fluorescence kinetics (speed and brightness) of the space-activated samples against Earth-based controls to determine if the lack of convection in microgravity slows down the metabolic-like reaction of the cell-free system.

5. Experimental plan (samples, controls, data collection, ≤100 words): Samples: Three mycelium-regolith cubes containing embedded BioBits® pellets and the mCherry DNA circuit.

Controls: One “Dry” brick (unactivated) and one “Wet” brick (Earth-activated) as baselines.

Execution: Use a sealed MWA (Maintenance Work Area) to inject 100 μL of rehydration buffer into the bricks via syringe to simulate a localized atmospheric leak.

Data Collection: Cubes are placed into the P51 viewer; astronauts take time-lapse photos to track fluorescence development. Finally, miniPCR® will amplify the mCherry gene from the “Dry” brick to assess DNA degradation during the flight.

Part B: Individual Final Project

1. Decide and write down Aim 1 of your final project.
   Answer:

2. Add your chosen final project slide to the appropriate deck.
   (Attach or link slide here)

3. Submit the Final Project selection form if not already done.
   (Confirmation note here)

4. Begin planning documentation based on provided guidelines.
   (Notes here)

5. Prepare your first DNA order and place it in the correct Twist tab (deadline varies by group).
   (Details here)

week-10-hw-Imaging-and-Measurement

Homework: Final Project

1. Identify at least one aspect of your project that you will measure.
Answer:

1. The expression level of the L lactate dehydrogenase Gene
2. The concentration of lactic acid

2. Describe all the elements you would like to measure.
Answer: Lorem ipsum dolor sit amet.

3. What technologies will you use (e.g., gel electrophoresis, DNA sequencing, mass spectrometry)?
Answer: Lorem ipsum dolor sit amet.

Homework: Waters Part 1 — Molecular Weight

1. Based on the predicted amino acid sequence of eGFP, what is the calculated molecular weight?
Answer: 27052.56 kda (after reomving the H tag)

2. Select two charge states from the BioAccord data and determine z for each.
Answer: Lorem ipsum dolor sit amet.

3. Calculate the MW of the protein using the relationship between m/z, MW, and z.
Answer: Lorem ipsum dolor sit amet.

4. Calculate the mass accuracy of the measurement.
Answer: Lorem ipsum dolor sit amet.

Homework: Waters Part 2 — Peptide Map

1. How many Lysines (K) and Arginines (R) are in eGFP?
Answer: 20 and 6

2. How many peptides will be generated from tryptic digestion of eGFP?
Answer: 19

3. Based on LC-MS data, how many chromatographic peaks do you see between 0.5 and 6 minutes?
Answer: 19

4. Does the number of peaks match the number of peptides predicted?
Answer: Yes

5. Identify the m/z of the peptide shown in Figure 3b.
Answer: Lorem ipsum dolor sit amet.

6. What is the charge (z) of the most abundant charge state of the peptide?
Answer: Lorem ipsum dolor sit amet.

7. Calculate the mass of the singly charged form of the peptide ([M+H]+).
Answer: Lorem ipsum dolor sit amet.

8. Identify the peptide based on MS/MS fragmentation spectrum.
Answer: Lorem ipsum dolor sit amet.

9. What is the mass accuracy of measurement?
Answer: Lorem ipsum dolor sit amet.

10. What percentage of the sequence is confirmed by peptide mapping?
Answer: Lorem ipsum dolor sit amet.

Bonus Questions
1. Determine the peptide sequence for the fragmentation spectrum in Figure 4.
Answer: Lorem ipsum dolor sit amet.

2. Do the peptide map data make sense and confirm the protein is eGFP? Why or why not?
Answer: Lorem ipsum dolor sit amet.

Homework: Waters Part 3 — Secondary/Tertiary Structure

1. Explain the difference between native and denatured protein conformations.
Answer: Lorem ipsum dolor sit amet.

2. What changes do you see in the mass spectrum between native and denatured protein analyses?
Answer: Lorem ipsum dolor sit amet.

3. What is the charge state of the peak at ~2800 m/z in the native spectrum?
Answer: Lorem ipsum dolor sit amet.

4. Did I make GFP? Fill out the table with theoretical vs observed data.
Answer: Lorem ipsum dolor sit amet.

Week 1 Lab: Pipetting

Group Final Project

Group Name: Phage Forge

Group Members:

@2026a-nourelden-rihan, @2026a-ritika-saha, @2026a-rahul-yaji, and @2026a-keerthana-gunaretnam

Individual Final Project

Carbon Forge Red: Engineering a Photoautotrophic System for the Conversion of CO₂ into L-Lactic Acid as a Raw Material for Poly Lactic Acid on Mars.

HTGAA 2026: Individual Final Project Documentation

SECTION 1: ABSTRACT

1. Abstract: Sustainable Mars settlement requires In-Situ Resource Utilization (ISRU) to reduce dependence on Earth-based supply chains. This project addresses the critical need for manufacturing materials on Mars by engineering a biological system to convert atmospheric $CO_2$ into Polylactic Acid (PLA), a versatile bioplastic for 3D printing. The broad objective is to create a photoautotrophic platform using Chlorella vulgaris for carbon fixation and polymer precursor production. We hypothesize that by redirecting metabolic flux from pyruvate to lactate via the introduction of $L$-lactate dehydrogenase ($Lldh$) and pyruvate kinase ($pk$), while knocking down phosphoenolpyruvate carboxylase ($ppc$), significant yields of $L$-lactic acid can be achieved. Specific aims include genetically modifying the algae, validating lactate accumulation, and refining extraction protocols. Methods involve CRISPR-based metabolic engineering, cell lysis, and chromatography for purification, followed by chemical polymerization. This system bridges the gap in Martian ISRU by providing a renewable source for construction and tool fabrication.

SECTION 2: PROJECT AIMS

Define three aims for your final project (minimum one sentence per aim).

1. Aim 1: Experimental Aim
   The first aim of my final project is to engineer Chlorella vulgaris to produce $L$-lactic acid from $CO_2$ by utilizing CRISPR-Cas9 gene editing to introduce $L$-lactate dehydrogenase ($Lldh$) and pyruvate kinase ($pk$) genes while knocking down the phosphoenolpyruvate carboxylase ($ppc$) gene. I will use Benchling for genetic circuit design and codon optimization, Addgene plasmids for the CRISPR backbone, and Asimov Kernel for metabolic modeling. The experimental workflow involves algal transformation, selection via antibiotic resistance, and verification of lactate secretion using high-performance liquid chromatography (HPLC).

2. Aim 2: Development Aim
   The second aim is to scale the biological production into a functional manufacturing pipeline by optimizing the downstream purification and polymerization of $L$-lactic acid into 3D-printable Poly Lactic Acid (PLA) filament. Following a successful Aim 1, this phase involves developing efficient cell lysis protocols, utilizing ion-exchange chromatography for high-purity lactic acid recovery, and performing ring-opening polymerization to create a resin suitable for extrusion into 3D printing filaments.

3. Aim 3: Visionary Aim
   The long-term vision for this project is to establish a self-sustaining In-Situ Resource Utilization (ISRU) framework for Mars settlement, where atmospheric carbon is converted into essential structural materials without Earth-based feedstock. By validating these experiments under simulated Martian atmospheric conditions, this project aims to address the major barrier of high-mass transport costs in space exploration, enabling a new paradigm of “biological manufacturing” where settlers can grow their own tools, spare parts, and habitats from the air they cannot breathe.

SECTION 3: BACKGROUND

Background and Literature Context

Provide background research that explains the current state of knowledge and identifies the gap your project addresses.

1. Briefly summarize two peer-reviewed research citations relevant to your research (minimum four sentences).
2. Explain how your project is novel or innovative (minimum three sentences).
   Examples:
   • New applications or uses of existing biological tools or concepts
   • Development of new approaches, methodologies, or technologies
   • Ways the project challenges existing paradigms or assumptions
   • How the work expands the boundaries of synthetic biology
3. Explain why your project matters and what impact it could have (minimum five sentences).
   Examples:
   • The problem addressed
   • Importance of the problem
   • Broader societal contribution
   • Advancement of knowledge or capability
   • Field-level change
4. Describe the ethical implications associated with your project and identify relevant ethical principles (minimum two paragraphs).
   • Paragraph 1: What ethical implications are involved?
   • Paragraph 2: What measures should be taken to ensure ethical conduct and societal responsibility?

SECTION 4: EXPERIMENTAL DESIGN, TECHNIQUES, TOOLS, AND TECHNOLOGY

Create a detailed experimental plan for your final project. Include a timeline for each part (minimum 15 lines/sentences).

• Include specific methods, tools, and technologies for each part of the project and analysis.
• Describe expected results for each experiment.
• Include figures if possible to show workflows.
• Reminder: All HTGAA projects must include some DNA design!

Techniques Checklist

☐ Pipetting
☐ Lab Safety
☐ Bioproduction
☐ Registry of Standard Biological Parts
☐ Chassis Selection (e.g., DH5alpha)
☒ Bioethical Considerations
☐ Plasmid Preparation
☐ Bacterial Culturing
☐ Quality Control/Analysis
☐ Bacterial Processing (Centrifugation, Lysis, DNA Purification)
☐ DNA Construct Design
☐ Restriction Enzyme Digestion
☐ Gel Electrophoresis
☐ DNA Purification From Gel
☐ Cell-Free Systems
☐ Freeze-Dried Cell-Free Systems
☐ Databases (GenBank, NCBI, Ensembl, UCSC Genome Browser)
☐ miniPCR Tools
☐ Protein Purification

Lab Automation

☐ Creating Code for Laboratory Automation
☐ Using Liquid Handling Robots (e.g., Opentrons)
☐ Designing a Twist Order
☐ Creating a plan to use the Autonomous Lab at Ginkgo Bioworks

CRISPR

☐ CRISPR/Cas9
☐ Designing Prime Editing gRNA

Protein Design

☐ Protein Design
☐ Use of Boltz or PepMLM
☐ Use of Asimov Kernel
☐ Use of Benchling
☐ Models and Notebooks
☐ Databases

1. Expand upon two techniques you checked above (minimum four sentences).
2. Identify any HTGAA Industry Council companies associated with your project (optional):
   • Addgene
   • Epibone
   • Ginkgo Bioworks
   • Helix Nano
   • Millipore Sigma
   • BioFabricate
   • Biome Consortia
   • Bolt
   • Boltz.bio
   • Cultivarium
   • DeepCure
   • Mycoworks
   • New England Biolabs
   • Opentrons
   • SecureDNA
   • Takeda Pharmaceuticals
   • Thermo Fisher Scientific
   • Transfyr.ai
   • Twist Biosciences
   • Upside Foods
   • Waters Corporation

SECTION 5: RESULTS & QUANTITATIVE EXPECTATIONS

You are required to validate at least one aspect of your final project aims.

Acceptable validations include:

• Designing DNA relevant to your project
• Performing PCR or Gibson assembly
• Creating and performing a cell-free assay
• Running code or computational analysis
• Designing and testing DNA constructs via Twist

1. What aspect of your project did you choose to validate?
2. Write a detailed protocol of how you validated it.
3. What synthetic biology techniques did you use?
4. Present data and analysis (experimental or simulated).
5. Describe challenges, limitations, and alternative strategies.

SECTION 6: ADDITIONAL INFORMATION

• List all references cited (bullet list).
• Create a supply list and budget for your project (bullet list).
  • Supplies, equipment, and budget needed.