Week 1 HW: Principles and Practices

Week 1 Biological Engineering application: governance, policy;

Abstract: Raising the same crop species over large areas and long time periods increases the risk of disease and pest outbursts, limits biodiversity and requires huge amounts of amendments and fertilizers. By genetically modifying the already socially approved off and profitable maize specie, global warming can be slowed down.

Context: Maize, also known as corn is a tall stout grass that produces cereal grain. As 2020, world production measured about 594 million tons grain from about 139 million ha. Its economic and cultural impact, now being the most cultivated crop globally, is undeniably one of the biggest driving factors of the human consumption companies and their effect on Earth’s wellbeing. Maize’s usage ranges from direct and indirect (processed victuals) human consumption, livestock feed and fabrication of biomaterial- monocultures fulfil the following criteria: they are large, focus on a single product (thus being easily farmed and harvested), and cater to distant markets.

Corn production, 1961 to 2023:

Important problems posing maize monocultures include the susceptibility to pathogens: bacteria such as Bacillus thuringiensis, viruses; insects- caterpillars, wireworms (forcing farmers to spend huge amounts of money to buy and use insecticides, in hope of avoiding a pest disaster; also harming the environment by doing so); and diseases originating from the lack of specific nutrients, in turn, needing regular fertilizers that may not be fully absorbed by the plants and making their way into running waters, groundwater and poisoning them, the soil and worms that could have served as soil fertilizers themselves and aerate the soil for better root growth, as well as food for birds useful to crops. Addressing the problem of common corn pests, profit seeking company, Bayer Global made accessible today’s most grown type of corn- a GMO, capable of withstanding B. thuringiensis, claiming a patent for it, having farmers buy new seeds every year. Even so, a problem arises: once the prokaryote evolves and learns how to parasite the so-used GMO, it’s over. 

*Today’s Musa acuminata’s “Gros Michel” cousin- the Cavendish banana- grown specifically for its tolerance against Fusarium oxysporum fungi (the pathogen responsible for Gros Michel’s decline) seems to face an evolved version of the same fungi, possibly causing a massive shortage in banana production in the next 5 years. With this in mind, it isn’t impossible to assume that todays’ most grown crop may have to face a dangerous, hard to stop pathogen. Corn monoculture can spam over hundreds of hectares, but they all share the same genetic material, the same DNA- so all the pathogen needs to do is “hack” a single corn plant, and it can spread to the next, until it takes over the whole plantation. So, farmers have to spend a lot of money on insecticides, fungicides and fertilizers, heavily harming the environment meanwhile. Monocultures may therefore also lead to soil exhaustion when the soil becomes depleted of nutrients.

Plant communication is one of the most debated topics in modern research of vegetal life, as its proper understanding may change everything we now know (or think we do) about how plants perceive their surroundings and how they respond to threats such as drought and pest attacks. claim that plants may fabricate and use special molecules, and ions to send chemical and electric signals to one another; be it interspecific- mutualism or intraspecific (own specie). Some plants rely on these volatile organic compounds, root exudates and phytohormones to recognize their own kin. And seem to show “altruistic” traits, not only alert neighboring plants of approaching pests or harm already done by bacteria on their self, but also to signal where specific mineral deposits are in the underground. A defiance to “survival of the fittest”, plants seem to prioritize the survival of their kin (their genes), instead of itself- sometimes, assuring enough sunlight also reaches plants of its species (as long as the individual has enough to grow with no problems). As interesting as this idea is, it has not yet been researched completely; even so, plant cooperation has been documented in corn plants as well.

Status Quo: Capitalism is presented as the main reason for the thirst for money, thus further encouraging environmentally harmful technological and agricultural processes. This is why climate change has failed to be eradicated, or at least slowed down considerably. As effects of global warming and the increase in average global temperature (+1.60 °C), I list the increase in the number of floods, extreme heat, increased food and water shortages, more diseases (and a longer and more difficult process of combating them) and massive economic losses- which will most likely cause more damage, due to the need for large companies to maintain themselves.

As for the European Union, a significant stigma overshadows all the possibilities for GMO development and financial gain; even so, legal and actually grown, dent corn (MON810) stands out for being not only consumed, but also grown hugely in Portugal and Spain. Due to lack of incentive to educate themselves and being prone to believe misinformation, especially when it comes to genetically modified organisms, people tend to fear and often reject them. *MON810 seems to be an exception. Public opinion of modified crops, in the USA, do not seem to be perfect, but is significantly better than in the EU.

Proposed engineering application: Genetically modified dent corn, not for higher yield or pest resistance, but for something unique. Practically the same plant, this modified version of MON810, an already g. modified, socially accepted and harvested, can serve as the diversity needed in monocultures, practically the same plant, it can still produce phytohormones used in plant communication and recognize the “traditional” GMO and not harm it, rather helping it by guiding it to underground nutrients it needs- all with the help of chemical signals. The genomic change proposed determines it to use, and so, seek, different nutrients than the original.

   As all plants, the now grown corn uses a specific ratio of minerals, macronutrients and micronutrients for its metabolic processes. Everything from the fruit’s nutritional proprieties to its leaves capability of photosynthesizing is decided by what enzymes, proteins and other molecules the plant DNA codes for. All life (and nonlife: viruses) make use simple molecules to build new ones, more complicated, plants are like green, autotrophic factories running nonstop. Plants differentiate nutrients when absorbing them from the soil through highly selective mechanisms, primarily using specific transporter proteins and ion channels embedded in their root cell membranes. Instead of simply absorbing everything in the soil, root hairs use energy to actively pick up essential mineral ions.

Macronutrient removal in forage maize:

plant macronutrients: N, P, K.

Micronutrient removal in forage maize:

With the help of genomic editing technology, the proposed corn plant may be designed to make different proteins and substances, thereby making use of different nutrients, the ones that the original maize doesn’t use. Assuming it behaves the same, so it’s recognized as the same species, what does the modified corn use them for?

• Make up the same, well-known corn, but nutritionally different; contain more phosphorus-based molecules, different ratios, even different vitamins all throughout. Possibly, make its molar weight different so it can be separated from original corn fruits by density.

• Make fruits for the sole reason of them being composed and used for plants needing more of the nutrients corn doesn’t use- be it as legume plants and phosphorous.

• Make the plant give up on producing fruit altogether and focus on chemical making phytohormones for improved senses and comprehension of surroundings for itself and the original, at the same time: insecticides/fungicides to ward away/ kill possible pests from the original corn plants, thus reducing the necessity for industrially-made harmful chemicals.

DNA “balance”: Hard to nail, but possible, close enough so the original corn recognizes the new specie as its own kin, but different enough so that pathogens have to double the effort, giving bio-friendly pesticides more time to act. Downsides: Cutting corn production to almost half. Endangering profit seeking companies, risk coups and legal loopholes to be used against developing the GMO. The GMO may not show willing to help the original corn, prioritize itself/ their “new” kin- kill, damage the original. Horizontal gene flow via winds or pollen carrying insects; proposed GM may not produce it.

Incentive to implement & superiority amongst alternatives: Lowers the risk of plantation eradication by single pathogen- farmers feel safer, more relieved. Can be planted, taken care of, and harvested the same as the plant famers are used to growing, no new gear, machines, procedures needed. Lowers the total costs, no insecticides, pesticides needed. Fertilizers usage not mandatory, less targeted substances used- soil pH, proprieties changed, deranged. More ecofriendly- large companies want the world to know that they’re willing to help fight global warming.

   Alternatives to monocultures do exist, but they’re harder to manage and ultimately, harvest: parallel rows of corn, thyme, rosemary, etc. Other approaches include alternative rows of different types of corn- most of the time they have the same density, in turn, making their separation really hard, so not economically advantageous and costumers do now want their corn produce diverse. 

Legislative analysis:

• Policy goals: Prevent harm or help deal with harm already done. Make proposed GMO as safe as possible; assure it manages to do the thing it was projected to do- reduce environmental impact of agriculture, especially maize cultures. Acting similar to modern companies, regulate seed commerce under patent. Insecticides produced by GMO must be biodegradable and harmless to plantation biome and close to it (including useful prokaryotes, such as chemoautotroph nitrogen-fixing bacteria, other weeds, insects, little mammals, etc.) Proposed corn interaction with traditional GM corn should be researched and be considered cooperative, not harmful- even with evolution interfering. Soil proprieties should be conserved (be not changed more than 98.0%) all throughout growing season, during harvest and after.

• Governance actions: Laboratory research and design: proteins/ genetic material made by GM maze should not leave assigned wet laboratory until declared safe by scientists. GM maze DNA should not interact with any bacteria/ virus until declared stable- laboratory, instruments sterile at all times. Assure no harm to humans by direct contact, breath, ingestion of less than 0.5L in time span under 24h. After health-related requirements met, proposed GM crop may be grown in regulated terrain. After satisfying results, GMO can be produced in larger quantities and sold under patent. Should correspond to EU/SUA/global/local legislation.

1. Requirement: Planting distance treatment- proposed GMO and original corn should be planted in alternating (1:1), parallel rows, without intersecting. Ideal distance suggested after research, tests and wet lab confirmation, (ex: each maize plant: 75 cm x 40 cm, strip cropping 40 cm x 20 cm). The purpose is assuring healthy distance between specimens as extreme closeness can trigger a more competitive approach by both sides. Seasonal check-ups can assure following of rule, deviation from which can lead to legal act- suing responsible farmer for not respecting agreed (by buying seeds under patent) on rule. Meeting the requirement can still result in fines under pretext of overwatering and movement of soil thus reducing distance between crops.

2. Requirement: usage of fertilizers- only the ones approved by developers of GM corn, according to research, tests and wet lab confirmation. Dose of fertilizers allowed to use should be regulated so it assures no significant soil/ water pollution or damage to local life. Regulate under fertilizer market propose so availability guaranteed- not expensive (tho, not necessary fertilizers/ amendments)- important actor. Hard to regulate and check with smaller farmers.

3. Act under patent- seeds cannot be harvested with the scope of germination and regrowing, GM corn should only be grown alongside traditional corn. Substances that can cause harm to life (animals/ plants/ fungi/ eukaryote/ prokaryotes), viruses, alter organic produce such as plant-bases compost, produce meant for human consumption approved by local/ national food and drug administration committee/ regulating legal body produced by proposed plant should not be collect- it should not be grown for this scope. Hard to regulate and check with smaller farmers. Biosecurity should not be put in hazard; copyright for GM crop should be assured. Overly burdensome requirements could discourage smaller research groups and concentrate development in large corporations.

• Scoring rubric:

  Does the option:	Option 1	Option 2	Option 3
  Enhance Biosecurity	n/a	n/a	2
  • By preventing incidents	n/a	n/a	1
  • By helping respond	n/a	n/a	2
  Foster Lab Safety	3	3	1
  • By preventing incident	2	2	1
  • By helping respond	3	3	2
  Protect the environment	1	1	2
  • By preventing incidents	1	1	2
  • By helping respond	2	n/a	2
  Other considerations
  • Minimizing costs and burdens to stakeholders	n/a	2	1
  • Feasibility?	1	3	2
  • Not impede research	2	2	n/a
  • Promote constructive applications	1	n/a	n/a

• Prioritizing: Act under patent as its deviance can cause direct biological, chemical damage to human organs. Consumer and farmer health above all. Ethical concerns: Monopole and distancing from main scope- lower global warming.

By original/ traditional corn I mean: GM dent corn MON810; by GM corn/proposed GMO I mean the genetically edited plant described to be dependent of different nutrients than MON810.

Bibliography/ sources: (in no particular order)

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. The Light Eaters: How the Unseen World of Plant Intelligence Offers a New Understanding of Life on Earth- Zoe Schlanger (with citations mentioned in book)

HW, part. 2:

Week 2 lecture prep

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?

   The error rate of polymerase is presented to be 1:10⁶- not only is it accurate, rarely changing bases. Also making use of an error correcting mechanism, assuring extremely low chances of wrong doing. The human genome contains approximately 3 billion base pairs (3x10⁹), meaning that ~3000 base pairs get screwed up, making their gene prone to being unable to be expressed correctly or at all. With some of them being proofread, thus corrected, and considering ~30% of human DNA is made up by introns, every cell also having about 99% DNA “dormant” (unused), noticeable problems rarely appear. Sizing up to millions of cells, problems do appear- cancer, spontaneous hazardous genetic mutations, etc. The human body evolved to deal with these problems, biology itself, deals with this discrepancy via proofreading (when an incorrect base gets detected, the polymerase enzyme makes a cut in the chemical bonds, releasing incorrect nucleotide). Also, cells can resort to apoptosis if genetic mutation cause messing up in chemical ratios- as seen in cancer prone cells. And finally, evolution gets driven by genetic drift that can be caused by polymerase’s “wrongdoing”.

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?

   The average human protein has around 1036bp, those coding for about 330 amino acids; On average, there are about 3 codons per amino acid, the number of different DNA sequences that can encode for the same average human protein is roughly 10¹⁵⁷. Why don’t all these sequences work in practice? Coding for a protein doesn’t mean expression is sure to work in a cell. 1.Codon bias: different organisms prefer different codons (for the same protein), rare codons are usually translated too slow to be efficient. 2. DNA sequence determines mRNA folding resulting in poor translation and premature termination due to slow ribosome movement.

Dr. LeProust

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

The solid-phase phosphoramidite chemical synthesis is the most used technology used today for making custom DNA oligonucleotides.

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

Because of accumulating stepwise errors and yield drop; side reactions and chemical limitations. Each addition cycle in phosphoramidite syn. is ~99% efficient at best, at over 200 bases, the probability of having a correct product drops to max 14%. In traditional porous supports, the growing long chain can block reagent access, further lowering efficiency.

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

Based on previous answer, accumulating errors make a 2000 base pair gene (4000 nucleotides) almost impossible to synthesize. It is just not feasible.

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

   The 10 essential amino acids are histidine- H, leucine- L, lysine- K, methionine- M, phenylalanine- F, threonine- T, tryptophan- W, valine- V, and arginine- R.  
   
   *The Lysine Contingency was a genetic alteration Henry Wu performed in the dinosaur genome. The modification knocked out the ability of the dinosaurs to produce the amino acid Lysine. This forced the dinosaurs to depend on lysine supplements provided by the park's veterinary staff.* It makes no sense since vertebrates do not synthesize these amino acids anyhow, they get it from their surroundings and later use it in protein synthesis, no apparent change is made by making the dinosaurs “dependent” on the park’s plants. 

Cited 22., 23..

Week 2 HW: DNA Read, Write & Edit

Week 2 Gel art, DNA processing;

  Abstract: The DNA molecule is an "information-packed" polymer, it encodes for proteins' monomer- amino acids. Even more so, DNA is electronegatively charged and so, using electrophoresis, DNA fragments' migration to the positively charged part of container can be controlled to constitute "gel art".

I:

I used rcdonovan to iterate a design (after unselecting Ndel, PvulI and Xhol enzymes), that I could later modify to my liking. I used ↑and ↓ to change specific ladders.

2. I made an account on benchling, after importing the lambda DNA, I selected “digests” to make a total of 8 different ladders. I begun playing around with restriction enzymes. After which, I ended up with the following:

Ladder

1. EcoRI, EcoRV, HindIII;
2. KpnI;
3. BamHI, HindIII;
4. EcoRI;
5. BamHI;
6. EcoRi, SalI;
7. BamHI;
8. EcoRI, SalI;
9. HindIII;

The gel art is supposed to resemble the letter “m”.

benchling- gel art

II:

With no access to a lab, I studied the protocol and took notes.

III:

3.1. I choose the beta hemoglobin subunit, constituent part of hemoglobin heterotetramer pigment, responsible for O2 transport in the human body. Hemoglobin is composed of four groups, 2 Alpha respectively 2 Beta, each having an iron-containing heme group (4 in total). The reason for my choice is that I find hemoglobin and its copper-containing homologue interesting.

sp|P68871|HBB_HUMAN Hemoglobin subunit beta OS=Homo sapiens OX=9606 GN=HBB PE=1 SV=2

Next, I used uniprot to find HBB, homo sapiens, protein amino acid sequence:
uniprot-p68871

a.a. HBB seq:

MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

3.2. Using Cusabio, I reverse translated the a.a. seq. into nucleotide seq.: reverse tranlation

nucleotide HBB seq:

ATGATGGTTCATTTAACTCCTGAAGAAAAATCTGCTGTTACTGCTTTATGGGGTAAAGTTAATGTTGATGAAGTTGGTGGTGAAGCTTTAGGTCGTTTATTAGTTGTTTATCCTTGGACTCAACGTTTTTTTGAATCTTTTGGTGATTTATCTACTCCTGATGCTGTTATGGGTAATCCTAAAGTTAAAGCTCATGGTAAAAAAGTTTTAGGTGCTTTTTCTGATGGTTTAGCTCATTTAGATAATTTAAAAGGTACTTTTGCTACTTTATCTGAATTACATTGTGATAAATTACATGTTGATCCTGAAAATTTTCGTTTATTAGGTAATGTTTTAGTTTGTGTTTTAGCTCATCATTTTGGTAAAGAATTTACTCCTCCTGTTCAAGCTGCTTATCAAAAAGTTGTTGCTGGTGTTGCTAATGCTTTAGCTCATAAATATCATTAA

3.3. Because of its common usage as a vector “acceptor”, I chose to optimize the DNA sequence for Escherichia coli. I used Benchling’s option for optimization and obtained:

E. coli optimized:

ATGATGGTGCATCTGACGCCGGAAGAAAAAAGTGCGGTGACCGCACTGTGGGGCAAGGTAAACGTCGATGAAGTCGGGGGCGAGGCTCTCGGGCGCCTGTTGGTGGTGTATCCCTGGACTCAACGGTTTTTTGAGAGCTTCGGAGACCTTTCTACCCCGGACGCGGTAATGGGAAACCCGAAAGTCAAAGCACACGGTAAGAAAGTGCTGGGCGCGTTTTCCGATGGCTTGGCTCATCTCGATAACCTGAAAGGTACGTTTGCCACCCTGTCGGAACTGCATTGTGATAAACTGCACGTTGACCCTGAAAATTTCCGTTTACTGGGCAATGTGCTTGTTTGCGTTTTAGCTCACCACTTTGGTAAAGAGTTCACACCACCGGTTCAGGCGGCCTACCAGAAGGTAGTTGCAGGTGTCGCCAATGCCCTAGCGCATAAATATCATTAA

• Also, its amino acid sequence is:

MMVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

benchling-optimized seq

3.4. For the DNA to successfully be transcribed and translated into my chosen protein, I could encode it into a plasmid, that I can insert into a bacteria such as E. Coli to naturally transcribe it using mRNA and later translate that RNA, using ribosomes, into the beta hemoglobin subunit. For increased efficiency I can use the optimized sequence to make up the vector.

3.5. A single gene can produce multiple proteins trough alternative processing of RNA transcript- alternative splicing: eukaryote DNA contains introns and exons, during splicing the exons are joined together while the introns get cut out. There is a chance for an exon to be cut out instead, resulting in a different sequence => different amino acids => completely different protein, or for an intron to be skipped and kept in the “mature” mRNA.

The same amino acid can be coded for by different codons (ex: ACG, ACA, ACT, ACC => Thr).

• T -> U

VI:

4.1. I set up my accounts.

4.2. I followed the instructions gave on HTGAA site.

Optimized+ necessary exons:

TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGCCATTAAAGAGGAGAAAGGTACCATGATGGTGCATCTGACGCCGGAAGAAAAAAGTGCGGTGACCGCACTGTGGGGCAAGGTAAACGTCGATGAAGTCGGGGGCGAGGCTCTCGGGCGCCTGTTGGTGGTGTATCCCTGGACTCAACGGTTTTTTGAGAGCTTCGGAGACCTTTCTACCCCGGACGCGGTAATGGGAAACCCGAAAGTCAAAGCACACGGTAAGAAAGTGCTGGGCGCGTTTTCCGATGGCTTGGCTCATCTCGATAACCTGAAAGGTACGTTTGCCACCCTGTCGGAACTGCATTGTGATAAACTGCACGTTGACCCTGAAAATTTCCGTTTACTGGGCAATGTGCTTGTTTGCGTTTTAGCTCACCACTTTGGTAAAGAGTTCACACCACCGGTTCAGGCGGCCTACCAGAAGGTAGTTGCAGGTGTCGCCAATGCCCTAGCGCATAAATATCATTAACATCACCATCACCATCATCACTAACCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

benchling-complete seq

I downloaded the sequence as a FASTA file;

4.3.& 4.4.& 4.5. Using Twist, I managed to import my sequence, transform it into pTwist Amp High copy vector. Downloaded it as GenBank construct and imported it to benchling. twist plasmid

V:

5.1. My desired DNA to sequence is human reticular connective tissues’. As it might hide the pathway to synthesizing type 0 negative human blood in labs all around the world, making human blood donations unnecessary, furthermore, its DNA could play an important role in bone marrow synthesis.

tech: I would use Illumina sequencing-by-synthesis because of its high accuracy and high throughput (whole genome possible). I identified it to be second generation seq. tech. The input is the extracted genomic DNA from reticular connective tissue, the steps I’d follow are:

a) extraction, purification- get the DNA separated from fats, sugars, proteins and any other contaminates;

b) fragmentation into ~225 bp parts via enzymes; followed by end repair

c) adapter ligation- attached synthetic DNA sequences to both ends of each reticular DNA fragment for easier reading;

d) run genetic material through PCR machine for higher quality DNA, avoid nucleotide pools and get more material so I have more room for error.

NGS- second generation sequencing by synthesis technology

• Works by attaching a fluorescent dye to each nucleotide via polymerase enzyme and uploading information to a 4 color image of chip after each chemical flow and identify sequence after.

• The primary output is a DNA nucleotide sequence.

  5.2. The DNA sequence/ material I would synthesize is the phytogene found in coffee plants responsible for caffeine synthesis from xanthosine. It is not more efficient or cheaper than modern harvesting methods of caffeine or chemical lab synthesis, but it does have the advantage of less intermediary steps and purer resultants Caffeine can be used in medicine as treatment for sleep apnea, high yield purity is needed. In vector: synthesized DNA, attached to a Promoter, 7x His Tag, DNA seq to synthesize some chemical to incentivise bacteria to keep plasmid (e.q. gene made protein to make cell immune to antibiotic found in substrate), proposed coffee plant DNA, terminator. In substrate: xanthine+ ribofuranose => xanthosine, selected antibiotic.

tech: Twist to code for and buy plasmid. Later injected and accepted by bacteria (ex: E. Coli).

plant metabolic pathway of creating “natural” caffeine

5.3. The DNA I would edit would be found in bacteriophages. As virus’ genomes are the shortest; and because of their capability to reproduce fast, I could engineer virus DNA to fight against/ kill TB prokaryote. In third world countries, TB treatment is available, but not present enough to actually keep all population safe. An inoffensive virus spreading trough population only targeting TB could solve this century old problem.

tech: DRAG & DROP genome insertion mechanism- so that I’m not required to modify both strands of DNA and the whole genome, in turn.

Week 3 HW: Lab automation

Week 3 Lab automation: Opentrons artwork;

  Abstract: Laboratory experiments and follow of protocols can lead to errors, thus in wrong assumptions. Automating lab work not only saves precious time, but also ensures quality of information delivered.

I:

1. I used rcdonovan’s site to iterate a design. Instead of manually creating the image I chose to import it from google and then make as many changes as I desired. Shown image is Aphex Twin’s, a famous British musician (“pioneering figure”), logo. Image copied from wikipedia.

used colors: mkate2; electra2; mturquoise2.

2. After a really long time, I managed to make the google colab copy code run and output Aphex Twin’s logo as theoretical Opentrons hardware operation on petri dish. opentron.google-colab

3. A.I. usage was involved in my multiple attempts to understand essential code found in Opentrons Colab, and to later verify and fix code impediments that stopped it from running.

I submitted my code to Google Gemini, including shown error code and asked for explanations to why it wasn’t working properly and modified version of code. (the main problem I had to overcome was using the color library:

To fix the error, we need to synchronize the naming. The visualization tool (Matplotlib) doesn’t recognize protein names like “Electra2,” so we must use standard color names (like “lime”, “cyan”, etc.) in both the well_colors mapping and the all_designs data keys. Here is the fully rewritten, working code. I have mapped each protein to its closest visual color. code Key Improvements Made: • Synchronized Dictionaries: The keys in all_designs now perfectly match the values in well_colors. This prevents the ValueError: No well found… error. • Matplotlib Compatibility: By using colors like “lime” and “crimson”, the protocol.visualize() function can now render the pattern without crashing. • Logic Preserved: The protocol still aspirates in $20 \mu L$ chunks and drops the tip between colors to prevent cross-contamination. Would you like me to help you split the “lime” protein coordinates across two wells to ensure the robot doesn’t run out of liquid?

II:

from opentrons import types

metadata = {
    'author': 'Casian Veselin',
    'protocolName': 'Aphex Twin logo ',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

# --- Robot Deck Setup ---
TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

# Mapping each protein set to a specific well on the 96-aluminum block
# Updated mapping using standard Matplotlib colors
well_colors = {
    'A1': '#0077ff',  # Electra2 (Blue)
    'A2': '#4b0082',  # mKate2 (Deep Purple)
    'C1': '#00ffff'   # mTurquoise2 (Cyan)
}

def run(protocol):
    # --- Load Labware ---
    tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')
    pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])

    temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)
    color_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul', 'Cold Plate')

    agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')
    center_location = agar_plate['A1'].top()
    pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

    # --- Data ---
    # Grouping all point sets into a dictionary for clean iteration
    all_designs = {
     '#4b0082': [(-9.9, 38.5),(-7.7, 38.5),(-5.5, 38.5),(-3.3, 38.5),(-1.1, 38.5),(1.1, 38.5),(3.3, 38.5),(5.5, 38.5),(7.7, 38.5),(9.9, 38.5),(-14.3, 36.3),(-12.1, 36.3),(-9.9, 36.3),(-7.7, 36.3),(-5.5, 36.3),(-3.3, 36.3),(-1.1, 36.3),(1.1, 36.3),(3.3, 36.3),(5.5, 36.3),(7.7, 36.3),(9.9, 36.3),(12.1, 36.3),(14.3, 36.3),(16.5, 36.3),(-16.5, 34.1),(-14.3, 34.1),(-12.1, 34.1),(-9.9, 34.1),(-7.7, 34.1),(-5.5, 34.1),(-3.3, 34.1),(-1.1, 34.1),(1.1, 34.1),(3.3, 34.1),(5.5, 34.1),(7.7, 34.1),(9.9, 34.1),(12.1, 34.1),(14.3, 34.1),(16.5, 34.1),(18.7, 34.1),(20.9, 34.1),(-20.9, 31.9),(-18.7, 31.9),(-16.5, 31.9),(16.5, 31.9),(18.7, 31.9),(20.9, 31.9),(23.1, 31.9),(-25.3, 29.7),(-23.1, 29.7),(-20.9, 29.7),(20.9, 29.7),(23.1, 29.7),(25.3, 29.7),(-27.5, 27.5),(-25.3, 27.5),(-23.1, 27.5),(23.1, 27.5),(25.3, 27.5),(27.5, 27.5),(-29.7, 25.3),(-27.5, 25.3),(-25.3, 25.3),(25.3, 25.3),(27.5, 25.3),(29.7, 25.3),(-31.9, 23.1),(-29.7, 23.1),(-27.5, 23.1),(27.5, 23.1),(29.7, 23.1),(31.9, 23.1),(-34.1, 20.9),(-31.9, 20.9),(-29.7, 20.9),(29.7, 20.9),(31.9, 20.9),(34.1, 20.9),(-34.1, 18.7),(-31.9, 18.7),(-25.3, 18.7),(-23.1, 18.7),(-20.9, 18.7),(-18.7, 18.7),(-16.5, 18.7),(-14.3, 18.7),(-12.1, 18.7),(-9.9, 18.7),(-7.7, 18.7),(-5.5, 18.7),(-3.3, 18.7),(-1.1, 18.7),(1.1, 18.7),(3.3, 18.7),(5.5, 18.7),(7.7, 18.7),(9.9, 18.7),(31.9, 18.7),(34.1, 18.7),(-34.1, 16.5),(-31.9, 16.5),(-27.5, 16.5),(-25.3, 16.5),(-23.1, 16.5),(-20.9, 16.5),(-18.7, 16.5),(-16.5, 16.5),(-14.3, 16.5),(-12.1, 16.5),(-9.9, 16.5),(-7.7, 16.5),(-5.5, 16.5),(-3.3, 16.5),(-1.1, 16.5),(1.1, 16.5),(3.3, 16.5),(5.5, 16.5),(7.7, 16.5),(9.9, 16.5),(31.9, 16.5),(34.1, 16.5),(36.3, 16.5),(-34.1, 14.3),(-20.9, 14.3),(-18.7, 14.3),(-16.5, 14.3),(-14.3, 14.3),(-12.1, 14.3),(-9.9, 14.3),(-3.3, 14.3),(-1.1, 14.3),(1.1, 14.3),(3.3, 14.3),(5.5, 14.3),(7.7, 14.3),(9.9, 14.3),(12.1, 14.3),(34.1, 14.3),(36.3, 14.3),(-34.1, 12.1),(-25.3, 12.1),(-16.5, 12.1),(-14.3, 12.1),(-12.1, 12.1),(-3.3, 12.1),(-1.1, 12.1),(1.1, 12.1),(3.3, 12.1),(5.5, 12.1),(7.7, 12.1),(9.9, 12.1),(12.1, 12.1),(34.1, 12.1),(36.3, 12.1),(-34.1, 9.9),(-5.5, 9.9),(-3.3, 9.9),(-1.1, 9.9),(1.1, 9.9),(3.3, 9.9),(5.5, 9.9),(7.7, 9.9),(9.9, 9.9),(12.1, 9.9),(14.3, 9.9),(34.1, 9.9),(36.3, 9.9),(38.5, 9.9),(-36.3, 7.7),(-34.1, 7.7),(-7.7, 7.7),(-5.5, 7.7),(-3.3, 7.7),(-1.1, 7.7),(1.1, 7.7),(3.3, 7.7),(5.5, 7.7),(7.7, 7.7),(9.9, 7.7),(12.1, 7.7),(14.3, 7.7),(34.1, 7.7),(36.3, 7.7),(38.5, 7.7),(-36.3, 5.5),(-34.1, 5.5),(-7.7, 5.5),(-5.5, 5.5),(-3.3, 5.5),(-1.1, 5.5),(5.5, 5.5),(7.7, 5.5),(9.9, 5.5),(12.1, 5.5),(14.3, 5.5),(16.5, 5.5),(34.1, 5.5),(36.3, 5.5),(38.5, 5.5),(-36.3, 3.3),(-34.1, 3.3),(-9.9, 3.3),(-7.7, 3.3),(-5.5, 3.3),(-3.3, 3.3),(-1.1, 3.3),(7.7, 3.3),(9.9, 3.3),(12.1, 3.3),(14.3, 3.3),(16.5, 3.3),(34.1, 3.3),(36.3, 3.3),(38.5, 3.3),(-36.3, 1.1),(-34.1, 1.1),(-9.9, 1.1),(-7.7, 1.1),(-5.5, 1.1),(-3.3, 1.1),(-1.1, 1.1),(9.9, 1.1),(12.1, 1.1),(14.3, 1.1),(16.5, 1.1),(18.7, 1.1),(34.1, 1.1),(36.3, 1.1),(38.5, 1.1),(-38.5, -1.1),(-36.3, -1.1),(-34.1, -1.1),(-9.9, -1.1),(-7.7, -1.1),(-5.5, -1.1),(-3.3, -1.1),(-1.1, -1.1),(9.9, -1.1),(12.1, -1.1),(14.3, -1.1),(16.5, -1.1),(18.7, -1.1),(34.1, -1.1),(36.3, -1.1),(38.5, -1.1),(-36.3, -3.3),(-34.1, -3.3),(-9.9, -3.3),(-7.7, -3.3),(-5.5, -3.3),(-3.3, -3.3),(-1.1, -3.3),(12.1, -3.3),(14.3, -3.3),(16.5, -3.3),(18.7, -3.3),(34.1, -3.3),(36.3, -3.3),(38.5, -3.3),(-36.3, -5.5),(-34.1, -5.5),(-12.1, -5.5),(-9.9, -5.5),(-7.7, -5.5),(12.1, -5.5),(14.3, -5.5),(16.5, -5.5),(18.7, -5.5),(20.9, -5.5),(34.1, -5.5),(36.3, -5.5),(38.5, -5.5),(-36.3, -7.7),(-34.1, -7.7),(-14.3, -7.7),(-12.1, -7.7),(-9.9, -7.7),(12.1, -7.7),(14.3, -7.7),(16.5, -7.7),(18.7, -7.7),(20.9, -7.7),(34.1, -7.7),(36.3, -7.7),(38.5, -7.7),(-36.3, -9.9),(-34.1, -9.9),(-14.3, -9.9),(-12.1, -9.9),(14.3, -9.9),(16.5, -9.9),(18.7, -9.9),(20.9, -9.9),(34.1, -9.9),(36.3, -9.9),(38.5, -9.9),(-36.3, -12.1),(-34.1, -12.1),(-16.5, -12.1),(-14.3, -12.1),(-12.1, -12.1),(16.5, -12.1),(18.7, -12.1),(20.9, -12.1),(23.1, -12.1),(34.1, -12.1),(36.3, -12.1),(-36.3, -14.3),(-34.1, -14.3),(18.7, -14.3),(20.9, -14.3),(23.1, -14.3),(25.3, -14.3),(34.1, -14.3),(36.3, -14.3),(-34.1, -16.5),(-31.9, -16.5),(18.7, -16.5),(20.9, -16.5),(23.1, -16.5),(25.3, -16.5),(31.9, -16.5),(34.1, -16.5),(36.3, -16.5),(-34.1, -18.7),(-31.9, -18.7),(20.9, -18.7),(23.1, -18.7),(25.3, -18.7),(31.9, -18.7),(34.1, -18.7),(-34.1, -20.9),(-31.9, -20.9),(-29.7, -20.9),(29.7, -20.9),(31.9, -20.9),(-31.9, -23.1),(-29.7, -23.1),(-27.5, -23.1),(27.5, -23.1),(29.7, -23.1),(-29.7, -25.3),(-27.5, -25.3),(-25.3, -25.3),(25.3, -25.3),(27.5, -25.3),(29.7, -25.3),(-27.5, -27.5),(-25.3, -27.5),(-23.1, -27.5),(23.1, 27.5),(25.3, -27.5),(-25.3, -29.7),(-23.1, -29.7),(-20.9, -29.7),(20.9, -29.7),(23.1, -29.7),(-23.1, -31.9),(-20.9, -31.9),(-18.7, -31.9),(-16.5, -31.9),(16.5, -31.9),(18.7, -31.9),(20.9, -31.9),(-20.9, -34.1),(-18.7, -34.1),(-16.5, -34.1),(-14.3, -34.1),(-12.1, -34.1),(-9.9, -34.1),(-7.7, -34.1),(-5.5, -34.1),(-3.3, -34.1),(-1.1, -34.1),(1.1, -34.1),(3.3, -34.1),(5.5, -34.1),(7.7, -34.1),(9.9, -34.1),(12.1, -34.1),(14.3, -34.1),(16.5, -34.1),(18.7, -34.1),(20.9, -34.1),(-9.9, -36.3),(-7.7, -36.3),(-5.5, -36.3),(-3.3, -36.3),(-1.1, -36.3),(1.1, -36.3),(3.3, -36.3),(5.5, -36.3),(7.7, -36.3),(9.9, -36.3),(12.1, -36.3),(14.3, -36.3),(16.5, -36.3),(-9.9, -38.5),(-7.7, -38.5),(-5.5, -38.5),(7.7, -38.5)],
     '#0077ff': [(-16.5, 36.3),(-20.9, 34.1),(-23.1, 31.9),(-36.3, 16.5),(-36.3, 14.3),(-27.5, 14.3),(-25.3, 14.3),(-36.3, 12.1),(-23.1, 12.1),(-20.9, 12.1),(-18.7, 12.1),(-38.5, 9.9),(-38.5, 7.7),(-38.5, 5.5),(-38.5, 3.3),(-38.5, -3.3),(-38.5, -5.5),(-38.5, -7.7),(-38.5, -9.9),(-36.3, -16.5),(34.1, -20.9),(31.9, -23.1),(27.5, -27.5),(25.3, -29.7),(23.1, -31.9),(-16.5, -36.3),(-14.3, -36.3),(-12.1, -36.3),(-3.3, -38.5),(-1.1, -38.5),(1.1, -38.5),(3.3, -38.5),(5.5, -38.5),(9.9, -38.5)],
     '#00ffff': [(-18.7, 34.1),(-23.1, 14.3),(-36.3, 9.9),(-38.5, 1.1)]
    }

    # --- Helper Functions ---
    def location_of_color(color_name):
        for well, protein in well_colors.items():
            if protein.lower() == color_name.lower():
                return color_plate[well]
        raise ValueError(f"No well found for {color_name}")

    def dispense_and_detach(pipette, volume, location):
        assert(isinstance(volume, (int, float)))
        above_location = location.move(types.Point(z=location.point.z + 5))
        pipette.move_to(above_location)
        pipette.dispense(volume, location)
        pipette.move_to(above_location)

    # --- Main Execution ---
    for protein_name, points in all_designs.items():
        if not points:
            continue

        pipette_20ul.pick_up_tip()

        for i, (x, y) in enumerate(points):
            # Aspirate every 20 drops (full pipette capacity)
            if i % 20 == 0:
                volume_needed = min(20, len(points) - i)
                pipette_20ul.aspirate(volume_needed, location_of_color(protein_name))

            target_location = center_location.move(types.Point(x=x, y=y))
            dispense_and_detach(pipette_20ul, 1, target_location)

        # Drop tip after completing one protein color to prevent mixing
        pipette_20ul.drop_tip()

code generated image

III:

1.Published paper review

scientific paper

This paper explores enhancing lipid production in plant cells through automated, high-throughput genome engineering and phenotyping, focusing on maize (Zea mays) and Nicotiana benthamiana. Rather than industrial manufacturing, the research develops a conceptual framework for accelerating biological Design-Build-Test-Learn (DBTL) cycles to improve the yield of lipid macromolecules. The system measures editing efficiency by targeting the photosynthetic gene HCF136; its knockout produces distinct changes in chlorophyll fluorescence intensity, serving as a high-throughput proxy for successful genomic modification and subsequent analysis of lipid metabolic pathways.

The study utilizes the iBioFAB biofoundry, an automated platform integrated with Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry (MALDI-MS) to expedite genome characterization. These biofoundries operate as specialized informatics-driven workstations that combine robotics with high-throughput instrumentation to scale repetitive protocols. By automating the transition from genome editing to cellular effect characterization, the system reduces human error and significantly optimizes the time required to identify high-yielding lipid variants, transforming traditionally manual plant engineering into a scalable, iterative process.

2. Final individual project idea:

1)

2)

3)

Week 4 HW: Protein Design I

Week 4 Protein Design part I;

A. Conceptual questions answers

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)

a Dalton is a unit of measure, equal to 1/12 of carbon-12 atom weight; approx. 1.66 x 10^-27 Kg.

Type of meat is not specified, I like chicken so.. On average, 100g of chicken breast has 27g of protein; for 500g, that’s 135g protein;

1 gram =6.023 × 10²³ atomic mass units => 135g are ~8.13 x 10²⁵ Da;

Dividing the Daltons of protein in chicken by an a.a. weight, I should end up with roughly the number of molecules found in 500g of meat => ~8.12 x 10²³ molecules.

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

Lmao. Animal based products, as well as plants’ are made up of fat (lipids), proteins, carbohydrates and DNA/RNA. Except from the genetic material, that just passes through the digestive system, each macromolecule from either category is essential for human health. Now, why doesn’t the DNA we ingest get translated and build up proteins to turn us into.. cows? Mainly because it gets denaturized by HCl from stomachs, and because only RNA can be read as instructions for proteins, and anyhow it is not enough to cause any problems. Even if some gets transcribed, a couple of proteins will not take over our body and transform it Ben10 style.

Honorable mention: Viruses; They can “manipulate” host genetic material to make up the few proteins needed for virus “replication”; even in this scenarios, humans don’t turn into viruses, just end up replicating them.

3. Why are there only 20 natural amino acids?

Firstly, because they’re enough- they have enough structural diversity to make up any needed complex protein (and provide balance between hydrophobic and hydrophilic; acid and alkaline; etc.). Secondly, the 20 essential a.a. have a low energy cost (metabolically, they do not require much energy to be synthesized in-body), and are chemically stable, unlike other a.a. existent in nature. Furthermore, scientific speculation also impacts answer- “Frozen accident” theory: early in evolution, this set was likely chosen, and the machinery became so specialized that changing it to include new amino acids would be disadvantageous.

4. Can you make other non-natural amino acids? Design some new amino acids.

Non-natural a.a. typically refer to non-proteinogenic a.a., they are still found in nature, as they can form spontaneously.

Chemically, an amino acid is an organic compound made up of an alpha carbon (central) bonded with a carboxylic group -COOH, an amino group -NH2, a hydrogen, and a variable chain -R, that determines what the a.a. is. So, practically, it is possible to make up “new” a.a.

So, for my proposed a.a. will contain a naphthalene group including a nitrogen atom, and 1, double bonded, new oxygen atom, along with other carbon-carbon chains:

^ I used MolView.

5. Where did amino acids come from before enzymes that make them, and before life started? Chronologically, as in the Archean Eon, bacteria and archaea prevailed. Until then, bio-synthetical pathways for singular amino acids must have been already established;

It is believed life emerged from inorganic matter - Abiogenesis; So, that’s probably how amino acids even came to be, “before life started”. Others theories build upon extraterrestrial materials, as well as a.a., that could have come from a meteorite.

Personally, I resonate most with the Abiogenesis theory. And so, will advocate for it in present assignment.

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

α-helices are usually right-handed, as their dense, tightly packed structure has a.a.’s R “poking out”- they’re made out of L-amino acids. So by using D-amino acids, and not having the structure fall apart, the whole stereochemistry ought to be reversed, thus resulting in a left-handed α-helix.

7. Can you discover additional helices in proteins?

Yes, besides α-helices, other helical structures are classified as protein structures; examples include π-helix and 3↓10-helix. It is also possible to design and synthesize foldamers with helical shapes that do not exist in nature.

8. Why are most molecular helices right-handed? Because almost all amino acids found in living organisms are left-handed. D-amino acids are scarce, and may not even serve building proteins as their purpose.

9. Why do β-sheets tend to aggregate?

β-sheets tend to aggregate because of intermolecular forces acting upon atoms from structural groups in a.a. (such as hydrogen bonds and hydrophobic interactions). This can lead to stacking of multiple sheets together- causing issues, specifically amyloidogenic as they lack structural integrity on their edges; thus forming β-sheets with neighboring strands.

• What is the driving force for β-sheet aggregation?

Non-covalent intermolecular forces, mostly non-polar hydrophobic ones.

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

Because, thermodynamically speaking, β-sheets are more inert, and exhibit less energy needed- they’re more stable. In turn, making up cross- β structures. Hydrophobic forces cause parallel alignments, creating hydrogen bonds that are stable and protease-resistant.

• Can you use amyloid β-sheets as materials?

I figure that their increased stability and mechanical strength may facilitate amyloid β-sheets usage as building blocks for various materials. Speculatively speaking, they may be useful for in-human structures, but I think a protein plastic would be cool: poly amyloid β-sheets.

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

For a motif to be well-ordered it needs to be in the lowest energy state possible and be chemically and structurally stable (achievable by using the right amino acids); and that is usually the case for most proteins found in life organisms- depending still on the medium. Simply using β-sheets I could base the design on β hairpin model (3 antiparallel β-sheets), a Greek key motif with 4 or a β-barrel; moreover, I need to assure using the optimum a.a. interactions and proprieties (hydrophilic/phobic to let the protein “package itself” better and the polar residues outside to avoid internal protein disturbance). Also, protein motifs almost always depend on the rest of the protein, designing just the motif will be hard.

I based my design on GB1 domain Protein G

2 antiparallel β-sheets: MTYKLILN(GKTL)KGETTTEA

After changing some a.a. up for improved forces (C-C: disulfide bridges, ionic bonds, hydrophobic bonds) and reduced flexibility (a.a. -> P) and cutting some off, I ended up with a stable enough looking (I hope) motif:

MTPKLIKNGKTLKCETTT

B. Protein Analysis and Visualization

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

RuBisCO- Vegetal protein responsible for Carbon molecule morphism in Calvin cycle, fixing CO2 into PGA, prime step in making C6H12O6; I have chosen it because the other one didn’t work. Amino acid sequence used belongs to RuBisCO small subunit 1B (RBCS-1B);

sp|P10796|RBS1B_ARATH Ribulose bisphosphate carboxylase small subunit 1B, chloroplastic OS=Arabidopsis thaliana OX=3702 GN=RBCS-1B PE=1 SV=1

^ work protein isolated from Arabidopsis thaliana plant ( O03042 ).

2. Amino acid sequence:

MASSMLSSAAVVTSPAQATMVAPFTGLKSSASFPVTRKANNDITSITSNGGRVSCMKVWPPIGKKKFETLSYLPDLTDVELAKEVDYLLRNKWIPCVEFELEHGFVYREHGNTPGYYDGRYWTMWKLPLFGCTDSAQVLKEVEECKKEYPGAFIRIIGFDNTRQVQCISFIAYKPPSFTDA

• using Amino acid- colab counter.:

The length of the protein is: 181 aminoacids.
The most common amino acid is: S, which appears 15 times.

• using UniProt’s BLAST tool:

BLAST 250 results found in UniProtKB;
Popular organisms: A. thaliana (6).

• Protein belongs to “Ribulose-bisphosphate carboxylase family”.

• using RCSB:

1. Ribulose bisphosphate carboxylase small chain 1B, chloroplastic- correspondent to provided a.a. seq.
2. Ribulose bisphosphate carboxylase large chain. Whole RuBisCO hexadecamer protein conists of 2 small (C, D | 181 a.a.) and 2 large subunits (A, B | 479 a.a.).

• Deposited: 2016-03-17; • Resolution: 1.50 Å (smaller than 2.70 => good quality).

• Molecules apart from protein in solved structure:

• using SCOP 2, I identified protein belogining to structure classification family:

Protein visualisation using Pymol software:

pdb_00005IU0 (whole complex)

CARTOON

RIBBON

BALL AND STICK

for which, I used commands: 
{
show spheres
show sticks
set sphere_scale, 0.25
}

COLORATION BY SECONDARY STRUCTURE

• It has more helices

PROTEIN SURFACE

-it does have binding spots;

C. Using ML-Based Protein Design Tools

-using google colab on RuBisCO;

C1. Protein Language Modeling

• ESM2 link

The initial segment of the protein shows high mutational tolerance, visible as a large dark blue block. After the transit peptide, map transitions into a region of high sensitivity- yellow and bright green.

Proline (P) and Glycine (G) amino acids often contain more “bright” spots. These are “helix breakers”; adding them to a stable helix typically disrupts the fold, leading to a high sensitivity score.

Bibliography

PyMol usage

COMMANDS:

bg_color [color] (e.g. I used: black/white);

fetch [pdb_id] (e.g. I used: 00005IU0);

show cartoon, spheres, sticks, surface, lines, ribbon;

as cartoon, spheres, etc -changes entire selection to chosen aspect, hides other styles;

color [color], resn [a.a.+a.a.+..] (e.g. color red, resn ASP+GLU for Acidic/Negative a.a.);

set [element_character], [measure] (e.g. set sphere_scale, 0.25, set stick_radius, 0.2);

dist [name], [selection1], [selection2] -measures distance between two atoms or groups;

png [filename.png] -saves current screen view;

INTERFACE:

• A (Actions): Major operations like renaming, deleting, or applying complex presets (e.g., “publication” mode).
• S (Show): Adds representations like cartoon, sticks, or surface.
• H (Hide): Removes specific representations (the opposite of Show).

• L (Label): Adds text labels for residues, atoms, or distances.
• C (Color): Changes color of the object using predefined palettes or residue-specific schemes]

Week 5 HW: Protein Design II

Week 5 Protein Design part II;

Superoxide dismutase 1 (SOD1) – cytosolic antioxidant enzyme that converts superoxide radicals (O2-) into H2O2 and O2. In its native state, it forms a stable homodimer and binds Co and Zn.

Mutations in SOD1 cause familial Amyotrophic Sclerosis (ALS). A4V leads to most aggressive forms of disease, as it destabilizes the N-terminus, perturbs folding energetics and promotes toxic aggregation.

A. SOD1 Binder Peptide Design

Part 1. Generate binders with PepMLM

P00441:

sp|P00441|SODC_HUMAN Superoxide dismutase [Cu-Zn] OS=Homo sapiens OX=9606 GN=SOD1 PE=1 SV=2

A.a. seq: MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

Induced mutation: (A4V)

MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

Using: PepMLM colab:

from hugging face

• set the length to 12 in cells [3] and [4];
• ran all codes;

Part 2. Evaluate Binders with AlphaFold3

Using alphafold server:

0. Control interaction (FLYRWLPSRRGG)

Seed: 1935780713

ipTM score: 0.32

Mutated Residue

Red Highlighted section = the A4V mutated residue.

Original SOD1:

1. WHSGATALAWKK

Seed: 1320704135

ipTM score: 0.4

2. KRYYAVVLEWGE

Seed: 105903932

ipTM score: 0.38

3. WRYPAVVVRLGK

Seed: 1939890175

ipTM score: 0.31

4. WLYYPVAAAHKK

Seed: 25762303

ipTM score: 0.51

3. ipTM score

-> interface predicted template modeling. ipTM measures the accuracy of the predicted relative positions of the subunits within the complex. Values higher than 0.8 represent confident high-quality predictions, while values below 0.6 suggest likely a failed prediction. ipTM values between 0.6 and 0.8 are a gray zone where predictions could be correct or incorrect.

• where does the peptide bind?

Neither peptide seems to be closely bonded to SOD1. In reality, they may be weak forces acting throughout structure, facilitating functional bonds.

• does it localize near the N-terminus, close to A4V?

Except to 2nd peptide, the other 3 (+control) are proposed to not act close to the N-terminus of SOD1; it also doesn’t engage the beta-barrel region. All are surface-bound, further than expected from protein than I expected.

4. Describe ipTM values, observe if any PepMLM-generated peptide matched or exceeds the known binder?

They’re low, all of them appear to be under the threshold that could guarantee a definitive, reliable interaction prediction. AlphaFold’s server specifies that a ipTM score under 0.8 indicated a relatively inaccurate prediction. Thus, the peptides may actually bind in some way to SOD1 (there would be higher chances of a surface-bound cumulated Van Der Waals interactions). There also is the possibility for an erroneous generation of a binder peptide by the google colab.

Comparing strictly ipTM scores, AlphaFold is more confident in my last tested peptide (WLYYPVAAAHKK).

Part 3. Evaluate Properties of Generated Peptides in the PeptiVerse

Using PeptiVerse:

I selected “Calculate Basic Properties”, solubility, hemolysis, binding affinity and a neutral pH of 7.

photos

0.

1.

2.

3.

4.

The highest binding affinity is found in my second generated peptide (KRYYAVVLEWGE), and so, there is no correlation between ipTM and binding affinity; even so, all binder peptides are predicted to have poor binding affinity for mutated SOD1. All are non-hemolytic and soluble. 

Part 4: Generate Optimized Peptides with moPPIt

Using moPPIt colab

from hugging face

• Compared to PepMLM’s generated peptides, these ones are not as soluble (at least, based on the code’s prediction), more hemolytic. But overall, the ability to generate a peptide targeting a specific site/ surface may lead to improved results and better understanding of enzymatic activity of SOD1.

• I would probably use multiple softwares before advancing to wet lab testing and possibly clinical studies any generated peptide due to lack of confidence in generative models and chance of hemolysis. I find the second peptide to be the most safe, and even with its relatively small affinity score, there’s still chances of directed, promising protein binding.

I was curious, so using alphafold:

ipTM = 0.5 pTM = 0.89

Week 6 HW: Genetic circuits I

Week 6 Genetic circuits part I: Assembly Technologies;

  Abstract: Using different techonolgies, DNA can be synthesized via PCR (polymerase chain reaction); and genes can be added to vectors that later get expressed in cell dependent systems. 

Lab protocol questions:

• linked docs

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

The Phusion HF PCR Master Mix is one of the “Media, Equipment and Consumables” from the protocol.

It consists of Phusion DNA Polymerase, deoxynucleotides and reaction buffer (containing MgCl2)

• Phusion DNA Polymerase- high fidelity (few errors) and robust performance (consistency).
• Deoxynucleotides- mix of standard nucleotides, but exclusive to DNA: A,C,T,G (“deoxy-“).
• Reaction buffer- used for maintaining optimal biochemical proprieties (such as pH) and ensure maximum enzyme activity (also includes essential ions for primer annealing).

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

Primer annealing represents the stage of primer bind to specific single-stranded DNA;

Mainly, annealing temperature is determined by individual primer melting temperature and chemical environment of reaction. Generally, it is determined approx. melting point of primer -5°C.

• thermofisher annealing calculator;

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

• Description: Based on used DNA sequence, PCR determines cell-independent synthesis of copies of DNA.

Restriction enzyme digest

• Description: using restriction enzymes, there isn’t really much “creating” done, just cutting. Special enzymes are incubated along DNA sequences and act upon it, cutting it into smaller pieces based on presence and identification of special codon/ nucleotide short sequences. After undergoing electrophoresis, the different DNA segments spread and get predictably “digested” based on molecular weight.

Both serve the purpose of vector linearization, making Gibson assembly mechanism possible.

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

First of all, the sequences need to have blunt identical ends so that it can be cut and ligated. And so, DpnI digestion may be applied to ensure that methylated parental DNA templates are selectively degraded; gel electrophoresis could also serve as a way to check DNA purity.

5. How does the plasmid DNA enter the E. coli cells during transformation?

Through pores, ruptures in cell membrane created via thermic shock or electroporation (under the act of diffusion).

6. Describe another assembly method in detail (such as Golden Gate Assembly)

1. Explain the other method in 5 - 7 sentences plus diagrams (either handmade or online).

2. Model this assembly method with Benchling or Asimov Kernel!

Golden Gate Assembly

Golden Gate assembly is a highly efficient DNA cloning method. It uses type II enzymes that “cut” DNA consequent to recognized nucleotide sequence (usually palindromic), mostly after 4-5 nucleotides-post recognition site; and are dependent of Mg²⁺ as a cofactor. They act on both the plasmid and fragment pretty much the same; thus, the digested insertion fragment covalently binds to plasmid overhang complementary DNA strand.

Compared to Gibson a. it doesn’t require polymerases to build up lacking DNA strand fragments, but it does usually include ligase enzymes to ensure vector stability and successful annealing. G.G. assembly is preferred when working with many insertion fragments (30-50) because of its simultaneous activity of ligases and restriction enzymes.

Type II restriction enzyme example:

BSAI (takes 5-15 mins to digest PCR sample)

5’ ..GGTCTC (N1) | .. 3'

3’ ..CCAGAG (N5) | .. 5'

=> 4 base overhang

Gibson assembly scheme

Bibliography

• NL Biolabs- Phusion™ HF PCR Master Mix;

• thermofisher- primer annealing;

• NE Biolabs- annealing calculator;

• snapgene- GA photo;

Week 7 HW: Genetic circuits II

Week 7 Genetic circuits part II: Neuromorphic Circuits;

  Abstract: . 

Part 1)

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

IANNs allow for designing circuits that can process multiple types of input signals at once to execute sophisticated, non-linear tasks (logic pathways); traditional genetic circuits operate on linear Boolean logic (yes/no, 0/1), and are inefficient for mapping complex goals or biological relationships. Furthermore, IANNs adapt and learn: (or at least have the potential for it) they enable the execution of complex decision boundaries by approximating any continuous function on a bounded domain.

Traditional genetic circuit -> only Boolean

IANNs -> quantifiable response

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.

Intracellular diagnostic biosensor that discriminates early‑stage oncogenic signaling from normal cellular behavior and triggers a therapeutic response only when a cancerous state is detected. The IANN takes multiple molecular biomarkers as inputs, each represented by a separate TX/TL factor (or, for eg, metabolite-sensitive promoters). Each input is translated into a graded regulatory signal, that makes up the first layer of the NN. The output is a graded effector response.

Actual IANNs may become unstable and exhibit crosstalk between promoters and unpredictable behavior (cellular noise).

3. Below is a diagram depicting an intracellular single-layer perceptron where the X1 input is DNA encoding for the Csy4 endoribonuclease and the X2 input is DNA encoding for a fluorescent protein output whose mRNA is regulated by Csy4. Tx: transcription; Tl: translation.

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

Template shows regulating XOR function, where if input x1 and x2 are both present or absent, no FP gets translated, but if one is absent and the other present, fluorescent protein gets synthesized.

Part 2)

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

Fungal materials are pretty much produce that form from the mycelium part of a fungi (the mushroom part is just the reproductive organ responsible for maturing and dispersing spores) and mostly grown on a substrate that is later removed. Depending on the specie used, the physical and chemical proprieties may be selected so that it mimics rubber, polyethene, polyvinylchloride, leather and even wood; it can be manipulated to take shapes such as fungal packages, containers used instead of PET or other conventionally used polymers. They can decompose naturally, so they’re an ecofriendly variant of plastics. Their applicability ranges from insulation, packaging, and construction materials.

Actual examples:

• ecoative - packaging, foam, leather, insulation and even food;
• mycoworks - furniture, decor materials;
• loop-biotech - coffins.

My personal favorite brand, they enrich soil quality by making coffins out of saprophytic mycelium that later develops and decomposes organic matter (dead bodies), promoting nature’s cycle of life. The nutrients made available by fungi can later be used by photosynthesizing plants to fix carbon and filter out the atmosphere.

• biohm.uk - construction materials, packaging and furniture

another use of bioengineered fungi is replacement of meet by enhancing metabolism so production of fibrous structures with lots of protein is increased, while also redirecting the chitin from the cell walls to reduce their toughness; Crispr can also be used to change taste to be more meat-like: increasing heme molecules.

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?

For once, being able to synthesize more complex proteins. Fungi are largely used nowadays to synthesize several more complex molecules and even proteins (mostly via fermentation); Unicellular fungi (like Saccharomyces cerevisiae and Aspergillus niger) are still eukaryotic cells so their metabolic pathways are more stable (that also means they’re harder to engineer) because of the presence of organelles: the endoplasmic reticulum (+ chaperones) and Golgi apparatus to fold proteins more efficiently and can perform complex glycosylation, being able to synthesize things like antibodies and insulin; mitochondria, vacuoles can also pose a huge advance due to more organised and controlled anabolic activity.

Also, fungi evolved to secrete a lot of stuff often (in the wild, they survive by pumping enzymes into their environment to break down organic matter), fungi, especially filamentous, can secrete massive amounts of proteins directly into growth medias- they just diffuse the product out, without the industrial need of cell kill, and later distillation.

But! Fungi grow slower, and are more stable + have bigger genomes, which means they’re harder to modify and keep them stable.

Opposed to bacteria, I would use fungi to synthesize complex proteins or ferment, instead of simpler chemicals such as ethanol (alcoholic ferm. in fungi).

• genetically engineering S. cerevisiae, it could ferment toxic substrate into fertiliser or other helpful dispersible macronutrients in absorbable form (insecticidal organophosphates into inorganic (di)hydrogen phosphate, depending on soil pH – fungi are also really tolerant to acidic media;
• synthesis of specific antibodies to use in diagnostics (BlyS/BAFF) or bio-therapy (belimumab); *both examples are provided from SLE care.

Part 3)

1. Review the Individual Final Project documentation guidelines.

2. Submit this Google Form with your draft Aim 1, final project summary, HTGAA industry council selections, and shared folder for DNA designs.

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

Week 9 HW: Cell-Free Systems

Week 9 Cell-Free Systems;

  Abstract: Cell free (protein synthesis) systems are crude cell independent synthesis technologies that, by using lysate substrate can perform biochemical reaction such as prot synthesis outside of living cells. 

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

Flexibility: substrates, cofactors, inhibitors and many other factors can be directly manipulated as CFPS doesn’t really on a cell’s ability to stay alive. Toxic or “non-physiological” substances (D-a.a., HCN etc.).

Efficiency: there is no need for cloning, long growth phases (time related factors) or promoting substances to increase yield (eq: cytokines) to assure synthesis. Also, there’s less work needed- no codon-optimization needed, chaperones, so there can be more reactions ran in parallel with different conditions.

CFPS can lead to endless possibilities when it comes to amino acid/protein synthesis, not only being able to extend a bit over the 20 natural a.a., but actually building up proteins with who knows with what actions formed from any a.a. imaginable (H-,COOH-,NH2-,R-C), also, the synthesized proteins can have any propriety or cytotoxicity attributed as there’s no cell membrane (or viability in general) to care about.. More complex labeling is also easier as tRNA charging, energy mix and modification enzymes can be directly tuned, without interfering the cell’s native metabolism.

All system energy is channeled to one/ more proteins of interest and just that.

2. Describe the main components of a cell-free expression system and explain the role of each component.

Cell extract composed of the molecular machinery and cofactors need for reactions: Ribosome ribonucleic complex, RNA polymerase, other desired proteins. RNA p. is simply the enzyme that makes up corresponding tRNA to given DNA (TX); the RRNC sustains TL reactions by translating the tRNA in proteins; other proteins may be added to enhance target protein synthesis structure, function or TX-TL sustainability. Eq: translation factors (IF, EF, RF) are essential GTP-dependent proteins driving bacterial protein synthesis.

Template DNA/mRNA, amino acids and nucleotides/ nucleoside triphosphates (ATP, GTP, CTP, UTP) are also provided in most cases for obvious reasons.

Other components include: salt glutamates (K, Mg) and other ions to stabilize ribosome function and RNA structure.

3. 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.

Protein synthesis is energetically expensive, each peptide bond and each step of translation consume ATP and GTP; without constant regeneration, ATP and GTP are rapidly depleted, leading to early unwanted termination of TX-TL reactions and very low yields. n traditional cell-free protein synthesis reactions, the energy source (typically phosphoenolpyruvate (PEP) or creatine phosphate) is the most expensive substrate. However, for most biotechnology applications glucose is the preferred commercial substrate.

Article demonstrates how G6P (glucose-6-phosphate) can be used as an cheaper alternative to PEP; G6P –(phosphoglucose isomerase; -2Na)-> fructose-6-phosphate –(phosphofructokinase-1)-> fructose-1,6-bisphosphate –(aldolase- cleave/ break down)-> G3P ->..-> phosphoenolpyruvate (PEP) –(Pyruvate kinase)-> ADP->ATP, pyruvate;

pretty much this but less hardcore, because CFS. note: there are more reactions that take place such as 3PGA ⇌ 2PGA via phosphoglycerate mutase

So, from what I’ve read, there’s an undeniable need for a substrate and some enzymes to catalyze reactions to ATP, the further you go from phosphorylation, the more enzymes are needed.

4. Compare prokaryotic versus eukaryotic cell-free expression systems. Choose a protein to produce in each system and explain why.

5. 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.

Membrane proteins need a hydrophobic medium to fold properly and remain soluble. So I would most probably adjust to imposed settings- add lipids/liposomes, nanodiscs (membrane patches with scaffold proteins). But,

a. proteins may not insert into lipids spontaneously (=> decreased yield, waste build-up)- mimic cellular insertion mechanism.

b. protein synthesis requires ribosomes and hydrophobic a.a. => proteins aggregate and result in inclusion bodies (useless)- detergents (eg. DDM) can form shielding micelles around protein hydrophobic parts to keep them soluble.

6. 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.

1. Genetic material template degraded, contaminated with salts or not enough. Scale up concentration template.
2. Non-optimal reaction conditions, there are a lot of factors that impact CFS (as well as CDS, but cells can mediate themselves considerably)- ions, temperature, energy, pH, redox states, other reagent wrong concentration; follow protocol or change protocol, accordingly.
3. Degradation of protein; depends a lot..

Homework question from Kate Adamala

Design an example of a useful synthetic minimal cell as follows:

1. Pick a function and describe it.

a. What would your synthetic cell do? What is the input and what is the output?

Optimize immune responses towards Helicobacter pylori. It detects CagA or VacA oncoproteins specific to pylori bacteria via surface-bound antibodies/ Toll-like receptors: antibody-induced clustering of surface receptors (eg. BCR, Fc, or RTKs) => activates associated tyrosine kinases to initiate downstream signaling pathways, which directly phosphorylate translation components => protein synthesis.

b. Could this function be realized by cell-free Tx/Tl alone, without encapsulation?

No, the DNA wound get transcribed randomly and lead to autoimmune disease activation; there’s no trigger protein or reusability.

c. Could this function be realized by genetically modified natural cell?

Maybe, but it would be too hard to modify everything as I want it to be and also keep alive and efficiently running.

d. Describe the desired outcome of your synthetic cell operation.

H. pylori survives by using its virulence factors (such as CagA and VacA) to evade immune detection and modulate the response. It can induce regulatory T cells (important for avoiding autoimmune responses in humans) that suppress the effective Th1/Th17 responses, leading to chronic inflammation rather than elimination. So, by attaching specific CagA/ VacA receptors on prostatic cell’s surface, it can only get simulated in the presence of a pylori infection and help the immune system synthesize ligands that bind to a part of regulatory cytokines such as IL-10, TGF-beta (downregulate T cell activity) and proliferation, promoting (immune-boosts) cytokines such as IFN-gamma, IL-6 or IL-2 (doesn’t provide inflammatory response).

2. Design all components that would need to be part of your synthetic cell.

a. What would be the membrane made of?

Chitin to withstand gastric acid low pH (<2), inspired by fungi cell wall.

b. What would you encapsulate inside? Enzymes, small molecules.

Template DNA, enzymes responsible for the phosphorylation cascade from the antibody to nucleus/nuclei; enzymes for protein assembly and a synthetic ER + Golgi apparatus (for ILs syn, maturation and transport) to assure correct protein folding and efficiency.

c. Which organism your Tx/Tl system will come from? Is bacterial OK, or do you need a mammalian system for some reason? (hint: for example, if you want to use small molecule modulated promotors, like Tet-ON, you need mammalian)

Possibly long-lived plasma cells’ DNA to get a comprehensive idea of how the cytokines need to be synthesized and maybe fungi to ensure some membrane regeneration activity. But for efficiency, I think bacterial system templates can be used as well as ILs, IFNs aren’t that hard to translate, and the surface-attached antibodies/ other receptors can be placed then when the syn cell is fabricated.

d. How will your synthetic cell communicate with the environment? (hint: are substrates permeable? or do you need to express the membrane channel?)

ILs can mostly pass the cell membrane fairly easily, ligand proteins not so much as they’re big (at least they’re not polar) and get caught up in the lipid bilayer. In biological cells they get transported in a vesicle that pops outside of cell; the whole process is driven by ER ad GA.

3. Experimental details

a. List all lipids and genes. (bonus: find the specific genes; for example, instead of just saying “small molecule membrane channel” pick the actual gene.)

To be feasible: there are high chances of promoting autoimmune responses, but because it’s easier to transport I’ll go with IL-2 and IFN gamma, (no ligands involved). I still depend on GA and ER presence and function and other phosphorylation proteins/ small molecules, lipids and enzymes.

b. How will you measure the function of your system?

Sensitivity + specificity to oncoproteins; Synthesis capabilities.

Homework question from Peter Nguyen

Freeze-dried cell-free systems can be incorporated into all kinds of materials as biological sensors or as inducible enzymes to modify the material itself or the surrounding environment. Choose one application field — Architecture, Textiles/Fashion, or Robotics — and propose an application using cell-free systems that are functionally integrated into the material. Answer each of these key questions for your proposal pitch:

• Write a one-sentence summary pitch sentence describing your concept. • How will the idea work, in more detail? Write 3-4 sentences or more. • What societal challenge or market need will this address? • How do you envision addressing the limitation of cell-free reactions (e.g., activation with water, stability, one-time use)?

Homework question from Ally Huang

GenesInSpace competition

1. Provide background information that describes the space biology question or challenge you propose to address. Explain why this topic is significant for humanity, relevant for space exploration, and scientifically interesting. (Maximum 100 words)

Jupiter’s moon, Europa has liquid water on its surface and many substantial elements and may hide alien life. But, I think it may actually be able to host human life in the distant future by changing its atmosphere and overall proprieties. The main problems are extreme cold, intense radiation from Jupiter’s magnetosphere and low gravitational pull.

100 words are nowhere enough

When we think of space the first thing that comes to mind is the lack of breathable gas, UV radiation or extreme cold, but that’s not everything that stands in the way of extraterrestrial survival of humans. A mediocre host planet such as:

• Mars may provide some shelter from UV, is a little warmer and is quite material rich, but it’s extremely hazardous imposing weather is not a problem I aim to resolve with synbio.

• The Moon is close to Earth and that’s about all. It is nowhere close to hospitable.

• Europa, Jupiter’s moon atmosphere is mainly made up from oxygen, but it’s surface also consists of P, S, C, H, O compounds and liquid water under a ~20 km thick ice layer. As it orbits around Jupiter, its surface is tugged, and essentially stretched by Jupiter’s strong gravitational pull and generates heat- energy via the process named tidal heating, so there may even be hydrothermal systems in Europa’s oceans. And well, I want to live on Europa not in Europe :)

Problems

1. Lethal Radiation Levels- Van Allen radiation belts are zones of high energy charged particles (p⁺, e^-), solar wind trapped by Earth’s magnetic field, located within the magnetosphere, these radiation sites pose serious health risks to astronauts and electronics.

Well, Europa falls right in Jupiter’s active radiation site cavity, the largest and most powerful of any planetary magnetosphere in the Solar System. And unfortunately, wider and flatter than the Earth’s magnetosphere, Jupiter’s is stronger by an order of magnitude, while its magnetic moment is roughly 18,000 times larger. *Jupiter’s magnetic field is generated by electrical currents in the planet’s outer core (liquid metallic H2) and large amounts of SO2 gas (from Io volcanic plasma) forming a torus.

2. Extreme cold- surface temperature of Europa is extremely low, averaging around -160°C, -200°C. Low enough to avoid a surface temperature of +100°C due to overaccumulation of GHGs as they are also needed in high concentration to reduce radiation; CH4 is highly flammable; O3 is not flammable, but it is a powerful oxidizing agent that increases fire risks- it can cause explosive reactions with combustible materials such as CH4.. and decomposes spontaneously at high temperatures, concentrations or under shock. But I think the really slow increase of temperature can also stabilize the atmosphere with all the evaporated H2O and other ions.

3. Thick ice shell: can melt over time;

4. Low gravity. Γ =G(M/r²), where G (or K)= 6.6743 × 10-11 ;

rEu ~ 1560.8 km, mEu = 4.79984×10 ²² kg => mean density ~ 3.013g/cm3 (0.546 Earths).

ΓEu = 1,315 m/s^² (ΓEarth x 7.45)

ΓEarth ~ 9.8 m/s²;

Low gravity heavily impacts human health and bone and muscle development and function. And well, after all the years needed for methane, ozone synthesis, I guess humans may have progressed enough to bring large enough quantities of heavy metals such as Ni, that are already in Europa’s core, to enhance its gravitational pull. But this whole idea is such a far fetch- I absolutely love it.

2. Name the molecular or genetic target that you propose to study. Examples of molecular targets include individual genes and proteins, DNA and RNA sequences, or broader -omics approaches. (Maximum 30 words)

• IgG antibodies and their enzymatic proprieties; sequenced mature plasma B cell DNA to replicate; • Methanogenic archaea - Methanococcus jannaschii (deep-sea hydrothermal vent dweller) structure and metabolic processes.

3. Describe how your molecular or genetic target relates to the space biology question or challenge your proposal addresses. (Maximum 100 words)

IgG is way simpler and maybe more efficient to synthesize using BioBits and be put into action. The main problem is its folding, as it requires complex modular assembly of two heavy and two light chains into the “Y” shape. I couldn’t find an a.a. seq specific for IgG with the variable domain specific for catalyzing H2O into O3, but sequencing a plasma B cell genome, CFS can replicate TX-TL and synthesize target protein (~150 kDa structure).

4. Clearly state your hypothesis or research goal and explain the reasoning behind it. (Maximum 150 words)

Europa’s atmosphere and overall hostability can be increased by increasing its atmosphere ability to absorb radiation and keep heat in by using a gas such as methane (CH4), or ozone (O3). They not only are greenhouse gases that keep warm from diffusing off the moon’s surface, but can also absorb radiation and a large enough quantity may actually make Europa way less dangerous. Using a methanogenic cell’s metabolic pathway as a template, CO2 can be indirectly turned into CH4; and endogenous ozone production present in immune sys. to turn H2O (deep under all the ice) into O3. A CFS can achieve this without the need of life sustaining factors.

5. Outline your experimental plan - identify the sample(s) you will test in your experiment, including any necessary controls, the type of data or measurements that will be collected, etc. (Maximum 100 words)

I need to be sure that CFS can get under the ice without usage of electromagnetic devices to warm up and have the substrate to be able to work. Also, it needs to synthesize a lot of proteins to sustain the CH4 metabolic pathway, or be able to act as a catalyzer for the H2O+O2-> H2O3 reaction, that later decomposes into O3 and H2O2 (that turns into O2 and H2O photochemically). I need to somehow experiment self-sustainability. ER action at -200°C or substituent efficiency and feasibility.

Bibliography:

A User’s Guide to Cell-Free Protein Synthesis;

Wikipedia: Magnetosphere of Jupiter;

Week 10 HW: Measurement Technology

Week 10 Advanced Imaging & Measurement Technology;

Abstract: .

Waters Part 1 — Molecular Weight

eGFP Sequence:

MVSKGEELFTG VVPILVELDG DVNGHKFSVS GEGEGDATYG KLTLKFICTT GKLPVPWPTL VTTLTYGVQC FSRYPDHMKQ HDFFKSAMPE GYVQERTIFF KDDGNYKTRA EVKFEGDTLV NRIELKGIDF KEDGNILGHK LEYNYNSHNV YIMADKQKNG IKVNFKIRHN IEDGSVQLAD HYQQNTPIGD GPVLLPDNHY LSTQSALSKD PNEKRDHMVL LEFVTAAGIT LGMDELYKLE HHHHHH

Note: This contains a His-purification tag (HHHHHH) and a linker (the LE before it).

expasy calc:

1. No His-tag, M, linker => Theoretical pI/Mw: 5.59 / 26810.29 => expected MW ~ 26810.29;

Original => MW= 28006.60;

2. m/z=(M+z)/z; M-protein mass, z- no of charges

MW=n x m/ zn-n;

n= (m/zn+1)/(m/zn-m/zn+1) = ~31;

=> theoretical MW= 26810,29;

-> Accuracy= |MWexp-MWtheo| / MWtheo = 41 ppm <50.

3. I cannot confidently determine the charge state from the zoomed-in peak alone. The isotopic peaks are not clearly resolved; So, the spacing needed to identify z is not visible; the charge state is better assigned from the full charge-state distribution in the main spectrum.

• around 10

Waters Part 2 — Peptide Map Work - primary structure

Native, compact form protein has its most basic amino acids mostly buried deep inside, hard for protons to access; inaccessible for protonation.

Denatured, unfolded form protein has all amino acids exposed due to changes in pH, temperature, substrate, etc.

M stays the same, the peaks are influenced only by z – number of charges.

the peaks are spaced about 0.5 m/z units, isotope spacing is ~1/z => the charged state is about z= 2+

Waters Part III — Peptide Mapping - primary structure

1.

eGFP contains 20 lysines (K) and 7 arginines (R), for a total of 27 trypsin cleavage residues.

MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITLGMDELYK

2.

How many peptides will be generated from Tryptic digestion of eGFP?

Using expasy:

No. of peptides after Tryptic digestion: 19.

3.

4.

5.

6.

7.

BONUS questions

8.

9.

Waters Part IV — Oligomers

Waters Part V — Did I make GFP?

Week 11 HW: Bioproduction & Cloud Labs

Week 11 Bioproduction, cloud-autonomous laboratories;

  Abstract:  .   

A: The 1,536 Pixel Artwork Canvas | Collective Artwork

global community project:

*I guess I’m more the destructive type as I had more fun deleting pixels than I had painting the canvas (only after the experiment ended tho!!) ;)

–

It was such a nice and unexpected initiative, I also liked hearing about the million dollar project and r/place. I’m looking forward to what’s going to happen next in the context of the “1536 Pixel Artwork” :)

I would have liked a shorter cooldown as I’m not really the most patient person ever, and a more diverse colour palette.

B: Cell-Free Protein Synthesis | Cell-Free Reagents

1. Each component’s role in the cell-free reaction.

E. coli Lysate

BL21 (DE3) Star Lysate (includes T7 RNA Polymerase)

• core Tx/Tl mechanism drivers: ribosomes, tRNA, relevant enzymes and sometimes E. coli assigned cofactors; T7 specific polymerase that attaches to given DNA and transcribes it into mRNA (for high-copy T7 promoters).

Salts/Buffer

Potassium Glutamate

• determines ionic “strength”, mimics intracellular conditions to stabilize ribosomes and other protein enzymes.

HEPES-KOH pH 7.5

• reaction buffer; keeps pH optimal for enzymatic activity.

Magnesium Glutamate

• Mg²⁺ (cofactor) source, important for ribosome, polymerase activity and nucleotide interactions

Potassium phosphate monobasic

• KH2PO4: one potassium atom, retains 2 acidic hydrogens; acts as the more acidic than dibasic p.p., lowers pH is solution (acidity corrector) => buffer + phosphate for metabolic reactions and nucleotide synthesis. pH~4.1-4.5 at 25°C.

Potassium phosphate dibasic

• K2HPO4: 2 potassium atoms, one acidic hydrogen atom; more basic, can raise pH, same importance as p.p. monobasic. pH~8.5-9.6 at 25°C.

Energy / Nucleotide System

Ribose

• carbon 12 source used to regenerate nucleotides via ‘salvage pathways’.

Glucose

• primary energy source that gets worked down to ATP through glycolytic pathways by enzymes in the lysate.

Ribonucleic monophosphate (NMPs) – ribose sugar, phosphate group, nitrogenous base = AMP, CMP, GMP, UMP.

AMP

• precursor to ATP, incorporated into RNA as adenosine, purine base.

CMP

• incorporated into RNA as cytosine, pyrimidine base.

GMP

• can be converted to GTP- important for Tl and RNA synthesis, incorporated into RNA as guanine, purine base.

UMP

• incorporated into RNA as uracil, pyrimidine base.

Guanine

• base precursor that can be converted to GMP via salvage pathways, supplements nucleotide pools.

Translation Mix (Amino Acids)

17 Amino Acid Mix

• supplies most amino acids needed for protein synthesis. All a.a. except tyrosine, cysteine and tryptophan; (Phe, Leu, Ser, Pro, His, Gln, Arg, Ile, Met, Thr, Asn, Lys, Arg, Val, Ala, Asp, Glu, Gly)

Tyrosine

• poor solubility at neutral pH because of its hydrophobic aromatic ring. It’s often prepared in alkaline solution (pH>7) and added separately so it doesn’t precipitate.

Cysteine

• chemically unstable due to reactive thiol (-SH) group and gets oxidized to cystine, especially in oxygenated solutions. Is added separately to avid redox balance in reaction.

Additives

Nicotinamide

• precursor to NAD⁺, NADH; supports redox balance and metabolic energy regeneration.

Backfill

Nuclease Free Water

• backbone of solution, adjusts final concentration, volume and prevents nucleic acid degradation. Treated to remove DNases, RNases.

2. 1-hour PEP-NTP mix vs 20-hour NMP-ribose-glucose mix

The 1-hour system directly supplies high-energy molecules (PEP) and fully formed NTPs, enabling rapid but short-lived protein production with minimal metabolic processing. In contrast, the 20-hour system uses cheaper precursors (NMPs, ribose, glucose) and relies on endogenous metabolic pathways in the lysate to regenerate NTPs and ATP, allowing sustained, longer reactions. This makes the latter more cost-effective and stable over time but slower in initial output.

3. How can transcription occur if GMP is not included but Guanine is?

Guanine can be salvaged into GMP in some metabolic pathways that can be underwent in the lysate. Can, also, be phosphorylated to GTP.

C: Planning the Global Experiment | Cell-Free Master Mix Design

1. biophysical or functional property of each protein that affects expression or readout in cell-free systems

a. folding kinetics

b. ph sensitivity

c. oxygen dependence

d. maturation time

e. ATP regeneration (nucleotide source, a.a. incorporation)

f. amino acid supply

1. sfGFP

Famously robust and ultra-fast folding. No need for cellular chaperons. Resistant to high salt concentrations and temperature fluctuations.

2. mRFP1

Reduced photostability and slow maturation of chromophore (=> can delay fluorescence signal), less bright. Because CFS have limited working time, a big part of the protein may remain invisible before energetic resources run dry.

3. mKO2

Bright, maturates fast, sensible to pH.

4. mTurquoise2

High quantum yield (efficiency of converting absorbed light into emitted light), good folding efficiency, photostable, overall stable monomeric structure (=> doesn’t tend to form aggregates)

5. mScarlet_I

Rapid maturation, really bright (=> helps in decreasing red protein-specific readout delay; corelates translation rate to fluorescent signal almost perfectly), also folds efficiently.

6. Electra2

Rapid maturation, not that stable or bright, but tolerant to salts.

Adjusting cell-free mastermix for mTurquoise2

Add oxygen for chromophore formation?

D. Build-A-Cloud-Lab

I played around a bit on simulation tool to try and come up with a design. (I have no idea what I did there)

Bibliography

• AI tool It was, in fact, really helpful :)

• “Design-driven optimization of low-cost reagent formulations for reproducible and high-yielding cell-free gene expression”

Week 1 Lab: Pipetting

Individual Final Project

SECTION 1: ABSTRACT

Provide a concise, self-contained summary of your project (minimum 150 words).

The abstract should allow a reader to understand the purpose, approach, and expected outcomes of the work without referring to other sections.

By selectively changing present genes and adding new ones to Zea Mays, the common maize, it may be able to redirect its energy from producing fruit to synthesizing masses of phyto-volatiles from phosphorous compounds from the soil, that may improve the activity of the whole crop field by increasing yield due to less pest induced stress, and reduce the need for dangerous, polluting chemicals. Furthermore, if it works accordingly, it may redirect farmer’s usage from nitrogen rich fertilizers that, not only are polluting the soil, having the industry dependent on expensive environmentally hazardous practices, to all purpose fertilizer, even manure, that reestablishes natural healthy ratios for soil and important, useful micro-life inside it.

Plants have significantly longer and more complex genomes (~2.3 Gb for corn) compared to any bacteria I recently reported to in any HTGAA homework- I’m waiting for week 12 :). So, first of all, I need to map out exactly how I intend to edit the genome and keep it stable over time, efficient and not a biosecurity hazard. The changes I targeted are mostly changing metabolic pathways, but the most complex/ tricky to achieve is having the plant not produce fruit at the same time, as that would waste too much energy. By it still being able to make fruit its seeds can be collected and sold more conveniently compared to grafts, cuttings or pups (corn is not a perennial plant), but by it being after the harvest, all energy goes into protecting the field; for reproductive meanings, it can be managed in a growing facility.

Precisely:

• I will use … technology

1. CRISPR-Cas9

2. Plant Tissue Culturing

• I will use … chemicals

• I will test for it this way …

• I will use … plant immune mechanisms

• I will use … genes

CAMV35S promotor. I need to multiply the DNA => plasmid in bacteria

Project mapping questions:

• What pathogens do the plant defend itself from?

• Will they evolve to be resistant against it? How do I manage that?

• What chemicals does it use, what molecule types?

phytoalexins, organophosphates, phenols, terpenes, alkaloids, lignans, glycosides, pyrethroids, neonicotinoids, insect growth regulators, sulfur compounds, essential oils?

• Is the common corn plant the best pick? I just need a plant to disperse chemical compounds, not be edible, and a completely different plant can also help in the absorption of surplus macronutrients and has more genetic variability that slows down pathogen infection. But it’s only the macronutrients that change, I can benefit from using a corn plant by having the same external factors needed for thriving and so, I t can be grown in the same conditions as the rest of the field

It may not be the best because of its potency and metabolic work, but it may be the most convenable for the average farmer and industries;

Downsides: there may be plants, bushes that grow back after being cut down annually that could synthesize my chemical of choice better and not needing as much genomic change.

Upsides: it grows with the same needs as commercial corn, no special care needed and by being the same plant same-kin activity in the monoculture may be beneficial. By using a different plant, mostly bushes, its efficiency can be decreased by either being too picky or too adaptable and hard to get rid of.

• How do I improve specific metabolic activity/ what metabolic pathways do I change? How do I make it metabolically stable?

• How do I get the plant to make more leaves (more surface area) + produce fruit later?

• Can I make it absorb specific molecules?

• How do I disperse the chemicals?

• How do I specifically modify its genome?

Specific aims:

1. Your abstract should briefly address the following elements:

a. Significance: What problem or question does the project address, and why is it important?

b. Broad Objective: What is the overall goal of the project?

c. Hypothesis: What prediction or principle is the project testing or demonstrating?

d. Specific Aims: What key steps or milestones will be completed to achieve the objective?

e. Methods: What experimental or technical approaches will be used?

SECTION 2: PROJECT AIMS

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

1. Aim 1: Experimental Aim (this project):

a. “The first aim of my final project is to [achievable experimental goal] by utilizing [protocols, tools, or strategies].”

i. This aim should describe the core experimental objective you will attempt during this class. List or link any relevant methods or resources you plan to use (e.g., experimental protocols, automation workflows, DNA or protein designs, protein design tools, or Twist orders).

ii. You will provide a detailed step-by-step experimental plan for Aim 1 in the Experimental Design section of this assignment.

The first aim of my final project is to demonstrate practically that I can synthesize CRISPR-Cas9 via vector kit and use it accordingly on Zea mays’ genome (in select few cells) to cut out sequences and replace with metabolism altering genes and prove effective synthesis in vitro, by utilizing PCR, detection mechanism and plant tissue culture to generate a stable tissue I can further experiment on.

Empirically, I need to limit myself to quick and accessible methods to ensure real progress; nobody constricts me from working on my hypothesis even after HTGAA spring 2026 has ended. I can use electrophoresis on a to know the DNA has, at least, been changed.

Experimental goals:

1. Construct the CRISPR/Cas9 plasmid; order it

2. Construct the

3. Aim 2: Development Aim:

a. Describe the next step that would follow a successful Aim 1, extending the work beyond the scope of this course. This aim should represent a realistic progression of the project, such as executing additional experiments, solving a technical limitation, or developing the system or technology further.

Altering of large genome proportions to achieve great purpose.

3. Aim 3: Visionary Aim:

a. Describe the long-term vision for the project. Explain how the broader concept could have an impact if fully realized.

b. Examples include:

i. Challenging an existing paradigm or clinical practice.

ii. Addressing a major barrier in a field.

iii. Enabling a new experimental capability or research approach.

iFP- Additional info

Individual Final Project, Additional Information and resources

Group Final Project