Week 01 HW: Pre-Prep Week 2

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 polymerases depends on their type, as Human DNA has mechanisms for proofreading that other organisms’ DNA lacks. The error rate for DNA polymerase is 1 in every 10^{7 base pairs. As compared to the human genome size of 6 X 10}9 base pairs. The mechanisms include mismatch repair, base excision repair, nucleotide excision repair, NHEJ, HR, damage checkpoint, and some tolerance mechanisms. This is how biology deals with discrepancies via multiple mechanism before-during-after DNA replication and the cell cycle.

Prompts - What is the length of human DNA base pairs? what the different ways in which DNA is proffread nd repaired?

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?

As each amino acid has 3 codons mostly and proteins are made of a combination of 20 amino acids. With a lot of amino acids with multiple codons , the ways to code are astronomically huge.
Codes at times fail to work as the amino acids might not form the protein folding form, mRNA fold, tRNA, ribosome as the same ones required in the cells. That is where protein design and coding gets complex to gain the exact intended expression.
https://doi.org/10.3390/biom14010132

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

phosphoramidite chemistry

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

As error accumulates on every nucleotide addition , so instead assembling enzymatically i better.

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

Again error rates decrease final yields and limit purification, which also leads to yield loss.

10 AA in animals and What do you think about Lysine Contingency?

The ten essential amino acids in animals are histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arginine;
“lysine contingency” means the biological dependence of animals on external lysine supply due to the absence of lysine biosynthetic pathways. This creates a dependency that can be exploited in evolution and synthetic biology for metabolic control and biocontainment.

My take on Lysine Contingency is - This can certainly be a powerful tool to implement biocontainment when we decide to inhabit or spread bioforms on extraterrestrial land. But it also threatens the very existence of human & animal bodies, in turn showing our dependency on plants. It certainly makes me further appreciate the importance of the food chain and the evolutionary process of life forms. Prompts : 10 amino acids in all Animals? What does Lysine Contingency mean?

Week 01 HW: Principles and Practices

1. First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about.

As I have been thinking about the different ways synthetic biology can help with menstruation and its complexities (painful, irregular bleeding) when it comes to people with PCOS and endometriosis. I want to develop an autonomous endometrial gene circuit that senses estrogen or progesterone peaks and locally regulates endometrial growth. The goal is non-hormonal, reversible control of menstrual bleeding, which could prevent heavy bleeding or abnormal endometrial proliferation while minimizing systemic hormone exposure.

WHY - Hormonal regulation comes with lots of cons, and in general, having control over the reproductive cycle can benefit the population and quality of life overall.
During iGEM Startups 2024, I worked theoretically on Femflux, an aptamer biosensor that analyzes interstitial fluid to analyze estrogen and later predicts & helps peri-menopausal women. I realised that as people experiencing menstrual cycles, we have little to no control over it, despite it affecting us beyond reproduction. While interviewing multiple indian women, a common answer towards the want to use a product like this was- WHY? As if the choice to prevent or help our health is not enough of a reason to want tech development. The answers were sad, showing the priority of healthtech use for them was an afterthought, unnecessary even.
So, I would ike to explore beyond current methods for controlling bleeding (hormonal contraception, surgery) that are either systemic or irreversible. The power to bring back control and want.

This system could give women personalized, cycle-specific control with minimal side effects.
It could also integrate with broader synthetic biology tools like hormone biosensors and predictive health platforms, potentially linking to osteoporosis prevention or menopause health monitoring.

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

Main Goal: Ensure the autonomous endometrial synthetic switch is safe, ethical, equitable, and respects women’s autonomy, while minimizing potential misuse or exploitation.
Safety & Non-Malfeasance

• Prevent off-target gene silencing outside the endometrium.

• Ensure reversibility and prevent long-term adverse effects.

  Equity, Autonomy & Access Control

• Ensure that women can choose whether to use the system themselves.

• Prevent exploitation by third parties (e.g., coercive use by healthcare providers, employers, insurers).

• Protect patient privacy and decision-making authority.

  Research Transparency & Constructive Use Encourage safe development and sharing of protocols to advance science while preventing misuse

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

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

Prioritize:

• Primary: Ethical Consent, Autonomy & Self-Use (most directly protects women, prevents exploitation).

• Secondary: Regulatory Preclinical (ensures safety).

Trade-offs:

• Assumes users can understand synthetic biology risks; additional education required.

Audience:

• FDA / EMA: Set safety, reversibility, and access rules

• Academic & Industry Labs: Comply with transparency and security standards

• Hospitals / Patient Advocacy Groups: Implement consent and self-use protocols

Johns Hopkins Medicine Researchers Find Early Success Using Endometrial mRNA Therapy to Treat Infertility | Johns Hopkins Medicine

Hormonal control of Menstrual bleeding https://www.youtube.com/watch?v=5q4ExatWfUU

Femflux https://docs.google.com/presentation/d/1Ew0E1LpoUeQWVf2WQQrztRrIYIA44LLRTVOx55lRuIE/edit?usp=sharing

Week 02 HW: DNA Read Write & Edit

Part 01: Restriction Digestion Art

Gel Image produced via Benchling to produced a rendition of the logo of a pop band called BTS.

3.1 Which protein have you chosen and why? Using one of the tools described in recitation (NCBI, UniProt, google), obtain the protein sequence for the protein you chose. Below is the protein sequence for ESR2 Human estrogen beta using UniProt. I have chosen this protein as it has been

sp|Q92731|ESR2_HUMAN Estrogen receptor beta OS=Homo sapiens OX=9606 GN=ESR2 PE=1 SV=2 MDIKNSPSSLNSPSSYNCSQSILPLEHGSIYIPSSYVDSHHEYPAMTFYSPAVMNYSIPS NVTNLEGGPGRQTTSPNVLWPTPGHLSPLVVHRQLSHLYAEPQKSPWCEARSLEHTLPVN RETLKRKVSGNRCASPVTGPGSKRDAHFCAVCSDYASGYHYGVWSCEGCKAFFKRSIQGH NDYICPATNQCTIDKNRRKSCQACRLRKCYEVGMVKCGSRRERCGYRLVRRQRSADEQLH CAGKAKRSGGHAPRVRELLLDALSPEQLVLTLLEAEPPHVLISRPSAPFTEASMMMSLTK LADKELVHMISWAKKIPGFVELSLFDQVRLLESCWMEVLMMGLMWRSIDHPGKLIFAPDL VLDRDEGKCVEGILEIFDMLLATTSRFRELKLQHKEYLCVKAMILLNSSMYPLVTATQDA DSSRKLAHLLNAVTDALVWVIAKSGISSQQQSMRLANLLMLLSHVRHASNKGMEHLLNMK CKNVVPVYDLLLEMLNAHVLRGCKSSITGSECSPAEDSKSKEGSQNPQSQ

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

reverse translation of sp|Q92731|ESR2_HUMAN Estrogen receptor beta OS=Homo sapiens OX=9606 GN=ESR2 PE=1 SV=2 to a 1590 base sequence of most likely codons. atggatattaaaaacagcccgagcagcctgaacagcccgagcagctataactgcagccag agcattctgccgctggaacatggcagcatttatattccgagcagctatgtggatagccat catgaatatccggcgatgaccttttatagcccggcggtgatgaactatagcattccgagc aacgtgaccaacctggaaggcggcccgggccgccagaccaccagcccgaacgtgctgtgg ccgaccccgggccatctgagcccgctggtggtgcatcgccagctgagccatctgtatgcg gaaccgcagaaaagcccgtggtgcgaagcgcgcagcctggaacataccctgccggtgaac cgcgaaaccctgaaacgcaaagtgagcggcaaccgctgcgcgagcccggtgaccggcccg ggcagcaaacgcgatgcgcatttttgcgcggtgtgcagcgattatgcgagcggctatcat tatggcgtgtggagctgcgaaggctgcaaagcgttttttaaacgcagcattcagggccat aacgattatatttgcccggcgaccaaccagtgcaccattgataaaaaccgccgcaaaagc tgccaggcgtgccgcctgcgcaaatgctatgaagtgggcatggtgaaatgcggcagccgc cgcgaacgctgcggctatcgcctggtgcgccgccagcgcagcgcggatgaacagctgcat tgcgcgggcaaagcgaaacgcagcggcggccatgcgccgcgcgtgcgcgaactgctgctg gatgcgctgagcccggaacagctggtgctgaccctgctggaagcggaaccgccgcatgtg ctgattagccgcccgagcgcgccgtttaccgaagcgagcatgatgatgagcctgaccaaa ctggcggataaagaactggtgcatatgattagctgggcgaaaaaaattccgggctttgtg gaactgagcctgtttgatcaggtgcgcctgctggaaagctgctggatggaagtgctgatg atgggcctgatgtggcgcagcattgatcatccgggcaaactgatttttgcgccggatctg gtgctggatcgcgatgaaggcaaatgcgtggaaggcattctggaaatttttgatatgctg ctggcgaccaccagccgctttcgcgaactgaaactgcagcataaagaatatctgtgcgtg aaagcgatgattctgctgaacagcagcatgtatccgctggtgaccgcgacccaggatgcg gatagcagccgcaaactggcgcatctgctgaacgcggtgaccgatgcgctggtgtgggtg attgcgaaaagcggcattagcagccagcagcagagcatgcgcctggcgaacctgctgatg ctgctgagccatgtgcgccatgcgagcaacaaaggcatggaacatctgctgaacatgaaa tgcaaaaacgtggtgccggtgtatgatctgctgctggaaatgctgaacgcgcatgtgctg cgcggctgcaaaagcagcattaccggcagcgaatgcagcccggcggaagatagcaaaagc aaagaaggcagccagaacccgcagagccag

3.3. Codon optimization.
Optimisation performed using Twist biosciences. Name,Original sequence,Flank 5’,Optimized sequence,Flank 3’,Organism of expression,Type,Preserved regions,Restriction sites,Sites only,Length,Optimized,Issues ESR2_Human_Estrogen_Receptor_Beta,ATGGATATTAAAAACAGCCCGAGCAGCCTGAACAGCCCGAGCAGCTATAACTGCAGCCAGAGCATTCTGCCGCTGGAACATGGCAGCATTTATATTCCGAGCAGCTATGTGGATAGCCATCATGAATATCCGGCGATGACCTTTTATAGCCCGGCGGTGATGAACTATAGCATTCCGAGCAACGTGACCAACCTGGAAGGCGGCCCGGGCCGCCAGACCACCAGCCCGAACGTGCTGTGGCCGACCCCGGGCCATCTGAGCCCGCT,,ATGGATATCAAGAACTCACCCTCTAGCCTGAACTCTCCATCCTCCTACAACTGCTCCCAGAGCATCCTGCCCCTGGAACACGGCAGCATCTACATCCCCTCATCCTATGTGGACAGCCACCACGAATACCCTGCCATGACCTTCTACTCCCCAGCTGTGATGAACTACTCCATTCCCTCCAATGTGACCAACCTGGAGGGAGGCCCTGGGAGGCAGACAACCTCTCCCAATGTGCTGTGGCCCACCCCAGGGCACCTGAGCCCCCTGGTGGTGCACAGGCAGCTGTCTCACCTCTATGCTGAGCCCCAGAAGAGCCCCTGGTGTGAGGCCAGAAGCCTGGAGCACACCCTGCCTGTGAACCGGGAAACCCTGAAGAGGAAGGTCTCTGGGAACCGCTGTGCCTCTCCTGTGACTGGGCCAGGCAGCAAGAGAGATGCCCACTTCTGTGCCGTGTGCTCTGACTATGCCTCTGGCTACCACTATGGGGTGTGGTCCTGTGAGGGCTGCAAGGCCTTCTTCAAGAGAAGCATCCAGGGGCACAATGACTACATCTGCCCAGCCACCAACCAGTGCACCATTGACAAGAACAGGAGGAAGAGCTGCCAGGCCTGCAGGCTGAGGAAGTGCTATGAGGTGGGCATGGTGAAATGTGGGAGCAGGCGGGAGCGCTGTGGCTACCGCCTGGTGCGGCGGCAGAGGAGTGCTGATGAGCAGCTGCACTGTGCAGGGAAGGCCAAGAGATCTGGAGGCCACGCACCCCGGGTGCGGGAGCTGCTGCTGGATGCCCTGAGCCCTGAGCAGCTGGTGCTGACCCTGCTGGAGGCTGAGCCTCCTCACGTGCTGATCAGCCGGCCCTCTGCCCCCTTCACTGAGGCCAGCATGATGATGAGCCTGACCAAGCTGGCTGACAAGGAGCTGGTGCATATGATCAGTTGGGCCAAGAAGATCCCTGGCTTTGTGGAGCTGTCCCTCTTTGACCAGGTGCGGCTGCTGGAGAGCTGCTGGATGGAGGTGCTGATGATGGGGCTGATGTGGAGGAGCATTGACCATCCTGGGAAGCTGATCTTTGCCCCTGACCTGGTGCTGGACAGGGATGAGGGGAAGTGTGTGGAGGGCATCCTGGAGATTTTTGACATGCTGCTGGCCACCACATCCAGGTTCCGGGAGCTGAAGCTGCAGCACAAGGAGTACCTGTGTGTGAAGGCCATGATCCTGCTCAACTCCTCCATGTACCCTCTGGTGACTGCCACCCAGGATGCTGACAGCAGCAGGAAGCTGGCCCACCTGCTGAATGCTGTGACTGATGCCCTGGTGTGGGTGATTGCCAAGTCTGGAATCTCCTCCCAGCAGCAGAGCATGCGGCTGGCCAACCTGCTGATGCTGCTGAGCCATGTCCGGCATGCCTCCAACAAGGGGATGGAGCACCTGCTGAACATGAAGTGCAAGAATGTGGTGCCCGTCTATGACCTGCTGCTGGAGATGCTGAATGCCCACGTGCTGAGAGGCTGCAAGAGCTCCATAACTGGGTCTGAGTGCTCCCCAGCAGAGGATTCCAAATCCAAGGAGGGATCCCAGAATCCCCAGAGCCAG,TGA,Homo sapiens (9606),Other protein type,,,false,1593,true,

3.4. What technologies could be used to produce this protein from your DNA?

Cell free method, Cell dependent method ( plasmid with promoter appropriate to mamallian/bacterial system) The plasmid can then be transfected to human cell line like HEK293 or transformed into E.coli for cheaper expression. Cell free method has all the required ribosomes, rnas, polymerases and amino acids. It is fast and does’t require live cells. Though not sure if its suitable for ESR2 function.

Part 4: Prepare a Twist DNA Synthesis Order Peformed in as part of final project .

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

I would want to sequence the DNA region responsible for endometrial lining proliferation. I am interested in analysing inducers of profliferation and further check mechanism pathways that cause over-proliferation like endometriosis, or even understand what mechanism in the dna might be especially effected in pcos patients . (ii) What technology or technologies would you use to perform sequencing on your DNA and why? thrid gen nanopore sequencing that will capture vairants that other tech might miss.

What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? What are the essential steps of your chosen sequencing technology? How does it decode the bases of your DNA sample (base calling)? What is the output of your chosen sequencing technology?

I would collect endometrial biopsy tissue ( For endometriosis: 1. eutopic endo sample , 2. eutopic endo sample, For PCOS : 3. follicular phase sample 4. luteal phase sample ) I would extract genomic DNA and do DNA sequencing using Nanopore. As epigentic pathway also plays important role in pathology of endomtetiosis, i will do methylation profiling. The expected output for the project will be : 1. FASTQ files that shows structural variants, potential genetic targets. 2. Methylation regions between healthy and disease tissue

5.2 DNA Write (i) What DNA would you want to synthesize (e.g., write) and why? These could be individual genes, clusters of genes or genetic circuits, whole genomes, and beyond.

I was thinking about targeting specific gene targets based on analysis of the FASTQ & methylation pathway to knock out & knock in genes. This would help me come with

(ii) What technology or technologies would you use to perform this DNA synthesis and why? Also answer the following questions:

What are the essential steps of your chosen sequencing methods? What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability? 5.3 DNA Edit & Read (i) What DNA would you want to edit and why? In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why?

Colossal Biosciences Inc., a biotechnology company using genetic engineering to de-extinct various historic animals such as the woolly mammoth, dodo, and dire wolf. (ii) What technology or technologies would you use to perform these DNA edits and why? Also answer the following questions:

How does your technology of choice edit DNA? What are the essential steps? What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing? What are the limitations of your editing methods (if any) in terms of efficiency or precision?

My answer to all these questions cover the 3 projects i proposed at the start of the course - link is here : Projects proposed for HTGAA

Week 03 HW: Lab Automation

Part 01: Opentron python file to create a image. Here is my code for the image below alongwith the prerequisitve codes before and after the - I used Sonnet 4.6 for the task.

from opentrons import types

metadata = { ‘author’: ’tanishka’, ‘protocolName’: ‘recreate img of an eye’, ‘description’: ‘’, ‘source’: ‘HTGAA 2026 Opentrons Lab’, ‘apiLevel’: ‘2.20’ }

##############################################################################

Robot deck setup constants - don’t change these

##############################################################################

TIP_RACK_DECK_SLOT = 9 COLORS_DECK_SLOT = 6 AGAR_DECK_SLOT = 5 PIPETTE_STARTING_TIP_WELL = ‘A1’

well_colors = { ‘B1’ : ‘Red’, ‘A1’ : ‘Orange’, ‘C1’ : ‘Green’ }

def run(protocol): ##############################################################################

Load labware, modules and pipettes

##############################################################################

Tips

tips_20ul = protocol.load_labware(‘opentrons_96_tiprack_20ul’, TIP_RACK_DECK_SLOT, ‘Opentrons 20uL Tips’)

Pipettes

pipette_20ul = protocol.load_instrument(“p20_single_gen2”, “right”, [tips_20ul])

Modules

temperature_module = protocol.load_module(’temperature module gen2’, COLORS_DECK_SLOT)

Temperature Module Plate

temperature_plate = temperature_module.load_labware(‘opentrons_96_aluminumblock_generic_pcr_strip_200ul’, ‘Cold Plate’)

Choose where to take the colors from

color_plate = temperature_plate

Agar 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)

##############################################################################

Patterning

##############################################################################

def location_of_color(color_string): for well, color in well_colors.items(): if color.lower() == color_string.lower(): return color_plate[well] raise ValueError(f"No well found with color {color_string}")

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)

YOUR CODE HERE

WELL_SPACING_MM = 2.25 SAFE_RADIUS_MM = 38.0

row_order = [ ‘A’,‘B’,‘C’,‘D’,‘E’,‘F’,‘G’,‘H’,‘I’,‘J’,‘K’,‘L’,‘M’,‘N’,‘O’,‘P’, ‘Q’,‘R’,‘S’,‘T’,‘U’,‘V’,‘W’,‘X’,‘Y’,‘Z’,‘AA’,‘AB’,‘AC’,‘AD’,‘AE’,‘AF’ ]

MAX_EXTENT = 48 * WELL_SPACING_MM / 2 SCALE = SAFE_RADIUS_MM / MAX_EXTENT

def well_to_xy(well_str): col_str = ‘’.join(filter(str.isdigit, well_str)) row_str = ‘’.join(filter(str.isalpha, well_str)) col = int(col_str) row = row_order.index(row_str) x = (col - 24.5) * WELL_SPACING_MM * SCALE y = (16 - row) * WELL_SPACING_MM * SCALE return x, y

F1 source wells - dark red outline

red_wells_F1 = [ ‘AB4’,‘AB5’,‘AB6’,‘AB7’,‘AB8’,‘AB9’,‘AB10’,‘AB11’,‘AB16’,‘AB17’, ‘AB19’,‘AB20’,‘AB21’,‘AB22’,‘AB23’,‘AB24’,‘AB25’,‘AB26’,‘AB29’,‘AB32’, ‘AB35’,‘AB41’,‘AB43’,‘AB44’,‘AB45’, ‘AA4’,‘AA5’,‘AA6’,‘AA7’,‘AA8’,‘AA9’,‘AA10’,‘AA11’,‘AA15’,‘AA16’, ‘AA17’,‘AA18’,‘AA19’,‘AA20’,‘AA21’,‘AA25’,‘AA32’,‘AA36’,‘AA38’,‘AA39’, ‘AA43’,‘AA44’,‘AA45’, ‘Z4’,‘Z5’,‘Z6’,‘Z7’,‘Z13’,‘Z15’,‘Z16’,‘Z17’,‘Z18’,‘Z19’,‘Z20’,‘Z21’, ‘Z25’,‘Z26’,‘Z28’,‘Z43’,‘Z44’,‘Z45’, ‘Y7’,‘Y8’,‘Y9’,‘Y10’,‘Y15’,‘Y16’,‘Y17’,‘Y22’,‘Y23’,‘Y25’,‘Y26’,‘Y27’, ‘Y43’,‘Y44’,‘Y45’, ‘X9’,‘X10’,‘X11’,‘X20’,‘X21’,‘X22’,‘X23’,‘X25’,‘X27’,‘X44’,‘X45’, ‘W19’,‘W20’,‘W21’,‘W22’,‘W23’,‘W42’,‘W43’,‘W44’,‘W45’, ‘V18’,‘V19’,‘V20’,‘V21’,‘V22’,‘V23’,‘V25’,‘V43’,‘V44’,‘V45’, ‘U18’,‘U19’,‘U20’,‘U31’,‘U33’,‘U44’,‘U45’, ‘T4’,‘T16’,‘T18’,‘T19’,‘T20’,‘T21’,‘T30’,‘T32’,‘T34’,‘T45’, ‘S9’,‘S15’,‘S16’,‘S17’,‘S22’,‘S35’,‘S36’,‘S45’, ‘R15’,‘R17’,‘R19’,‘R24’,‘R25’,‘R26’,‘R27’,‘R36’,‘R37’,‘R38’,‘R43’, ‘Q18’,‘Q20’,‘Q26’,‘Q30’,‘Q37’,‘Q38’,‘Q39’, ‘P16’,‘P33’,‘P38’,‘P40’, ‘N20’,‘M9’,‘M27’,‘L9’,‘L30’,‘L32’,‘L33’,‘L34’, ‘K9’,‘K11’,‘K31’,‘K32’,‘K33’,‘K34’,‘K45’, ‘J34’,‘J37’,‘I17’,‘I43’,‘H44’,‘H45’,‘G10’,‘G16’, ‘F27’,‘F28’,‘E18’,‘E29’,‘E30’,‘E31’,‘E32’ ]

C1 source wells - light orange/skin tone fill

red_wells_C1 = [ ‘AB12’,‘AB13’,‘AB14’,‘AB15’,‘AB18’,‘AB27’,‘AB28’,‘AB30’,‘AB31’, ‘AB33’,‘AB34’,‘AB36’,‘AB37’,‘AB38’,‘AB39’,‘AB40’,‘AB42’, ‘AA12’,‘AA13’,‘AA14’,‘AA22’,‘AA23’,‘AA24’,‘AA26’,‘AA27’,‘AA28’, ‘AA29’,‘AA30’,‘AA31’,‘AA33’,‘AA34’,‘AA35’,‘AA37’,‘AA40’,‘AA41’,‘AA42’, ‘Z8’,‘Z9’,‘Z10’,‘Z11’,‘Z12’,‘Z14’,‘Z22’,‘Z23’,‘Z24’,‘Z27’,‘Z29’, ‘Z30’,‘Z31’,‘Z32’,‘Z33’,‘Z34’,‘Z35’,‘Z36’,‘Z37’,‘Z38’,‘Z39’,‘Z40’,‘Z41’,‘Z42’, ‘Y5’,‘Y12’,‘Y13’,‘Y14’,‘Y19’,‘Y20’,‘Y24’,‘Y28’,‘Y29’,‘Y30’,‘Y31’, ‘Y32’,‘Y33’,‘Y34’,‘Y35’,‘Y36’,‘Y37’,‘Y38’,‘Y39’,‘Y40’,‘Y41’,‘Y42’, ‘X6’,‘X8’,‘X12’,‘X14’,‘X15’,‘X16’,‘X18’,‘X24’,‘X26’,‘X28’,‘X29’, ‘X30’,‘X31’,‘X32’,‘X33’,‘X34’,‘X35’,‘X36’,‘X37’,‘X38’,‘X39’,‘X40’,‘X41’,‘X42’,‘X43’, ‘W8’,‘W9’,‘W10’,‘W11’,‘W12’,‘W13’,‘W17’,‘W18’,‘W24’,‘W25’,‘W26’, ‘W27’,‘W28’,‘W29’,‘W30’,‘W31’,‘W32’,‘W33’,‘W34’,‘W35’,‘W36’,‘W37’,‘W38’,‘W39’,‘W40’,‘W41’, ‘V9’,‘V10’,‘V11’,‘V12’,‘V13’,‘V16’,‘V17’,‘V24’,‘V26’,‘V27’,‘V28’, ‘V29’,‘V30’,‘V31’,‘V32’,‘V33’,‘V34’,‘V35’,‘V36’,‘V37’,‘V38’,‘V39’,‘V40’,‘V41’,‘V42’, ‘U5’,‘U8’,‘U10’,‘U11’,‘U15’,‘U17’,‘U21’,‘U22’,‘U23’,‘U24’,‘U25’, ‘U26’,‘U27’,‘U28’,‘U29’,‘U30’,‘U32’,‘U34’,‘U35’,‘U36’,‘U37’,‘U38’,‘U39’,‘U40’,‘U41’,‘U42’,‘U43’, ‘T6’,‘T7’,‘T9’,‘T10’,‘T11’,‘T22’,‘T23’,‘T24’,‘T25’,‘T26’,‘T27’, ‘T28’,‘T31’,‘T33’,‘T35’,‘T36’,‘T37’,‘T38’,‘T39’,‘T40’,‘T41’,‘T42’,‘T43’,‘T44’, ‘S7’,‘S8’,‘S18’,‘S19’,‘S20’,‘S21’,‘S23’,‘S24’,‘S25’,‘S26’,‘S27’, ‘S28’,‘S32’,‘S33’,‘S37’,‘S38’,‘S39’,‘S41’,‘S42’,‘S43’,‘S44’, ‘R5’,‘R8’,‘R16’,‘R20’,‘R21’,‘R22’,‘R23’,‘R30’,‘R32’,‘R33’,‘R34’, ‘R39’,‘R40’,‘R41’,‘R42’,‘R44’,‘R45’, ‘Q7’,‘Q16’,‘Q17’,‘Q21’,‘Q24’,‘Q25’,‘Q29’,‘Q32’,‘Q34’,‘Q35’,‘Q36’, ‘Q40’,‘Q41’,‘Q42’,‘Q43’,‘Q44’,‘Q45’, ‘P21’,‘P25’,‘P26’,‘P28’,‘P29’,‘P34’,‘P35’,‘P37’,‘P39’,‘P41’,‘P43’,‘P44’,‘P45’, ‘O6’,‘O12’,‘O16’,‘O17’,‘O18’,‘O22’,‘O23’,‘O29’,‘O34’,‘O37’,‘O39’, ‘O40’,‘O41’,‘O42’,‘O43’,‘O44’,‘O45’, ‘N10’,‘N16’,‘N17’,‘N23’,‘N24’,‘N25’,‘N27’,‘N34’, ‘M5’,‘M16’,‘M17’,‘M21’,‘M25’,‘M26’,‘M28’,‘M31’,‘M32’,‘M33’,‘M39’, ‘M40’,‘M41’,‘M42’,‘M43’, ‘L6’,‘L7’,‘L8’,‘L11’,‘L16’,‘L17’,‘L18’,‘L20’,‘L21’,‘L23’,‘L24’, ‘L25’,‘L26’,‘L27’,‘L28’,‘L29’,‘L31’,‘L35’,‘L36’,‘L37’,‘L39’,‘L40’, ‘L41’,‘L42’,‘L43’,‘L44’, ‘K4’,‘K6’,‘K7’,‘K8’,‘K16’,‘K17’,‘K21’,‘K23’,‘K24’,‘K25’,‘K26’, ‘K27’,‘K28’,‘K29’,‘K30’,‘K35’,‘K36’,‘K37’,‘K38’,‘K39’,‘K40’,‘K41’,‘K42’,‘K43’,‘K44’, ‘J4’,‘J6’,‘J7’,‘J8’,‘J9’,‘J10’,‘J11’,‘J14’,‘J18’,‘J19’,‘J24’, ‘J25’,‘J26’,‘J27’,‘J28’,‘J29’,‘J30’,‘J31’,‘J32’,‘J33’,‘J35’,‘J36’, ‘J38’,‘J39’,‘J40’,‘J41’,‘J42’,‘J43’,‘J45’, ‘I5’,‘I6’,‘I10’,‘I11’,‘I12’,‘I13’,‘I14’,‘I15’,‘I19’,‘I20’,‘I21’, ‘I22’,‘I33’,‘I34’,‘I35’,‘I36’,‘I37’,‘I38’,‘I39’,‘I40’,‘I41’,‘I42’,‘I44’,‘I45’, ‘H5’,‘H10’,‘H11’,‘H12’,‘H13’,‘H14’,‘H15’,‘H21’,‘H22’,‘H24’,‘H25’, ‘H28’,‘H29’,‘H35’,‘H36’,‘H37’,‘H38’,‘H39’,‘H40’,‘H41’,‘H42’, ‘G5’,‘G11’,‘G15’,‘G22’,‘G23’,‘G24’,‘G25’,‘G26’,‘G28’,‘G29’,‘G30’, ‘G33’,‘G34’,‘G38’,‘G39’,‘G40’,‘G41’,‘G42’,‘G44’,‘G45’, ‘F10’,‘F15’,‘F16’,‘F22’,‘F23’,‘F24’,‘F25’,‘F26’,‘F29’,‘F30’,‘F31’, ‘F33’,‘F34’,‘F35’,‘F38’,‘F44’, ‘E20’,‘E22’,‘E23’,‘E26’,‘E27’,‘E28’,‘E33’,‘E34’,‘E35’,‘E42’,‘E45’ ]

VOLUME_PER_DOT = 0.2 WELLS_PER_TIP = int(20 / VOLUME_PER_DOT)

Dispense F1 wells in Red (dark outline)

for batch_start in range(0, len(red_wells_F1), WELLS_PER_TIP): batch = red_wells_F1[batch_start : batch_start + WELLS_PER_TIP] aspirate_vol = len(batch) * VOLUME_PER_DOT

pipette_20ul.pick_up_tip()
pipette_20ul.aspirate(aspirate_vol, location_of_color('Orange'))

for well in batch:
  x, y = well_to_xy(well)
  adjusted_location = center_location.move(types.Point(x=x, y=y))
  dispense_and_detach(pipette_20ul, VOLUME_PER_DOT, adjusted_location)

pipette_20ul.drop_tip()

Dispense C1 wells in Orange

for batch_start in range(0, len(red_wells_C1), WELLS_PER_TIP): batch = red_wells_C1[batch_start : batch_start + WELLS_PER_TIP] aspirate_vol = len(batch) * VOLUME_PER_DOT

pipette_20ul.pick_up_tip()
pipette_20ul.aspirate(aspirate_vol, location_of_color('Red'))

for well in batch:
  x, y = well_to_xy(well)
  adjusted_location = center_location.move(types.Point(x=x, y=y))
  dispense_and_detach(pipette_20ul, VOLUME_PER_DOT, adjusted_location)

pipette_20ul.drop_tip()

Here is the image i recreated : 

Here is the python output from googlecolab:

Part 02: 1: Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications. Bryant et al. (2023). AssemblyTron: Flexible Automation of DNA Assembly with Opentrons OT-2 Lab Robots. Synthetic Biology, Volume 8, Issue 1. DOI: 10.1093/synbio/ysac032 While many tools exist to automate the Design, Test, and Learn steps of the Design-Build-Test-Learn (DBTL) cycle, the Build step (which involves physically assembling DNA) remains largely manual, low throughput and error-prone. AssemblyTron is an open-source Python software package that integrates DNA assembly design software outputs with the Opentrons OT-2 liquid handling robot, enabling automated execution of DNA assembly protocols with minimal human intervention.

2: Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details. I will create an assay to run on nebula to verify if the synthesised nanobody is folding properly & GFP is quantified.

Week 04 HW: Protein Design Part 1

Part A :

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)

No. of AA molecules in 500gm of meat = Proteins consist of amino acids linked by peptide bonds, losing ~18 Da (water) per bond, so ~0.9–1 g protein yields ~1 mol amino acid residues (using your ~100 Da average residue weight). Protein in 500 g meat: ~120 g average (24% protein content). Moles of amino acids: 120 g / 100 g/mol = 1.2 mol. Molecules: 1.2 mol × 6.022 × 10²³ = ~7.2 × 10²³ (adjusts to ~2.3 × 10²⁴ if using precise 110 Da avg

2. Why do humans eat beef but do not become a cow, eat fish but do not become fish? Because the proteins consumed are broken down into amino acids and are used to manufacture human proteins as dictated by our DNA codes.

3. Why are there only 20 natural amino acids? These amino acids have been enough to support and compose life, so there is no need for more yet.

4. Can you make other non-natural amino acids? Design some new amino acids. Yes, we can. We can use any amino acid’s molecular structure to make synthetic AAs.Eg. changing ethanol’s structure by adding a hydroxyl group.

5. Where did amino acids come from before enzymes that make them, and before life started? The formations of amino acids before enzymes are thought to be due to extreme reactive environment. During the early days of earth, it was full of reactive ammonia, gydrogen, methane with energy from sun & lightning. These created conditions required for complex AA formation like thermodynmic & kinetic energy.

6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect? D-amino acids means dextro that is anti-clockwise. So it will make left handed amino acid helix.

7. Can you discover additional helices in proteins? Helical structures can be discovered using various method like NMR spectroscopy, X-ray crystallography, computational protein modelling.

8. Why are most molecular helices right-handed? As proteins are primarily made of right-handed AA causing stable hydrogen bonding.

9. Why do β-sheets tend to aggregate? What is the driving force for β-sheet aggregation? B-sheets have strong intermolecular bonding of hydrogen with peptide backbone. Their side chains are hydrophobic causing stable sheet via van der Waals interaction forming agregations.

10. Why do many amyloid diseases form β-sheets? Amyloid dieade tend to be due to accumulation of insoluble fibril agregates. These are in fact beta sheets that disrupt cell function.

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

Week 05 HW: Protein Design Part II

Part 1: Generate Binders with PepMLM The target is human SOD1 protein (UniProt P00441), focusing on A4V mutation, which is ALS.

Mutant SOD1 Sequence (A4V):

ATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

Using PepMLM-650M, four peptides of 12 amino acids were generated and compared against known SOD1-binding peptide FLYRWLPSRRGG.

PepMLM Confidence Scores Sequence Description Perplexity FLYRWLPSRRGG Real Binder — WHSPVVAVAHWE Sim 1 10.949699 WSVGWAAIAWWX Sim 2 16.027645 WRSYATAIALWK Sim 3 11.729657 WRYYATGAEWKE Sim 4 13.769973

Part 2: Evaluate Binders with AlphaFold3 Each peptide was modeled against the mutant SOD1 sequence using AlphaFold3 to assess structural docking and interface confidence (ipTM).

Structural Observations Localization: none of the peptides localized specifically to the A4V mutation site at the N-terminus. All peptides were primarily surface-bound. Binding Sites: Sim 3 (WRSYATAIALWK) and Sim 4 (WRYYATGAEWKE) both bound to a region that appears to be a potential polymerization site. Sim 2 (WSVGWAAIAWWX) engaged the β-barrel region. The wild-type binder also localized to a suspected polymerization site.

Eg. of AlphaFold3 Visualization

Part 3: Evaluate Properties in the PeptiVerse therapeutic potential (solubility, hemolysis, and affinity) of the generated sequences.

Peptide Property Comparison Peptide Sequence Solubility Hemolysis Affinity (pKd) MW (Da) Net Charge pI GRAVY ipTM pTM FLYRWLPSRRGG 1.000 0.047 5.96 (Weak) 1507.7 +2.76 11.71 -0.71 0.33 0.78 WHSPVVAVAHWE 1.000 0.048 5.07 (Weak) 1417.6 -1.06 6.02 0.18 0.28 0.78 WSVGWAAIAWWX 1.000 0.174 7.71 (Med) 1314.6 -0.24 5.53 0.78 0.37 0.76 WRSYATAIALWK 1.000 0.056 6.83 (Weak) 1465.7 +1.76 9.99 0.06 0.41 0.72 WRYYATGAEWKE 1.000 0.063 5.71 (Weak) 1559.7 -0.23 6.28 -1.44 0.31 0.86

Analysis of Results The observed ipTM values across the PepMLM-generated candidates range from 0.28 to 0.41, reflecting a low-to-moderate confidence in specific interface orientation. However, two PepMLM peptides outperformed the known binder (ipTM 0.33): WRSYATAIALWK (0.41) and WSVGWAAIAWWX (0.37).

There is a general correlation between structural confidence and affinity; the sequence with the highest affinity (WSVGWAAIAWWX, pKd: 7.71) also showed a strong ipTM. Interestingly, the highest ipTM - WRSYATAIALWK.

Selection Candidate: WRSYATAIALWK While WSVGWAAIAWWX has the highest raw affinity, WRSYATAIALWK offers a superior balance of properties. It provides the highest structural confidence (ipTM 0.41) while maintaining a much safer hemolysis profile (0.056) .

Part 4: Generate Optimized Peptides with moPPIt

moPPIt Generated Peptides Sequence Solubility Hemolysis Affinity MW (Da) Net Charge pI GRAVY GGTTTDDTKAES 1.000 0.054 4.20 1182.1 -2.24 4.05 -1.42 ATTGYCGCTMQN 1.000 0.018 5.39 1249.4 -0.22 5.55 -0.21 DEGYKKQKGQIQ 1.000 0.041 4.75 1421.6 +0.76 8.43 -2.23

Comparison and Evaluation The PepMLM peptides generally focus on high-confidence structural docking and balanced biophysical properties, leaning toward a safe profile while improving affinity over the baseline. In contrast, the moPPIt peptides prioritize diverse chemical spaces and targeted binding.

The moPPIt set introduces extreme charge variations, such as the highly acidic GGTTTDDTKAES (pI 4.05) and the polar-rich DEGYKKQKGQIQ, which differ significantly from the more hydrophobic PepMLM designs. To evaluate these before clinical studies, we would need to perform experimental circular dichroism (CD) to confirm peptide stability and surface plasmon resonance (SPR) to validate the targeted binding affinity at the specific A4V or dimer interface sites chosen during the moPPIt steering process.

Week 06 HW: Genetic Circuits Part I

Part one 1: What are some components in the Phusion High-Fidelity PCR Master Mix, and what is their purpose? Phusion DNA Polymerase, Nucleotides, Optimised Phusion HF Reaction Buffer with MgCl2 - All at 2X concentration Generates long templates with high accuracy and speed, unattainable with a single enzyme, as per the Thermo Scientific protocol. It states that the error rate of Phusion DNA Polymerase is determined to be 4.4 × 10-7 in Phusion HF Buffer, which is approximately 50-fold lower than that of Thermus aquaticus DNA polymerase, and 6-fold lower than that of Pyrococcus furiosus DNA polymerase. Attached below are graphs that support these claims by comparing with traditional polymerase. The annealing temperature is at 60 degrees for all. https://documents.thermofisher.com/TFS-Assets/LSG/brochures/phusion-high-fidelity-dna-polymerases-flyer.pdf https://documents.thermofisher.com/TFS-Assets/LSG/manuals/MAN0012771_Phusion_HiFi_PCR_MasterMix_100rxn_UG.pdf

2: What are some factors that determine primer annealing temperature during PCR? Length and composition of primers determine annealing temperature. https://pmc.ncbi.nlm.nih.gov/articles/PMC332522/

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 and in terms of when one may be preferable to the other. PCR is used for DNA synthesis and restriction digestion (RD) for fragmenting and recognising proteins/amplicons. Protocol Comparison : PCR requires thermal cycling to denature DNA, anneal primers, and extend new strands RD’s protocol involves incubating the DNA with the specific enzyme at its optimal temperature for a set time (30-60 minutes).

4: How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning? 1; Ensure that your DNA construct has orientation from 5’ to 3’ covering target region with around 15bp complementary overlap ends. 2; Digest parental template to decrease noise and measure DNA size & concentration via electrophoresis and nanodrop. This will help to get good amount of DNA concentration for vector. Refer established protocols to confirm.

5: How does the plasmid DNA enter the E. coli cells during transformation? Whe electrical current/pulses are applied there is pore formation in e.coli. this allows dna to enter via diffusion inside e.coli cells. The pores in cell membrane are temporary and close during recovery in warm media.

Part 2: Assignment:DNA assembly on Asimov Kernel Link : https://kernel.asimov.com/htgaa-2026/repositories/repository/5fa19bb5-9e6a-4bd1-a545-0a312898f1cc/folder/4aaa06dd-54b3-4d6f-a588-c11c452d0a97 1: Exploring the Bacterial Demos Repository

I explored the devices in the Bacterial Demos Repository to understand how genetic parts work together. I ran the Simulator on several example constructs using the instructions in the Info panel. Key observations:

• Constructs are built from modular parts: promoters, coding sequences (CDS), and terminators
• The simulator models protein expression over time based on transcription, translation, and degradation rates
• Changing simulator parameters (e.g. degradation rate, copy number) significantly affects the output curves
• The Repressilator construct in the Bacterial Demos repo showed clear oscillating protein expression across all three repressors, confirming the circuit produces the expected periodic behaviour

2: Recreating the Repressilator

The Repressilator is a synthetic genetic oscillator first described by Elowitz and Leibler (2000). It consists of three genes arranged in a mutually repressive loop.

Each protein represses the next promoter in the loop, creating a negative feedback cycle that produces oscillating protein expression.

Construct Diagram & Simulator results

The ossiclation didn’t match as expected at first, I played around a bit and worked with typical bacterial promoters and repressors to change and get desired effect. As seen in the simulator results it worked .

Construct 1: Simple Toggle Switch

Design Rationale This construct is a bistable toggle switch based on the Gardner et al. (2000) design. Two repressors (LacI and TetR) mutually repress each other:

• pLac drives tetR expression → TetR protein is produced
• pTet drives lacI expression → LacI protein is produced
• LacI represses pLac, TetR represses pTet

This creates two stable states:

• State A: LacI is high, TetR is low (LacI wins)
• State B: TetR is high, LacI is low (TetR wins) The system should remain locked in one state until an external signal (e.g. IPTG to inhibit LacI, or aTc to inhibit TetR) flips it to the other state.

Construct & Simulator Diagram

The simulator showed one protein dominating and the other being suppressed, confirming bistable behaviour.

Construct 2: Negative Autoregulation

Design Rationale This construct tests simple negative autoregulation — one of the most common regulatory motifs found in natural genomes. The LacI protein represses its own promoter (pLac), creating a self-limiting feedback loop.

Negative autoregulation has two known biological benefits: 1 It speeds up the response time of gene expression compared to unregulated expression 2 It reduces noise and cell-to-cell variability in protein levels

Construct Diagram and Simulator Results

The simulator showed LacI expression rising then plateauing at a low level, consistent with negative autoregulation.

Construct 3: Constitutive GFP Reporter

Design Rationale This is the simplest possible construct is a constitutive promoter driving GFP expression with no regulatory feedback of any kind. There is no repressor or activator involved.

This construct serves as a baseline control to compare against the regulated constructs above. Without any feedback, GFP should accumulate at a rate determined purely by:

• Transcription rate (set by the promoter strength)
• Translation rate (set by the ribosome binding site)
• Protein degradation rate

Construct and Simulator Results

The simulator showed GFP expression rising and reaching a stable plateau as expected. There was no oscillation, confirming this is a stable unregulated system. The plateau level was higher than the self-regulated LacI in Construct 2, which makes sense
without negative feedback there is nothing to limit accumulation beyond the natural degradation rate. This confirms that negative autoregulation (Construct 2) does meaningfully reduce steady state protein levels.

References

• Elowitz, M.B. & Leibler, S. (2000). A synthetic oscillatory network of transcriptional regulators. Nature, 403, 335–338.
• Gardner, T.S., Cantor, C.R. & Collins, J.J. (2000). Construction of a genetic toggle switch in Escherichia coli. Nature, 403, 339–342.

Week 07 HW: Genetic Circuits Part II

Part 1: Intracellular Artificial Neural Networks 1: What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? IANNs process analog signals instead of only ON/OFF outputs. They handle noisy data better and perform complex decisions using fewer genetic parts.

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. An IANN can be used in cancer therapy cells. The cell detects tumor biomarkers and releases a killing protein only when signal levels match cancer conditions. A limitation is high energy use, which may slow growth or cause mutations.

4: 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.

The first layer produces Csy4, which controls the second layer by inhibiting fluorescence output. Protein Y is produced only after both layers process the signals together.

Part 2: Fungal Materials 1: What are some examples of existing fungal materials and what are they used for? What are their advantages and disadvantages over traditional counterparts? 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? Fungal materials include mycelium leather and bio-composites for packaging and construction. They are biodegradable and low-cost but are less durable and sensitive to humidity compared to traditional materials.

Fungi can be engineered to sense environmental conditions like pH or nutrients and respond accordingly. Fungi are better than bacteria for complex proteins and strong 3D biomaterials because they are eukaryotic and form hyphal networks.

Assignment Part 3: First DNA Twist Order Have designed multiple twist orders for the final individual project and has been discussed with TA.

Week 09 HW: CELL FREE SYSTEMS

Part One: General and Lecturer-Specific Questions 1.

Cell-free protein synthesis gives better control over reaction conditions and allows direct addition of molecules like inhibitors or non-natural amino acids. It is useful for producing toxic proteins and for rapid protein prototyping.

A cell-free system contains lysate with ribosomes and enzymes, a DNA/mRNA template, amino acids, and buffer salts. These components work together to produce proteins from genetic instructions.

ATP regeneration is important because protein synthesis uses large amounts of energy. A creatine phosphate–creatine kinase system can recycle ADP into ATP and maintain continuous protein production.

Prokaryotic systems are fast and cheap, making them suitable for simple proteins like GFP. Eukaryotic systems are better for complex proteins like antibodies because they support proper folding and modifications.

Membrane proteins need lipid structures like liposomes or nanodiscs to fold correctly. The main challenge is balancing detergent levels to prevent aggregation without damaging the system.

Low protein yield may result from poor DNA quality, RNase contamination, or incorrect magnesium concentration. These can be fixed by purifying DNA, adding RNase inhibitors, and optimizing salt levels.

Part two :

Kate Adamala - Synthetic Minimal Cell

Function: Endometriosis pain relief on demand

A synthetic minimal cell that senses elevated prostaglandin E2 (a key inflammatory marker in endometriosis) and responds by synthesizing and releasing ibuprofen precursor enzymes locally within the peritoneal cavity. Unlike systemic NSAIDs, this SMC delivers anti-inflammatory action only where inflammation is detected, reducing systemic side effects.

Components:

• POPC/cholesterol lipid bilayer for stability in peritoneal fluid
• PURE system encapsulated inside
• α-hemolysin nanopores for prostaglandin sensing and enzyme secretion
• pPGE2-responsive promoter driving COX-2 inhibitor peptide synthesis
• sfGFP reporter fused to output peptide for activity confirmation

Experimental validation:

• Fluorometry to confirm GFP-tagged peptide production upon PGE2 stimulation
• ELISA to measure prostaglandin reduction in treated vs untreated samples
• Cytotoxicity assay on peritoneal cell lines to confirm biocompatibility

Peter Nguyen - Architectural Textile

Pitch: A wearable menstrual health textile that bioluminesces to signal the onset of infection or abnormal pH associated with bacterial vaginosis or endometriosis flares, requiring no battery or external device.

Mechanism:

• Porous underwear-lining mesh embedded with microcapsules containing freeze-dried TX-TL components
• DNA circuit triggered by vaginal pH drop below 4.0 or presence of Gardnerella vaginalis enzyme sialidase
• Upon trigger, capsules absorb local moisture and translate luciferase reporter, producing visible bioluminescence at infection site
• Lyoprotectant matrix keeps enzymes stable for 12 months dry storage
• Rechargeable via buffer spray; signal resets once pH normalises

Societal challenge addressed: Bacterial vaginosis affects 1 in 3 women globally and is frequently misdiagnosed or ignored. A passive, wearable biosensor democratises early detection without clinic access.

Ally Huang - Cell-Free BioBits

Target: On-demand psilocybin biosynthesis for treatment-resistant depression in isolated or underserved communities

Women and queer individuals experience depression at disproportionately higher rates yet have less access to emerging psychedelic-assisted therapies due to legal, geographic, and financial barriers. A cell-free system producing therapeutic-dose psilocybin on demand from a freeze-dried DNA template offers a decentralised, stable, and scalable alternative to clinic-dependent treatment.

Genetic target: Synthetic DNA encoding the four-enzyme psilocybin biosynthesis pathway (PsiD, PsiK, PsiM, PsiH) from Psilocybe cubensis, codon-optimised for BioBits cell-free expression.

Hypothesis: The BioBits freeze-dried matrix will support sequential multi-enzyme expression sufficient to produce measurable psilocybin from tryptamine substrate at 37°C without living cells.

Experiment:

• Three conditions: GFP positive control, four-enzyme pathway test, no-DNA negative control
• Activation with water + tryptamine substrate
• Output measured via LC-MS for psilocybin quantification and P51 viewer for fluorescent pathway reporter
• Success = detectable psilocybin above therapeutic threshold (1–3mg equivalent) per reaction volume

Why cell-free: No living GMO organism, no containment risk, stable as freeze-dried powder for field or home use, legally easier to research than whole-cell biosynthesis.

Have used Claude (Sonnet 4.6 ) to brainstorm.

Week 10 HW: Imaging and Measurement

Waters Part I — Molecular Weight 1: Based on the predicted amino acid sequence of eGFP and any known modifications, what is the calculated molecular weight? eGFP Sequence Analysis: The sequence provided includes the eGFP core, an LE linker, and a 6x-His purification tag.

2:Calculated Molecular Weight: 27,988.97 Da (Daltons). Calculate the molecular weight of the eGFP using the adjacent charge state approach. Using Figure 1, we select two adjacent peaks:

Peak 1 (m/z_n): 875.4421 Peak 2 (m/z_n+1): 903.7148 2.1 Determine z for each adjacent pair of peaks: Using the formula for charge state calculation: $$z = \frac{m/z_{n+1} - 1.0078}{m/z_{n+1} - m/z_{n}}$$ $$z = \frac{903.7148 - 1.0078}{903.7148 - 875.4421} = \frac{902.707}{28.2727} = 31.92$$

Rounding to the nearest integer, the charge state for the 903.7 peak is 31+, and the 875.4 peak is 32+.

2.2 Determine the MW of the protein: Using the 32+ charge state:

MW = (875.4421 * 32) - (32 * 1.0078) MW = 28,014.15 - 32.25 Calculated MW: 27,981.90 Da 2.3 Calculate the accuracy of the measurement: Using the formula: Error (ppm) = [|Exp - Theory| / Theory] * 1,000,000

Error = [|27,981.90 - 27,988.97| / 27,988.97] * 1,000,000 Accuracy: 252.6 ppm 3: Can you observe the charge state for the zoomed-in peak in the mass spectrum for the intact eGFP? If yes, what is it? If no, why not? No. Because the protein is in its denatured state at a high charge state (32+), the isotopes are spaced by 1/z (1/32 = 0.03 m/z). A mass spectrometer with a resolution of 30,000 cannot resolve individual isotopes for a protein this large at that charge state; they blur into a single “envelope.”

Waters Part II — Secondary/Tertiary Structure 1: Explain the difference between native and denatured protein conformations. Denatured State: The protein is unfolded, often due to acidic solvents or heat. This “stretches out” the amino acid chain, exposing basic residues (Lysine, Arginine, Histidine) that were previously hidden in the core. Consequently, the protein picks up many protons, resulting in high charge states and peaks at lower m/z values (the 500-1500 range). Native State: The protein remains folded (e.g., the eGFP beta-barrel). Many basic residues are buried and inaccessible for protonation. The protein picks up fewer protons, resulting in lower charge states and peaks at higher m/z values (the 2000-4000 range).

2: Can you discern the charge state of the peak at ~2800 m/z in Figure 3? What is it? How can you tell? Yes. The charge state is 10+. Reasoning: In the zoomed inset of Figure 3, the individual isotopes are clearly resolved. The spacing between the isotopes is 0.1 m/z. Since the spacing is equal to 1/z, a spacing of 0.1 indicates a charge state of 10 (1/10 = 0.1).

Waters Part III — Peptide Mapping 1: How many Lysines (K) and Arginines (R) are in eGFP? Based on the sequence analysis, there are 20 Lysines (K) and 6 Arginines (R).

2: How many peptides will be generated from tryptic digestion of eGFP? Using the PeptideMass tool with zero missed cleavages, 27 peptides are predicted.

3: How many chromatographic peaks do you see between 0.5 and 6 minutes in Figure 5a? Looking at the Total Ion Chromatogram (TIC), there are approximately 18 distinct peaks that meet the >10% relative abundance threshold.

4:Does the number of peaks match the number of peptides predicted? No. There are fewer peaks in the chromatogram (18) than predicted peptides (27). This is common because some peptides are too small to be retained on the column, others are too hydrophobic to elute, and some co-elute (overlap) at the same time.

5: Identify the m/z and charge (z) of the peptide in Figure 5b. Calculate the mass of the singly charged form (MH+). m/z: 525.76712 Charge (z): The isotopes are spaced by 0.5 m/z. Therefore, $z = 2$ (since 1/2 = 0.5). Singly Charged Mass (MH+): (525.767 * 2) - 1.0078 = 1050.53 Da.

6: Identify the peptide and calculate the mass accuracy in ppm. Comparing the mass to the predicted list, the peptide is FEGDTLVNR (Theoretical MH+ = 1049.52 Da).

Error: 5.7 ppm. This is highly accurate and confirms the peptide identity.

7: What is the percentage of the sequence confirmed? According to Figure 6, the sequence coverage is 88%.

8: Bonus: What is the sequence for the fragmentation spectrum in Figure 5c? The sequence is FEGDTLVNR. The spectrum displays the characteristic y-ion and b-ion series that confirm this specific amino acid order.

9: Does the peptide map data make sense? Yes. 88% coverage is excellent. The high mass accuracy (under 10 ppm) and the matching fragmentation patterns definitively prove that the protein produced is the eGFP standard.

Waters Part IV — Oligomers Based on the CDMS mass spectrum in Figure 7 and the subunit masses:

7FU Decamer: Observed at 3.4 MDa. 8FU Didecamer: Observed at 8.33 MDa. 8FU 3-Decamer: Observed at 12.67 MDa. 8FU 4-Decamer: This would be represented by the furthest right, lower-abundance peaks near 16.0-17.0 MDa.

Used Chatgpt for formula, and understanding context.

Week 11 HW: Bioproduction & Cloud Labs

Part A: The 1,536 Pixel Artwork Canvas

Contribution I designed a small section of the artwork using a few pixels to again create the BTS pop band logo, cos i am obsessed. Might not look like it, as the pixels were already too full.

I liked how different ideas were combined into one large artwork. For next time, Divided sections for nodes could make collective teamwork visible.

Part B: Cell-Free Protein Synthesis

E. coli Lysate-BL21 (DE3) Star Lysate Provides ribosomes, enzymes, and T7 RNA polymerase needed for transcription and translation.

Salts/Buffer Maintain pH, ion balance, and ribosome stability during protein synthesis.

Energy / Nucleotide System Provides energy and nucleotide precursors required for RNA production and ATP regeneration.

Translation Mix (Amino Acids) Supplies amino acids needed to build proteins.

Additives: Nicotinamide Helps protect nucleic acids and supports redox balance.

Backfill: Nuclease-Free Water Adjusts reaction volume without introducing contaminants.

1-Hour PEP-NTP vs 20-Hour NMP-Ribose-Glucose Mix The PEP-NTP mix gives fast protein production but runs out of energy quickly. The NMP-Ribose-Glucose mix regenerates energy slowly, allowing protein expression for many hours.

Bonus Question Free guanine is converted into GMP by enzymes in the lysate. GMP is then converted into GTP for transcription.

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

sfGFP Folds quickly and gives fast fluorescence, making it reliable in cell-free systems.

mRFP1 Matures slowly, so fluorescence appears later but remains stable.

mKO2 Produces strong orange fluorescence but is sensitive to acidic conditions.

mTurquoise2 Very bright cyan protein but requires correct folding conditions.

mScarlet_I Bright red fluorescent protein that needs oxygen for maturation.

Electra2 Stable blue fluorescent protein but may aggregate under some salt conditions.

Hypothesis Using extra glucose and magnesium with better oxygen exposure will improve mScarlet_I fluorescence over 36 hours by supporting energy production and chromophore maturation.

Alternative Hypothesis Adding nicotinamide and cysteine may improve mTurquoise2 folding and produce stronger cyan fluorescence.

Master Mix Design The mix includes glucose for energy, magnesium for translation, cysteine and nicotinamide for stability, and buffers to maintain pH and ionic balance.