Week 1 HW: Principles and Practices

Class assignment – designing policy frameworks around bioengineering tools

⭐⭐ Web app with policy homework content ⭐⭐ (external link)

🔗✏️ Proof-of-work links (drafts, AI conversations/prompts used to brainstorm, generate the web app, and other sections) ✏️🔗

HTGAA 2026: Week 1 Governance Framework

Project: DIY Aging Biomarkers Kit for Community Labs
Objective: To lower the single biggest barrier to longevity research (cost) through an affordable, open-source testing kit.

Context: Diseases of aging account for 3/4 of global deaths, yet independent research is gated by prohibitive costs (often $500+ per commercial assay).

Proposed Tool: An accessible, open-source DIY Aging Biomarkers Kit designed specifically for the ~50 community biolabs worldwide. This kit allows independent researchers to measure cellular senescence and other aging hallmarks without relying on expensive institutional supply chains.

02. Policy Objectives

1. Democratize Access: Lower financial and institutional barriers so scientists in low-resource settings can meaningfully contribute to aging research.
2. Prevent Misuse: Ensure that wider access to bioengineering tools does not enable unsafe practices through proactive monitoring.
3. Ensure Translation: Guarantee that research outcomes meet quality standards sufficient for peer review and clinical translation.

03. Proposed Interventions

A1: Government Subsidies (Incentive)

Actor: Government funding bodies (NIH, ERC)
A dedicated micro-grant program (€5–25K) for community labs to accelerate biomedical progress by enabling new and existing scientists to carry out independent aging research.

• Risk: Could create a two-tier system where funded labs professionalize and lose grassroots accessibility.

A2: Lab Space Sharing Network (Coordination)

Actor: Academic Institutions, Incubators
An “Airbnb for lab benches” connecting institutions with spare capacity to independent researchers.

• Risk: Institutions may refuse due to liability concerns.

A3: AI Biosafety Co-Pilot (Technical)

Actor: Open-source developers, DIYbio.org
A software tool that checks experiment plans against biosafety databases, flags risks, and requires mentor sign-off. Serves as a dynamic guardrail.

• Risk: Over-reliance could reduce researchers’ own safety judgment (the “GPS effect”).

04. Impact Assessment Matrix

Legend:
✓ = Strong positive contribution
— = Not applicable / No direct impact

Assignment (Week 2 Lecture Prep)

From Professor Jacobson

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?

   Polymerase’s error rate is 1:10^{6 (one error per 10}6 bases), about one in ten million. The human genome is 3.2 Gbp (3.2 * 10^{9), which would then mean that we would expect to see 320 errors per haploid genome copy with no additional repair mechanisms (or double that amount for diploid replication). Yet we see that the actual final mutation rate is closer to 1 in 10}9, which is multiple orders of magnitude less.

   This is because the body applies additional “proofreading” and repair mechanisms, such as by leveraging mismatch repair proteins that scan newly synthesized DNA (they can recognize helix distortions where bases are mismatched and resynthesize them). Additionally, polymerase itself has a 3’ to 5’ exonuclease activity that removes incorrectly incorporated nucleotides immediately, allowing polymerase to redo the badly-synthesized base section.

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?

   Given that an average human protein is encoded by 1036 bp, and each codon consists of 3 bases, we can calculate that each protein is approximately 345 amino acids long (1036/3 ≈345).

   Since there are 61 codons that can code for 20 amino acids (64 total codons, but 3 of them are “stop” codons), and multiple codons can code for the same amino acid, a theoretically enormous number of DNA sequences could code for the same protein. Yet in practice we see that only a limited set of codon combinations actually lead to the production of a specific protein, and this is because there are additional biological limitations that dictate these capabilities: certain combinations are more viable (or not viable at all) based on external factors like the availability of matching tRNAs to produce it, or a given organism’s preference for a specific codon sequence, regulatory sequences that might be accidentally created, mRNA secondary structure stability, and translation efficiency.

From Dr. LeProust

1. What’s the most commonly used method for oligo synthesis currently?
2. Why is it difficult to make oligos longer than 200nt via direct synthesis?
3. Why can’t you make a 2000bp gene via direct oligo synthesis?

The current standard for de novo gene synthesis is solid‑phase phosphoramidite chemistry. It is effective but highly noisy (errors accumulate rapidly on each iteration due to approximately 95-99.5% stepwise coupling efficiency), which currently limits the total amount of viable DNA that can be synthesized, as, for a coding gene, a single base error can cause malfunction.

200 nt is the standard limit because at that point most strands have some error, but at a level where it is still feasible to fix it through cloning or purification.

A 2,000 bp gene would require 2,000 single-stranded nucleotides. The most optimistic coupling efficiency per base (99.9%), the accumulation of errors (only about 13.5% of synthesized strands would be full-length and error-free) would make the final product differ too much from the desired sequence to be useful.

We can, however, assemble genes of lengths much longer than the theoretical limitation for the method by synthesizing much smaller and more manageable strands (~50-150 bp) and assembling them through methods like PCR and Gibson assembly. This is the standard for longer DNA synthesis.

From 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
   • Isoleucine
   • Leucine
   • Lysine
   • Methionine
   • Phenylalanine
   • Threonine
   • Tryptophan
   • Valine
   • Arginine

   (Note: in humans, arginine is considered conditionally essential and is sometimes included in the list of 10)

   The definition of an “essential amino acid” is that it must be obtained from external sources because the organism cannot produce it on its own, so by this definition, and given that lysine is already found in this category, the “lysine contingency” would be a nonsensical solution (even without additional bioengineering, the dinosaurs would still have to rely on obtaining lysine from external sources like lysine-rich prey and plants, and in the movie they are already shown consuming these).

Week 2 HW: DNA Sequencing and Synthesis

PART 2

path to image: 2026a-zoe-isabel-senon/webpages/src/branch/main/assets/images/heartsim.jpg

(I tried to recreate a heart shape…)

I did the digest simulation in Benchling and checked the virtual gel simulation result:

image in: 2026a-zoe-isabel-senon/webpages/src/branch/main/assets/images/benchlingdigest1.jpg

image in: 2026a-zoe-isabel-senon/webpages/src/branch/main/assets/images/benchlingdigest2.jpg

3.1 PICK A PROTEIN & Find the protein sequence:

I picked SIRT1

And downloaded the sequence from Uniprot:

>sp|Q96EB6|SIR1_HUMAN NAD-dependent protein deacetylase sirtuin-1 OS=Homo sapiens OX=9606 GN=SIRT1 PE=1 SV=2
MADEAALALQPGGSPSAAGADREAASSPAGEPLRKRPRRDGPGLERSPGEPGGAAPEREV
PAAARGCPGAAAAALWREAEAEAAAAGGEQEAQATAAAGEGDNGPGLQGPSREPPLADNL
YDEDDDDEGEEEEEAAAAAIGYRDNLLFGDEIITNGFHSCESDEEDRASHASSSDWTPRP
RIGPYTFVQQHLMIGTDPRTILKDLLPETIPPPELDDMTLWQIVINILSEPPKRKKRKDI
NTIEDAVKLLQECKKIIVLTGAGVSVSCGIPDFRSRDGIYARLAVDFPDLPDPQAMFDIE
YFRKDPRPFFKFAKEIYPGQFQPSLCHKFIALSDKEGKLLRNYTQNIDTLEQVAGIQRII
QCHGSFATASCLICKYKVDCEAVRGDIFNQVVPRCPRCPADEPLAIMKPEIVFFGENLPE
QFHRAMKYDKDEVDLLIVIGSSLKVRPVALIPSSIPHEVPQILINREPLPHLHFDVELLG
DCDVIINELCHRLGGEYAKLCCNPVKLSEITEKPPRTQKELAYLSELPPTPLHVSEDSSS
PERTSPPDSSVIVTLLDQAAKSNDDLDVSESKGCMEEKPQEVQTSRNVESIAEQMENPDL
KNVGSSTGEKNERTSVAGTVRKCWPNRVAKEQISRRLDGNQYLFLPPNRYIFHGAEVYSD
SEDDVLSSSSCGSNSDSGTCQSPSLEEPMEDESEIEEFYNGLEDEPDVPERAGGAGFGTD
GDDQEAINEAISVKQEVTDMNYPSNKS

3.2 - Protein → DNA sequence

Obtained through the reverse translation tool after attaching the human codon table:

```jsx
Reverse Translate
Results for 747 residue sequence "sp|Q96EB6|SIR1_HUMAN NAD-dependent protein deacetylase sirtuin-1 OS=Homo sapiens OX=9606 GN=SIRT1 PE=1 SV=2" starting "MADEAALALQ"
>reverse translation of sp|Q96EB6|SIR1_HUMAN NAD-dependent protein deacetylase sirtuin-1 OS=Homo sapiens OX=9606 GN=SIRT1 PE=1 SV=2 to a 2241 base sequence of most likely codons.
atggccgacgaggccgccctggccctgcagcccggcggcagccccagcgccgccggcgcc
gacagagaggccgccagcagccccgccggcgagcccctgagaaagagacccagaagagac
ggccccggcctggagagaagccccggcgagcccggcggcgccgcccccgagagagaggtg
cccgccgccgccagaggctgccccggcgccgccgccgccgccctgtggagagaggccgag
gccgaggccgccgccgccggcggcgagcaggaggcccaggccaccgccgccgccggcgag
ggcgacaacggccccggcctgcagggccccagcagagagccccccctggccgacaacctg
tacgacgaggacgacgacgacgagggcgaggaggaggaggaggccgccgccgccgccatc
ggctacagagacaacctgctgttcggcgacgagatcatcaccaacggcttccacagctgc
gagagcgacgaggaggacagagccagccacgccagcagcagcgactggacccccagaccc
agaatcggcccctacaccttcgtgcagcagcacctgatgatcggcaccgaccccagaacc
atcctgaaggacctgctgcccgagaccatccccccccccgagctggacgacatgaccctg
tggcagatcgtgatcaacatcctgagcgagccccccaagagaaagaagagaaaggacatc
aacaccatcgaggacgccgtgaagctgctgcaggagtgcaagaagatcatcgtgctgacc
ggcgccggcgtgagcgtgagctgcggcatccccgacttcagaagcagagacggcatctac
gccagactggccgtggacttccccgacctgcccgacccccaggccatgttcgacatcgag
tacttcagaaaggaccccagacccttcttcaagttcgccaaggagatctaccccggccag
ttccagcccagcctgtgccacaagttcatcgccctgagcgacaaggagggcaagctgctg
agaaactacacccagaacatcgacaccctggagcaggtggccggcatccagagaatcatc
cagtgccacggcagcttcgccaccgccagctgcctgatctgcaagtacaaggtggactgc
gaggccgtgagaggcgacatcttcaaccaggtggtgcccagatgccccagatgccccgcc
gacgagcccctggccatcatgaagcccgagatcgtgttcttcggcgagaacctgcccgag
cagttccacagagccatgaagtacgacaaggacgaggtggacctgctgatcgtgatcggc
agcagcctgaaggtgagacccgtggccctgatccccagcagcatcccccacgaggtgccc
cagatcctgatcaacagagagcccctgccccacctgcacttcgacgtggagctgctgggc
gactgcgacgtgatcatcaacgagctgtgccacagactgggcggcgagtacgccaagctg
tgctgcaaccccgtgaagctgagcgagatcaccgagaagccccccagaacccagaaggag
ctggcctacctgagcgagctgccccccacccccctgcacgtgagcgaggacagcagcagc
cccgagagaaccagcccccccgacagcagcgtgatcgtgaccctgctggaccaggccgcc
aagagcaacgacgacctggacgtgagcgagagcaagggctgcatggaggagaagccccag
gaggtgcagaccagcagaaacgtggagagcatcgccgagcagatggagaaccccgacctg
aagaacgtgggcagcagcaccggcgagaagaacgagagaaccagcgtggccggcaccgtg
agaaagtgctggcccaacagagtggccaaggagcagatcagcagaagactggacggcaac
cagtacctgttcctgccccccaacagatacatcttccacggcgccgaggtgtacagcgac
agcgaggacgacgtgctgagcagcagcagctgcggcagcaacagcgacagcggcacctgc
cagagccccagcctggaggagcccatggaggacgagagcgagatcgaggagttctacaac
ggcctggaggacgagcccgacgtgcccgagagagccggcggcgccggcttcggcaccgac
ggcgacgaccaggaggccatcaacgaggccatcagcgtgaagcaggaggtgaccgacatg
aactaccccagcaacaagagc

>reverse translation of sp|Q96EB6|SIR1_HUMAN NAD-dependent protein deacetylase sirtuin-1 OS=Homo sapiens OX=9606 GN=SIRT1 PE=1 SV=2 to a 2241 base sequence of consensus codons.
atggcngaygargcngcnytngcnytncarccnggnggnwsnccnwsngcngcnggngcn
gaymgngargcngcnwsnwsnccngcnggngarccnytnmgnaarmgnccnmgnmgngay
ggnccnggnytngarmgnwsnccnggngarccnggnggngcngcnccngarmgngargtn
ccngcngcngcnmgnggntgyccnggngcngcngcngcngcnytntggmgngargcngar
gcngargcngcngcngcnggnggngarcargargcncargcnacngcngcngcnggngar
ggngayaayggnccnggnytncarggnccnwsnmgngarccnccnytngcngayaayytn
taygaygargaygaygaygaygarggngargargargargargcngcngcngcngcnath
ggntaymgngayaayytnytnttyggngaygarathathacnaayggnttycaywsntgy
garwsngaygargargaymgngcnwsncaygcnwsnwsnwsngaytggacnccnmgnccn
mgnathggnccntayacnttygtncarcarcayytnatgathggnacngayccnmgnacn
athytnaargayytnytnccngaracnathccnccnccngarytngaygayatgacnytn
tggcarathgtnathaayathytnwsngarccnccnaarmgnaaraarmgnaargayath
aayacnathgargaygcngtnaarytnytncargartgyaaraarathathgtnytnacn
ggngcnggngtnwsngtnwsntgyggnathccngayttymgnwsnmgngayggnathtay
gcnmgnytngcngtngayttyccngayytnccngayccncargcnatgttygayathgar
tayttymgnaargayccnmgnccnttyttyaarttygcnaargarathtayccnggncar
ttycarccnwsnytntgycayaarttyathgcnytnwsngayaargarggnaarytnytn
mgnaaytayacncaraayathgayacnytngarcargtngcnggnathcarmgnathath
cartgycayggnwsnttygcnacngcnwsntgyytnathtgyaartayaargtngaytgy
gargcngtnmgnggngayathttyaaycargtngtnccnmgntgyccnmgntgyccngcn
gaygarccnytngcnathatgaarccngarathgtnttyttyggngaraayytnccngar
carttycaymgngcnatgaartaygayaargaygargtngayytnytnathgtnathggn
wsnwsnytnaargtnmgnccngtngcnytnathccnwsnwsnathccncaygargtnccn
carathytnathaaymgngarccnytnccncayytncayttygaygtngarytnytnggn
gaytgygaygtnathathaaygarytntgycaymgnytnggnggngartaygcnaarytn
tgytgyaayccngtnaarytnwsngarathacngaraarccnccnmgnacncaraargar
ytngcntayytnwsngarytnccnccnacnccnytncaygtnwsngargaywsnwsnwsn
ccngarmgnacnwsnccnccngaywsnwsngtnathgtnacnytnytngaycargcngcn
aarwsnaaygaygayytngaygtnwsngarwsnaarggntgyatggargaraarccncar
gargtncaracnwsnmgnaaygtngarwsnathgcngarcaratggaraayccngayytn
aaraaygtnggnwsnwsnacnggngaraaraaygarmgnacnwsngtngcnggnacngtn
mgnaartgytggccnaaymgngtngcnaargarcarathwsnmgnmgnytngayggnaay
cartayytnttyytnccnccnaaymgntayathttycayggngcngargtntaywsngay
wsngargaygaygtnytnwsnwsnwsnwsntgyggnwsnaaywsngaywsnggnacntgy
carwsnccnwsnytngargarccnatggargaygarwsngarathgargarttytayaay
ggnytngargaygarccngaygtnccngarmgngcnggnggngcnggnttyggnacngay
ggngaygaycargargcnathaaygargcnathwsngtnaarcargargtnacngayatg
aaytayccnwsnaayaarwsn

```

3.3 - In your own words, describe why you need to optimize codon usage. Which organism have you chosen to optimize the codon sequence for and why?

I have chosen to optimize it for homo sapiens, because I am interested in human medicine and I want to learn more about how certain proteins might affect human lifespans.

We need to optimize codon usage because different organisms have different availability of tRNA to match certain codons, and we want to optimize for tRNA abundance to maximize translation speed, and mRNA stability and structure. These optimizations can be made because the same amino acid can be produced with different codons.

3.4 - What technologies could be used to produce this protein from your DNA? Describe in your words the DNA sequence can be transcribed and translated into your protein. You may describe either cell-dependent or cell-free methods, or both.

I could transfect the necessary genes into mammalian cells (like neurons, or HEK cells, which divide rapidly!) to make them express this protein in large quantities. We do this by basically integrating these “instructions” (as, for example, plasmids) for producing different specific proteins into the cell, and letting their machinery decode them in the same way that it does with their own genome.

(I have done this in the past with HEK cells to make them express RFP, using lipofectamine as the transfection method/reagent.)

We optimize the plasmid content for the target organism by choosing a certain promoter, codon-optimized CDS, and regulatory elements.

5.1 DNA Read

(i) What DNA would you want to sequence (e.g., read) and why? This could be DNA related to human health (e.g. genes related to disease research), environmental monitoring (e.g., sewage waste water, biodiversity analysis), and beyond (e.g. DNA data storage, biobank).

I would want to sequence the genomes of healthy centenarians or supercentenarians (people who reach 110 years of age), so that we can learn if there is a genetic basis for longevity and find enhancing variants. I know that certain variants of the FOXO3 gene have been found to be more prevalently enriched among this population (paper), and I would like to learn more.

(ii) In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why?

Also answer the following questions:

• Is your method first-, second- or third-generation or other? How so?**
• What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.**
• 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 choose Illumina short-read WGS, which is a relatively quick, affordable and accurate current industry standard for consumer sequencing.

It is a second-generation method that allows to do massively-parallelized short-reads.

To do this, we would take a sample from the supercentenarians (saliva or blood), perform end-repair and A-tailing, ligate Illumina adapters, and PCR-amplify to make libraries.

The output is a FASTQ file which we align to the human reference genome to find (call) SNPs, indels, etc. Basically to identify how and where this given genome differs from the “reference” one (assembled as an average of a group of +100 individuals).

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.

Klotho gene therapy for cognitive enhancement and longevity! I has been linked to better cognition in mice and humans (paper: https://pmc.ncbi.nlm.nih.gov/articles/PMC4176932/ )

(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?**

I would use the industry standard (phosphoramidite chemical oligo synthesis + Gibson assembly) as it is currently the most battle-tested one.

The limitation is that it only allows us to assemble up to around 200 nucleotides at a time before the error rate becomes so high as to make the output unusable (too different from the referent we want to “print”). To print out longer sequences, we must separately assemble them using methods like Gibson assembly.

5.3 DNA Edit

(i) What DNA would you want to edit and why? What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why?

Genes associated with longevity or cognitive enhancement, like FOXO3 or Klotho. I would like to test these in both humans and animals, especially dogs, since it could be helpful to obtain faster data about the effects in mammals, and possibly buy a few people additional time with their beloved pets :)

(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?**

I would use a CRISPR-based editing system and deliver it with AAV (the current most widely-used delivery mechanism for gene therapies). AAV is safer because it doesn’t integrate, which means that it doesn’t actually modify the existing genome (which can cause issues like cancer through insertional mutagenesis).

The main limitation of AAVs is their small cargo sizes, which severely limits the number of genes we could deliver through this method.

STEPS

• Found this paper using Perplexity: https://pmc.ncbi.nlm.nih.gov/articles/PMC5399099/
• Chose SIRT1 because of previous familiarity with it (it’s “famous” within the longevity field)
• Asked how to “reverse translate” this protein, got instructions to go to https://www.novoprolabs.com/tools/revtrans
• Found the Homo sapiens codon preference table: https://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=9606&aa=SELECT+A+CODE&style=GCG and pasted into the tool
• (PENDING describe the rest and attach Perplexity conversation URL: https://www.perplexity.ai/search/spermidine-is-a-protein-right-lcAd5toGRwm1Cqu6_a_ang#0 )

Week 3 HW: Lab Automation

Week 3 HW: Lab Automation

🎨 Assignment: Python Script for Opentrons Artwork

Link: https://colab.research.google.com/drive/1Yw0ZrnxIz1kG73b_9PcuZNgXGv6uQy9s?usp=sharing

mscarlet_i_points = [(-17.6, 17.6),(-15.4, 17.6),(-13.2, 17.6),(-11, 17.6),(-8.8, 17.6),(-6.6, 17.6),(-4.4, 17.6),(-2.2, 17.6),(0, 17.6),(2.2, 17.6),(4.4, 17.6),(6.6, 17.6),(8.8, 17.6),(11, 17.6),(13.2, 17.6),(15.4, 17.6),(17.6, 17.6),(19.8, 17.6),(-17.6, 15.4),(-15.4, 15.4),(-13.2, 15.4),(-11, 15.4),(-8.8, 15.4),(-6.6, 15.4),(-4.4, 15.4),(-2.2, 15.4),(0, 15.4),(2.2, 15.4),(4.4, 15.4),(6.6, 15.4),(8.8, 15.4),(11, 15.4),(13.2, 15.4),(15.4, 15.4),(17.6, 15.4),(19.8, 15.4),(-17.6, 13.2),(-15.4, 13.2),(-13.2, 13.2),(-11, 13.2),(-8.8, 13.2),(-6.6, 13.2),(-4.4, 13.2),(-2.2, 13.2),(0, 13.2),(2.2, 13.2),(4.4, 13.2),(6.6, 13.2),(8.8, 13.2),(11, 13.2),(13.2, 13.2),(15.4, 13.2),(17.6, 13.2),(19.8, 13.2),(-17.6, 11),(-15.4, 11),(-13.2, 11),(-11, 11),(-8.8, 11),(-6.6, 11),(-4.4, 11),(-2.2, 11),(0, 11),(2.2, 11),(4.4, 11),(6.6, 11),(8.8, 11),(11, 11),(13.2, 11),(15.4, 11),(17.6, 11),(19.8, 11),(-17.6, 8.8),(-15.4, 8.8),(-13.2, 8.8),(-11, 8.8),(-6.6, 8.8),(-4.4, 8.8),(-2.2, 8.8),(0, 8.8),(2.2, 8.8),(4.4, 8.8),(6.6, 8.8),(8.8, 8.8),(13.2, 8.8),(15.4, 8.8),(17.6, 8.8),(19.8, 8.8),(-17.6, 6.6),(-15.4, 6.6),(-13.2, 6.6),(-11, 6.6),(-6.6, 6.6),(-4.4, 6.6),(-2.2, 6.6),(0, 6.6),(2.2, 6.6),(4.4, 6.6),(6.6, 6.6),(8.8, 6.6),(13.2, 6.6),(15.4, 6.6),(17.6, 6.6),(19.8, 6.6),(-17.6, 4.4),(-15.4, 4.4),(-13.2, 4.4),(-11, 4.4),(-6.6, 4.4),(-4.4, 4.4),(-2.2, 4.4),(0, 4.4),(2.2, 4.4),(4.4, 4.4),(6.6, 4.4),(8.8, 4.4),(13.2, 4.4),(15.4, 4.4),(17.6, 4.4),(19.8, 4.4),(-17.6, 2.2),(-15.4, 2.2),(-13.2, 2.2),(-11, 2.2),(-8.8, 2.2),(-6.6, 2.2),(-4.4, 2.2),(-2.2, 2.2),(0, 2.2),(2.2, 2.2),(4.4, 2.2),(6.6, 2.2),(8.8, 2.2),(11, 2.2),(13.2, 2.2),(15.4, 2.2),(17.6, 2.2),(19.8, 2.2),(-24.2, 0),(-22, 0),(-19.8, 0),(-17.6, 0),(-15.4, 0),(-13.2, 0),(-11, 0),(-8.8, 0),(-6.6, 0),(-4.4, 0),(-2.2, 0),(0, 0),(2.2, 0),(4.4, 0),(6.6, 0),(8.8, 0),(11, 0),(13.2, 0),(15.4, 0),(17.6, 0),(19.8, 0),(22, 0),(24.2, 0),(26.4, 0),(-24.2, -2.2),(-22, -2.2),(-19.8, -2.2),(-17.6, -2.2),(-15.4, -2.2),(-13.2, -2.2),(-11, -2.2),(-8.8, -2.2),(-6.6, -2.2),(-4.4, -2.2),(-2.2, -2.2),(0, -2.2),(2.2, -2.2),(4.4, -2.2),(6.6, -2.2),(8.8, -2.2),(11, -2.2),(13.2, -2.2),(15.4, -2.2),(17.6, -2.2),(19.8, -2.2),(22, -2.2),(24.2, -2.2),(26.4, -2.2),(-24.2, -4.4),(-22, -4.4),(-19.8, -4.4),(-17.6, -4.4),(-15.4, -4.4),(-13.2, -4.4),(-11, -4.4),(-8.8, -4.4),(-6.6, -4.4),(-4.4, -4.4),(-2.2, -4.4),(0, -4.4),(2.2, -4.4),(4.4, -4.4),(6.6, -4.4),(8.8, -4.4),(11, -4.4),(13.2, -4.4),(15.4, -4.4),(17.6, -4.4),(19.8, -4.4),(22, -4.4),(24.2, -4.4),(26.4, -4.4),(-17.6, -6.6),(-15.4, -6.6),(-13.2, -6.6),(-11, -6.6),(-8.8, -6.6),(-6.6, -6.6),(-4.4, -6.6),(-2.2, -6.6),(0, -6.6),(2.2, -6.6),(4.4, -6.6),(6.6, -6.6),(8.8, -6.6),(11, -6.6),(13.2, -6.6),(15.4, -6.6),(17.6, -6.6),(19.8, -6.6),(-17.6, -8.8),(-15.4, -8.8),(-13.2, -8.8),(-11, -8.8),(-8.8, -8.8),(-6.6, -8.8),(-4.4, -8.8),(-2.2, -8.8),(0, -8.8),(2.2, -8.8),(4.4, -8.8),(6.6, -8.8),(8.8, -8.8),(11, -8.8),(13.2, -8.8),(15.4, -8.8),(17.6, -8.8),(19.8, -8.8),(-17.6, -11),(-15.4, -11),(-13.2, -11),(-11, -11),(-8.8, -11),(-6.6, -11),(-4.4, -11),(-2.2, -11),(0, -11),(2.2, -11),(4.4, -11),(6.6, -11),(8.8, -11),(11, -11),(13.2, -11),(15.4, -11),(17.6, -11),(19.8, -11),(-13.2, -13.2),(-11, -13.2),(-6.6, -13.2),(-4.4, -13.2),(6.6, -13.2),(8.8, -13.2),(13.2, -13.2),(15.4, -13.2),(-13.2, -15.4),(-11, -15.4),(-6.6, -15.4),(-4.4, -15.4),(6.6, -15.4),(8.8, -15.4),(13.2, -15.4),(15.4, -15.4),(-13.2, -17.6),(-11, -17.6),(-6.6, -17.6),(-4.4, -17.6),(6.6, -17.6),(8.8, -17.6),(13.2, -17.6),(15.4, -17.6),(-13.2, -19.8),(-11, -19.8),(-6.6, -19.8),(-4.4, -19.8),(6.6, -19.8),(8.8, -19.8),(13.2, -19.8),(15.4, -19.8)]
mjuniper_points = [(-4.4, 28.6),(-2.2, 28.6),(0, 28.6),(2.2, 28.6),(4.4, 28.6),(-4.4, 26.4),(-2.2, 26.4),(2.2, 26.4),(4.4, 26.4),(-4.4, 24.2),(-2.2, 24.2),(2.2, 24.2),(4.4, 24.2),(-6.6, 22),(-4.4, 22),(-2.2, 22),(2.2, 22),(4.4, 22),(6.6, 22),(-6.6, 19.8),(-4.4, 19.8),(-2.2, 19.8),(0, 19.8),(2.2, 19.8),(4.4, 19.8),(6.6, 19.8)]
mturquoise2_points = [(0, 26.4),(0, 24.2),(0, 22)]

📖 Assigment: Automation paper

“Automated Cell Culture Splitter (ACCS): An open-source benchtop system for cell passaging with integrated cell counting” — PNAS Nexus, December 2025

Citation: Automated Cell Culture Splitter (ACCS), PNAS Nexus, 4(12), pgaf385, 2025. https://academic.oup.com/pnasnexus/article/4/12/pgaf385/8405882

Why this paper: This paper presents an open-source system built around the Opentrons OT-2 that automates the passaging of adherent or suspension cells in 96-well plates. What makes it novel is the integration of a custom Cell Counting Imager (CCI) that adjusts seeding volumes per-well based on measured cell density (the robot doesn’t just pipette blindly, it makes decisions based on real-time data). The system fits inside a biosafety cabinet, processes 2–3 plates per working day with minimal intervention, and communicates status updates via Slack.

Relevance to my final project ideas: All three of my proposed projects involve cell culture steps. Automated cell passaging with integrated counting is exactly the infrastructure needed upstream of any of these experiments. The paper also demonstrates how custom hardware (the CCI) can be integrated with the OT-2’s Python API, which is relevant for designing my own automation protocols that might need plate reader integration for fluorescence readouts.

⚙️ Assignment: How I’ll apply automation in my projects

This is how I’d use automation for my proposed final project #1 (“Idea 1: CRISPR Epigenome Editing at Clock CpG Sites”): Step 1 — Transfection Setup (Opentrons OT-2/Flex) Dispensing cells, transfection reagents (e.g., Lipofectamine or PEI), and different gRNA/construct combinations across a 96-well plate. Each well gets a different gRNA targeting a different CpG site, allowing parallel testing of multiple epigenome editing targets.

Step 2 — Bisulfite Conversion Sample Prep (Opentrons OT-2) After editing, validate whether methylation was actually changed at the target site. This requires bisulfite conversion of genomic DNA — a multi-step protocol (denaturation → bisulfite treatment → desulphonation → cleanup) that is repetitive, error-prone, and ideal for automation. Zymo Research already provides Opentrons-compatible scripts for their EZ DNA Methylation kits.

Step 3 — Targeted PCR Setup (Opentrons OT-2) Set up PCR reactions targeting the specific CpG sites of interest (using bisulfite-specific primers) across the 96-well plate for downstream sequencing.

Pseudocode for Transfection Protocol:

from opentrons import protocol_api

metadata = {
    'protocolName': 'dCas9-TET1 Epigenome Editing Transfection Setup',
    'author': '[Your Name]',
    'description': 'Automated transfection of dCas9-TET1 + gRNAs targeting aging clock CpG sites'
}

requirements = {"robotType": "OT-2", "apiLevel": "2.16"}

def run(protocol: protocol_api.ProtocolContext):
    # --- LABWARE ---
    plate = protocol.load_labware('corning_96_wellplate_360ul_flat', '1')
    reagent_rack = protocol.load_labware('opentrons_24_tuberack_nest_1.5ml', '2')
    tiprack_20 = protocol.load_labware('opentrons_96_tiprack_20ul', '3')
    tiprack_300 = protocol.load_labware('opentrons_96_tiprack_300ul', '6')

    # --- INSTRUMENTS ---
    p20 = protocol.load_instrument('p20_single_gen2', 'left', tip_racks=[tiprack_20])
    p300 = protocol.load_instrument('p300_single_gen2', 'right', tip_racks=[tiprack_300])

    # --- REAGENTS (in tube rack) ---
    # A1: OptiMEM reduced serum media
    # A2: Lipofectamine 3000 reagent
    # A3: P3000 reagent
    # A4: dCas9-TET1 plasmid (shared across all wells)
    # B1-B6: gRNAs targeting 6 different aging clock CpG sites
    #   B1: gRNA for ELOVL2 promoter (cg16867657)
    #   B2: gRNA for FHL2 promoter (cg22454769)
    #   B3: gRNA for PENK intron (cg16008966)
    #   B4: gRNA for KLF14 promoter (cg07553761)
    #   B5: gRNA for TRIM59 (cg00481951)
    #   B6: non-targeting control gRNA
    # C1: Cell suspension (pre-trypsinized, counted)

    optimen = reagent_rack['A1']
    lipofectamine = reagent_rack['A2']
    p3000 = reagent_rack['A3']
    dcas9_tet1 = reagent_rack['A4']
    grnas = [reagent_rack[well] for well in ['B1', 'B2', 'B3', 'B4', 'B5', 'B6']]
    cells = reagent_rack['C1']

    # --- PROTOCOL ---

    # Step 1: Seed cells into all wells (200 uL per well, ~40,000 cells)
    # Note: In practice, cells would be seeded 24h prior
    # This step shows the protocol for Day 0
    protocol.comment("Step 1: Seeding cells")
    for well in plate.wells()[:48]:  # 48 wells = 6 gRNAs x 8 replicates
        p300.transfer(200, cells, well, new_tip='always')

    # [Protocol would pause here for 24h incubation]
    protocol.pause("Incubate plate at 37°C, 5% CO2 for 24 hours. Resume for transfection.")

    # Step 2: Prepare transfection complexes
    # Each gRNA condition gets 8 replicate wells (columns)
    protocol.comment("Step 2: Preparing lipofection complexes")

    for grna_idx, grna in enumerate(grnas):
        # Target wells: 8 wells per gRNA (one column)
        target_wells = plate.columns()[grna_idx][:8]

        for well in target_wells:
            # Add OptiMEM
            p20.transfer(10, optimen, well, new_tip='always')
            # Add dCas9-TET1 plasmid (250 ng)
            p20.transfer(2, dcas9_tet1, well, new_tip='always')
            # Add specific gRNA plasmid (250 ng)
            p20.transfer(2, grna, well, new_tip='always')
            # Add P3000 reagent
            p20.transfer(1, p3000, well, new_tip='always')
            # Add Lipofectamine
            p20.transfer(1, lipofectamine, well, mix_after=(3, 10), new_tip='always')

    protocol.comment("Transfection complete. Incubate 72h before harvesting.")
    # After 72h: harvest cells for bisulfite conversion + methylation analysis

Pseudocode for Bisulfite Conversion (Post-editing validation):

def run_bisulfite_conversion(protocol: protocol_api.ProtocolContext):
    """
    Automated bisulfite conversion of genomic DNA from transfected cells.
    Validates whether dCas9-TET1 successfully demethylated target CpG sites.
    
    Uses Zymo EZ DNA Methylation Kit protocol adapted for OT-2.
    """

    # Step 1: Add CT Conversion Reagent to each DNA sample
    # 130 uL CT reagent + 20 uL DNA sample per well
    for well in sample_plate.wells()[:48]:
        p300.transfer(130, ct_reagent, well, mix_after=(5, 100))

    # Step 2: Thermal cycling for bisulfite conversion
    # Would use Opentrons Thermocycler Module:
    #   98°C for 8 min (denaturation)
    #   64°C for 3.5 hours (conversion)
    #   4°C hold
    thermocycler.set_block_temperature(98, hold_time_minutes=8)
    thermocycler.set_block_temperature(64, hold_time_minutes=210)
    thermocycler.set_block_temperature(4)

    # Step 3: Desulphonation + Cleanup
    # Transfer to spin columns, wash, elute
    # (Magnetic bead-based cleanup would be more OT-2 friendly)
    for well in sample_plate.wells()[:48]:
        # Add binding buffer
        p300.transfer(600, binding_buffer, well, mix_after=(3, 200))
        # Transfer to magnetic bead plate
        # Wash 2x with wash buffer
        # Elute in 10 uL elution buffer
        p20.transfer(10, elution_buffer, well, mix_after=(5, 8))

    # Step 4: Set up targeted PCR for clock CpG sites
    # Bisulfite-specific primers for each target locus
    for i, primer_pair in enumerate(bisulfite_primers):
        target_wells = pcr_plate.columns()[i][:8]
        for well in target_wells:
            p20.transfer(10, pcr_master_mix, well)
            p20.transfer(2, primer_pair, well)
            p20.transfer(8, converted_dna, well, mix_after=(3, 15))

💡 Final project ideas

Added the slides to the slide deck.

3 final project ideas I’d like to pursue

1. Design targeted epigenetic editors to reverse aging methylation signatures

2. Training a Minimal Epigenetic Aging Clock and Design of a Fluorescent Age Reporter Construct

   3. Cross-Intervention Epigenetic Clock Comparison and CRISPR Knock-in Design of a p16 Aging Sensor

Week 4 HW: Protein Design Part I

⛓️ Collab Notebook Link

⛓️ Link to the Colab for the chosen protein (FOXO3): Collab notebook

💡 PART A - CONCEPTUAL QUESTIONS

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)

   Avogadro’s number: 1 mole of anything = 6.022 * 10^23 molecules. Since 1 Da = 1 g/mol, a molecule with mass 100 Da has a molar mass of 100 g/mol.

   so: 500g / (100g/mol) = 5 mo * 6.022*(10^23) = 3.01e+24 molecules

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

   The cells and proteins contained in the food are completely broken down into their most essential forms (ie amino acids) before being absorbed and used by the body. The proteins contained in the food are not used directly, instead they are broken down into amino acids which are then used as building materials when our cellular machine reads our own DNA to build the proteins we need (which might be different than the versions encoded by other species).

3. Why are there only 20 natural amino acids?

   A “why” question can never be rigurously answered in biology, but we can infer that this set of amino acids contains all the necessary properties to create life.

   (Note: by doing this exercise, I learned that there are actually 22 amino acids in nature, with the 2 additional ones being selenocystein and pyrrolysine)

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

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

   In 1953, scientists Stanley Miller and Harold Urey were able to produce amino acids by simulating early Earth conditions, by using electric sparks in a mixture of water, ammonia, methane and hydrogen. Other possible sources are meteorites (which have been found to be rich in amino acids), hydrothermal vents, photochemistry (UV radiation on ice surfaces) and, potentially, Strecker synthesis.

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

   Left-handed. They are the mirror image of L-amino acids, which are right-handed for the following reason: ”Natural L-amino acids have their φ (phi) and ψ (psi) backbone dihedral angles in a region of the Ramachandran plot (approximately φ = −60°, ψ = −40°) that produces a right-handed helix”

((Curious fact: origin of the “D” and “L” I just learned)) (nested/click to see)

    “The letters L and D come from an old naming system that compares molecules to the simple sugar glyceraldehyde and to “left/right” in Latin.
    
    Historical origin: Chemists first defined D‑ and L‑ using glyceraldehyde: in a standard Fischer projection, the isomer with the key group on the right was called D (from dextro = right), and the one with it on the left was called L (from laevo = left).

7. Can you discover additional helices in proteins?

   Yes, I learned that there’s other types like n-helix, collagen triple helix,

8. Why are most molecular helices right-handed?

9. Why do β-sheets tend to aggregate?

Because the edge strands of every β-sheet have unsatisfied hydrogen bond capacity, and are therefore “looking” for more strands to bond with. - What is the driving force for β-sheet aggregation?

The primary driver is hydrogen bonding. Each hydrogen bond contributes ~2-8kJ/mol, so a sheet with many bonds has a large energetic benefit.

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

”Amyloids are ordered protein aggregates with a characteristic cross-β structure: β-strands run perpendicular to the fibril long axis, forming a “spine” of stacked sheets. They are extraordinarily stable: resistant to heat, most detergents, and proteases.” - Can you use amyloid β-sheets as materials? - Yes. Some examples I have found: spider silk, Curli fibers, biotemplating for metallic wires. 13. Design a β-sheet motif that forms a well-ordered structure.

💡 Part B: Protein Analysis and Visualization

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

FOXO3 for its suspected longevity benefits. Certain alleles are found at higher frequencies in supercentenarians.

2 Identify the amino acid sequence of your protein.

• How long is it? What is the most frequent amino acid?

673 amino acids (states in the UniProt page)

• How many protein sequence homologs are there for your protein?

250 homologs

• Does your protein belong to any protein family?

”member of the forkhead box class O (FOXO) subfamily” (from RCSB)

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

The entry was submitted in 2008.

(page: https://www.rcsb.org/structure/2K86) (I couldn’t find this on the page, so I asked AI for help and it said: “Experimental method is solution NMR, so there is no crystallographic resolution value like “2.7 Å”; resolution only applies to diffraction/EM, not to NMR”)

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

There’s no other molecules present (according to this page). • Does your protein belong to any structure classification family?

Under “family & domains” in UniProt it says “Winged helix-like DNA-binding domain superfamily/Winged helix DNA-binding domain 1 hit”

4 Open the structure of your protein in any 3D molecule visualization software: • Visualize the protein as “cartoon”, “ribbon” and “ball and stick”.

• Color the protein by secondary structure. Does it have more helices or sheets?

It has more helices.

• Color the protein by residue type. What can you tell about the distribution of hydrophobic vs hydrophilic residues?

The core of the protein (interior) is dominated by hydrophobic residues (green patches). This is the hydrophobic core, which drives protein folding (hydrophobic residues hide from water by burying inward). The outer loops and exposed regions show more orange/red, meaning hydrophilic/charged residues (these face outward toward the watery cellular environment).

• Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)?

💡 C1. Protein Language Modeling

1. Deep Mutational Scans
   1. Use ESM2 to generate an unsupervised deep mutational scan of your protein based on language model likelihoods.
   2. Can you explain any particular pattern? (choose a residue and a mutation that stands out)

The most disfavored mutation is:

Pos 1: M->W, LLR=-17.779

The most favored one:

Pos 261: T->S, LLR=3.991

2. Latent Space Analysis

   1. Use the provided sequence dataset to embed proteins in reduced dimensionality.
   

   3. Analyze the different formed neighborhoods: do they approximate similar proteins?

      I had trouble finding this protein due to its nature.

   4. Place your protein in the resulting map and explain its position and similarity to its neighbors.

Week 5 HW: Protein Design Part II

🔎 Part 1: Generate Binders with PepMLM

Sequence with mutation: MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTS AGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVV HEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

![[htgaa-hw5-pt1-binders.png]]

In text, the generated ones (plus the one provided to compare) were:

0	WRYYAAALRHKX	9.957530
1	WRYYAVAARHKK	13.948671
2	WRSYVVVLELGX	18.185468
3	HHYPAVAVALKX	7.987091
4	FLYRWLPSRRGG	20.635231

AlphaFold however was throwing errors with these sequences, so I asked Claude to check: ![[Screenshot 2026-03-08 at 17.05.41.png]]

So I changed them to these:

• WRYYAAALRHKG
• WRYYAVAARHKK
• WRSYVVVLELGG
• HHYPAVAVALKG
• FLYRWLPSRRGG

🔎 PeptiVerse results

🦠 Peptide #1: WRYYAAALRHKG

AlphaFold:

• ipTM = 0.38
• pTM = 0.8 ![[Screenshot 2026-03-08 at 18.17.58.png]]

![[Screenshot 2026-03-08 at 19.08.57.png]]

➡️ Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried?

ipTM = 0.38 is low confidence of binding. It would be surface-bound and far from the beta-barrel region.

PeptiVerse:

🦠 Number #2 WRYYAVAARHKK

Alphafold:

• ipTM = 0.31
• pTM = 0.75 ![[Screenshot 2026-03-08 at 18.19.06.png]]

➡️ Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried? Surface-bound, relatively close to the mutation site. It does not engage the beta barrel site. iPTM = 0.31 which shows low confidence.

PeptiVerse:

🦠 Number #3 WRSYVVVLELGG

Alphafold:

• ipTM = 0.75
• pTM = 0.86

![[Screenshot 2026-03-08 at 18.48.53.png]] ➡️ Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried? This one is bound on the surface. ipTM = 0.75 which is somewhat confident (we treat a score between 70 and 90 as “confident”). It is somewhat close to the beta-barrel area. It isn’t too far from the mutation area, but also not super close.

![[Screenshot 2026-03-08 at 18.54.30.png]] PeptiVerse:

🦠 Number 4: HHYPAVAVALKG

Alphafold:

• ipTM = 0.29
• pTM = 0.86 ![[Screenshot 2026-03-08 at 18.15.35.png]]

➡️ Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried?

ipTM = 0.29 which is very low confidence of binding. No, it’s not close to the mutation site. It’s on the opposite side of there the beta-barrel sits, so no. It is surface-bound and closest to the positions 86-87.

PeptiVerse:

🦠 Number 5: FLYRWLPSRRGG

Alphafold:

• ipTM = 0.35
• pTM = 0.8

![[Screenshot 2026-03-08 at 18.20.14.png]]

➡️ Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried?

ipTM = 0.35, hence confidence of binding is low. It’s far from the N-terminus that has the A4V. It is surface-bound. It is close to the beta-barrel region (the arrows).

PeptiVerse:

🔎 Part 1 analysis

1. ➡️ In a short paragraph, describe the ipTM values you observe and whether any PepMLM-generated peptide matches or exceeds the known binder. Yes, WRSYVVVLELGG has a higher iPTM (0.75 vs 0.35) plus a lower perplexity score (18 vs 20), even though it has the highest perplexity score out of the other generated ones. In general, the iPTM values for all of them except this one are low (~0.30), meaning that AlphaFold is not very confident about their vinding affinity.

2. ➡️ In a short paragraph, describe what you see. Do peptides with higher ipTM also show stronger predicted affinity? Are any strong binders predicted to be hemolytic or poorly soluble? Which peptide best balances predicted binding and therapeutic properties? ![[Screenshot 2026-03-08 at 19.22.45.png]]

   WRSYVVVLELGG scores the highest iPTM from AlphaFold and the highest binding affinity according to PeptiVerse. The scores are correlated in both platforms for all peptides. Solubility is around 1.0 for all of them, so none are poorly soluble.

   All show hemolytic probabilities under 0.1 besides one, which is the strongest binder. The strongest binder is also one of the only two with a positive GRAVY score, showing the strongest hydrophobicity. (I learned that “Hydrophobic peptides often damage membranes”, so this would not be a good candidate). Therefore the binder with the strongest affinity also shows the highest toxicity and aggregation risk.

   Peptide #1 WRYYAAALRHKG shows the lowest hemolysis score, but its binding affinity is not too strong, so it would not make for a strong candidate, but it has a good safety profile.

   Peptide #2 has weak binding affinity and a high charge, so we discard it. (Why: “High overall positive charge is usually bad for a peptide drug candidate because it increases nonspecific binding to negatively charged membranes and biomolecules, which can drive toxicity, hemolysis, and off‑target effects”)

3. ➡️ Choose one peptide you would advance and justify your decision briefly. I would choose peptide #1 because it has the second highest binding affinity (despite still being moderately low) but a good safety profile.

Week 6 HW: Genetic Circuits Part I

ASIMOV KERNEL REPRESSILATOR

Original repressilator construct from the example repo, with its simulation data:

My simulation graphs did not match: Even after replacing all the parts specifically for the ones in the Characterized Bacteria repo, it still didn’t match. I will have to debug further.

I asked AI to help me debug and it said that some possible reasons might be that the parts I am using might have different metadata even if they appear identical, that my simulation window might be too long, that the RBS I chose might be too weak or strong, that the backbone or copy-number might differ (I tried with and without an added backbone), that the promoters might not have the right repressor behavior… many alternatives to test!

Week 9 HW: Cell-Free Systems

Week 9 Homework: Cell-Free Systems

[WIP!]

Homework Part A: General and Lecturer-Specific Questions

General homework questions

1. Explain the main advantages of cell-free protein synthesis over traditional in vivo methods, specifically in terms of flexibility and control over experimental variables. Name at least two cases where cell-free expression is more beneficial than cell production.

The benefit can be summarized as “it removes the constraints we usually face for protein synthesis when working with live cells”. For example, working with live cells requires culturing cells throughout the whole cell lifecycle, with all the possibilities for error that this implies (mistakes, unexpected cell behavior due to their non-deterministic nature, etc), as well as the required timelines (speed is limited by the fundamental speed constraints from the cell growth cycle), and costs. Cell-free systems also allow for much greater control, as the main system is boiled down to its most basic functional components, removing a lot of complexity (variables outside of our control), and therefore allowing the possibility of producing much more homogeneous products.

Two special cases that CFS allow are the use of molecules that would usually be toxic for cells, and working with a much wider variety of molecules that would otherwise be destroyed by the cell, as well as modified versions of the most basic components of this machinery (that could not work in a live cell), such as amino acids that don’t exist in nature.

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

The basic components are: 1) The lysate, which includes the “machinery”: basically ground up cells (prokaryotic or eukaryotic), providing the cellular components we need for the reactions, like ribosomes, enzymes, tRNAs and cofactors, which will translate mRNA into protein 2) The genetic template (DNA or RNA) that encodes the product we want 3) The “building blocks” (amino acids and nucleotides) 4) The energy system (needed to provide and replenish the ATP and GTP needed for the reactions) (ie Mg^2+ and K+) 1) Note: CFS use Phosphoenolpyruvate (PEP) for this 5) The “environmental tuning” factors (salts, buffers, temperature, etc)

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.

   Because protein synthesis is an energy-intensive process. In living cells, mitochondria handle the production of ATP, but in CFS we need to set up systems that can continually obtain ie the phosphates needed to convert ADP into ATP. Not being able to meet these energy needs will cause the reactions to stall or provide low yield.

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

   Only eukaryotic cells can perform PTM post-translational modifications (ie glycosylation), so any desired product requiring this process will require the use of eukaryotic cell lysate. This will also be required if the final desired product requires the usage of mammalian regulators. However, when these requirements are absent, prokaryotic systems are faster and cheaper.

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.

The main challenge when working with membrane proteins is that they tend to aggregate or misfold in aqueous environments (due to their hydrophobic regions trying to “escape” it) [ PENDING ]

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.
   • The required energy needs are not being met.
   • The proteins might be misfolding. In this case, we would check and tune the environmental variables, like the temperature or the salsts, and possibly add chaperones or use different membrane-mimicking systems.
   • The final or intermediary products might be suffering degradation. In this case, I would check for the presence of ie nucleases, proteases, etc.

Homework question from Kate Adamala

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

1. Pick a function and describe it.
   1. What would your synthetic cell do? What is the input and what is the output?
   2. Could this function be realized by cell-free Tx/Tl alone, without encapsulation?
   3. Could this function be realized by genetically modified natural cell?
   4. Describe the desired outcome of your synthetic cell operation.
2. Design all components that would need to be part of your synthetic cell.
   1. What would be the membrane made of?
   2. What would you encapsulate inside? Enzymes, small molecules.
   3. 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)
   4. How will your synthetic cell communicate with the environment? (hint: are substrates permeable? or do you need to express the membrane channel?)
3. Experimental details
   1. List all lipids and genes. (bonus: find the specific genes; for example, instead of just saying “small molecule membrane channel” pick the actual gene.)
   2. How will you measure the function of your system?

PENDING

Example solution

Based on: Lentini, R. et al., 2014. Nat comm, 5, p.4012.

1. Pick a function and describe it.
   1. What would your synthetic cell do? What is the input and what is the output?
      Expand the sensing capacity of bacteria. Input: theophylline (inert to bacteria). Output of the SMC: IPTG. Output of the whole system: GFP produced in bacteria. (Theophyline aptamer reference: Martini, L. & Mansy, S.S., 2011. Cell-like systems with riboswitch controlled gene expression. Chemical Communications, 47(38), p.10734.)
   2. Could this function be realized by cell-free Tx/Tl alone, without encapsulation?
      No. If the IPTG were not encapsulated, it would go into the bacteria without the need of theophylline-induced membrane channel synthesis, thus the synthetic cell actuator would not exist.
   3. Could this function be realized by genetically modified natural cell?
      Yes, in this particular case: the theophylline aptamer could be incorporated into a transformed gene. This lacks generality though – it is easier to make SMC than modify bacteria, so in this system a single bacteria reporter can be used to detect various small molecules.
   4. Describe the desired outcome of your synthetic cell operation.
      In the presence of SMC, bacteria sense theophylline.
2. Design all components that would need to be part of your synthetic cell.
   1. What would be the membrane made of?
      Phospholipids + cholesterol.
   2. What would you encapsulate inside? Enzymes, small molecules.
      cell-free Tx/Tl system, IPTG, gene for membrane transporter under the control of theophylline aptamer.
   3. 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)
      Bacterial, because of the theophylline riboswitch used as SMC input.
   4. How will your synthetic cell communicate with the environment? (hint: are substrates permeable? or do you need to express the membrane channel?)
      The membrane is permeable to the input molecule (theophylline), the output is IPTG that will cross the membrane via the membrane pore created after theophyline-initiated gene expression.
3. Experimental details
   1. List all lipids and genes. (bonus: find the specific genes; for example, instead of just saying “small molecule membrane channel” pick the actual gene.)
      • Lipids: POPC, cholesterol
      • Enzymes: bacterial cell-free Tx/Tl
      • Genes: a-hemolysin (aHL) to encapsulate in SMC
      • Biological cells: E.coli transformed with GFP under T7 promoter and a lac operator
   2. How will you measure the function of your system?
      Measure GFP output of the cells via flow cytometry. Alternatively, use enzymatic reporter, like luciferase, and measure bulk output of the enzyme.

Artificial cells translate chemical signals for E. coli.
(a) In the absence of artificial cells (circles), E. coli (oblong) cannot sense theophylline.
(b) Artificial cells can be engineered to detect theophylline and in response release IPTG, a chemical signal that induces a response in E. coli.

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)?

MY ANSWERS:

• Portable FD-CF pathogen sensor patches embedded in taxi and public transport vehicles to monitor airborne pathogen load
• Leveraging a freeze-dried system with an RNA switch (or CRISPR-based) detector, this device could be carried by transport service providers who could periodically measure the pathogen levels in the vehicle with a swab. It could be integrated into seatbelts or any other part of the car (it could be a patch or sticker).
• Use cases: 1) identify when the vehicle should be more thoroughly disinfected 2) driver safety 3) passenger safety (for example, riders who are especially immunocompromised could request vehicles with lower pathogen levels) 4) data collection for scientific purposes (ie epidemic detection and prevention)
• The device could be a patch with a cartridge containing the necessary water and buffers and a button or switch that the driver could click to easily mix the components, for example at the end of a day’s shift. The device would be single-use and replaced daily to ensure minimal contamination. The freeze-dried system ensures stability for at least 3-6 months at room temperature.

Homework question from Ally Huang

Freeze-dried cell-free reactions have great potential in space, where resources are constrained. As described in my talk, the Genes in Space competition challenges students to consider how biotechnology, including cell-free reactions, can be used to solve biological problems encountered in space. While the competition is limited to only high school students, your assignment will be to develop your own mock Genes in Space proposal to practice thinking about biotech applications in space!

For this particular assignment, your proposal is required to incorporate the BioBits® cell-free protein expression system, but you may also use the other tools in the Genes in Space toolkit (the miniPCR® thermal cycler and the P51 Molecular Fluorescence Viewer). For more inspiration, check out https://www.genesinspace.org/ .

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)
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)
3. Describe how your molecular or genetic target relates to the space biology question or challenge your proposal addresses. (Maximum 100 words)
4. Clearly state your hypothesis or research goal and explain the reasoning behind it. (Maximum 150 words)
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)

MY ANSWERS (PENDING)

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project