Week 1 - Principles & Practices

This week lays the foundation for ethics, safety, and governance in biotechnology — and we get hands-on with lab basics.

Table of Contents

• 1. Project idea
• 2. Governance and policy goals
• 3. Governance actions
• 4. Scoring of governance options
• 5. Prioritized options and trade‑offs
• 6. Ethical concerns and governance ideas
• 7. Use of AI

1. Project idea

1. First, describe a biological engineering application or tool you want to develop and why.

So far, one idea I have is to develop an alternative to coffee and tea based on modified bacteria or mushrooms that synthesize caffeine. This would be interesting because it wouldn’t require the extensive transportation, human labor, land use, and resources that coffee and tea currently do. Given the scale of global consumption of these two beverages, their impact is far from negligible. I personally enjoy tea and coffee quite a lot (which is probably what led me to think about this idea). I don’t plan on replacing them entirely, but I think that being able to offer alternatives could only be beneficial.

2. Governance and policy goals

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). Break big goals down into two or more specific sub‑goals.

• Safety and security
• Equity through open development
• Transparency and information sharing

3. Governance actions

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

4. Scoring of governance options

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

5. Prioritized options and trade‑offs

5. Last, drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade‑offs you considered as well as assumptions and uncertainties.

The main priority is the strict application of established biosafety and biosecurity practices. This option scores best across safety, feasibility, and environmental protection, and it is essential given that the project involves genetically modified organisms and potential human consumption. Preventing harm, avoiding accidental release, and maintaining public trust are prerequisites for any further development.

The second priority is regulated development and verification. Regulatory oversight is necessary to demonstrate that the technology is safe, transparent, and credible beyond the lab. While this step may slow progress, it protects consumers and ensures that any claims about safety or sustainability are evidence‑based.

Scaling the technology through hubs or micro‑factories would only be considered at a later stage. Although this option offers the greatest potential environmental benefits, it also carries higher risks related to misuse, safety, and governance. Scaling should therefore depend on strong biosafety records and regulatory approval.

Overall, prioritizing biosafety first, regulation second, and scaling last offers the most ethically robust path forward. It aligns with non‑malfeasance, protects consumers and the environment, and still leaves room for meaningful impact if the technology proves viable.

6. Ethical concerns and governance ideas

Reflecting on what you learned and did in class this week, outline any ethical concerns that arose, especially any that were new to you. Then propose any governance actions you think might be appropriate to address those issues.

An important ethical concern discussed this week is the risk of monopolies in synthetic biology. When a small number of actors control key technologies or biological systems, access to solutions like treatments can become limited or too expensive, increasing inequalities, as has already been the case with insulin.

To address this, governance actions could include avoiding exclusive control over essential technologies, encouraging open research and knowledge sharing, and ensuring public oversight in the development of synthetic biology–based treatments. Educating the general public about these technologies and their implications is also important to support informed debate and fair access.

7. Use of AI

Can you correct the English mistakes: […] How to better format the markdown

Week 2 - 1. Lecture Preparation

Next lecture will be around how to Read, Write and Edit DNA.

Homework Questions from Professor Jacobson:

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

Polymerases are enzymes that synthesize long chains of nucleic acids, such as DNA or RNA. Two main types exist: DNA polymerase and RNA polymerase, which assemble DNA and RNA molecules respectively by copying a DNA template strand through base-pairing interactions during semi-conservative replication. The error rate of DNA polymerase is approximately one error per 10⁶ bases.

This error rate may seem low, but considering the length of the human genome (about 3.2 × 10⁹ base pairs), it would result in roughly 3,000 errors per replication cycle, which would make the process unreliable.

To address this issue, biological systems rely on two main error-correction mechanisms. First, many DNA polymerases possess proofreading activity through an associated exonuclease, which detects and removes incorrectly incorporated bases during replication. Second, post-replication mismatch repair proteins, such as MutS, detect and repair mismatches between the parent strand and the daughter strand. Together, these mechanisms significantly reduce the final number of errors, allowing for stable and reliable DNA replication.

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?

An average human protein is about 1036 base pairs long. Because the genetic code is redundant, each amino acid can be encoded by multiple codons (typically 2–6), so in theory there are an immense number of different DNA sequences that could code for the same protein.

In practice, most of these sequences do not work. Cells show codon bias, preferring certain codons for efficient translation. Some sequences create strong DNA or RNA secondary structures that interfere with transcription or translation. Extreme GC or AT content can reduce stability or cause replication problems. Other sequences accidentally introduce regulatory signals or stop codons. Some are also difficult to synthesize accurately. As a result, only a small fraction of the theoretically possible DNA sequences can reliably produce the intended protein.

Homework Questions from Dr. LeProust:

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

The most commonly used method today is solid-phase phosphoramidite chemical synthesis. DNA bases are added one at a time to a growing strand that is attached to a solid support, repeating a cycle of coupling, capping, oxidation, and deprotection

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

Each nucleotide addition is not 100% efficient. Even small failure rates compound over many cycles. As the oligo gets longer, the fraction of full-length correct molecules drops rapidly, and truncated or error-containing products dominate. By around 200 nt, yield and accuracy become too low for reliable direct synthesis

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

A 2000 bp gene would require 2000 sequential chemical coupling steps. With cumulative errors and truncations, the probability of producing a full-length, correct molecule becomes essentially zero. Instead, long genes are made by assembling many shorter oligos (typically 50–200 nt) using enzymatic methods like PCR and ligation, followed by error correction and sequencing

Homework Question 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 in all animals are: Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, Valine, and Arginine (arginine is strictly essential during growth and development). Animals cannot synthesize these amino acids in sufficient amounts, so they must obtain them from their diet or from other organisms.

Lysine is especially interesting because it is essential, chemically distinct, and metabolically expensive to make. Animals have completely lost the ability to synthesize it, yet lysine is required for protein synthesis, regulation, and many post-translational modifications

In Jurassic Park, InGen claims the dinosaurs were engineered to be lysine-dependent, so they would die if they escaped because lysine would be unavailable in the wild. However, lysine is a common essential amino acid found in many foods, and humans themselves are lysine-dependent. As a medically trained author, Michael Crichton would have known this, and in the story the dinosaurs indeed survive outside containment. Therefor we can imagine that the “lysine contingency” was an intentional deception by InGen, a narrative of safety designed to reassure investors and regulators, rather than a genuine biological containment strategy.

Week 2 - 2. DNA Read, Write & Edit

This week explores the read–write–edit toolkit: sequencing and synthesis workflows, restriction digests and gel electrophoresis, and early genome-editing frameworks.

Table of Contents

• 0. Basics of Gel Electrophoresis
• 1. Benchling & In-silico Gel Art
• 2. Gel Art - Restriction Digests and Gel Electrophoresis
• 3. DNA Design Challenge
• 4. Prepare a Twist DNA Synthesis Order
• 5. DNA Read/Write/Edit

I’m a commited listener but without acces to a biology lab.

0. Basics of Gel Electrophoresis

I followed the class and take a look at the content around this technique.

1. Benchling & In-silico Gel Art

After creating an account on Benchling and using it to understand better the possibilities it offers.

I made a Digestion Simulation with all the indicated restriction enzymes :

Here is my created image using this same technique :

“Seagull flying in a clear sky”, I acknowleged it is quite simple it’s the first inspiration that came to my mind :)

2. Gel Art - Restriction Digests and Gel Electrophoresis

I don’t have acces to a lab as mentionned ealier

3. DNA Design Challenge

1. Choose your protein. I’ve chosen Chitodextrinase, it’s an enzyme that permit the degradation of chitin which a polysaccaride that form structure and rigidity of living orgnisme. It’s found mostly in mushrooms and insect, there for chitodextrinase is produced by animal eating insect or plants to protect themselve from fungi. I found it intersting because i worked on mycelium based composite and chitin is on of the main strucuring compond allowing those object to be resistant and light while being biodegradable. So i thought it would be intersting to understand better the process of it’s decomposition by living organisme.

Here is this protein Sequence :

LOCUS CAM6841167 609 aa linear BCT 18-MAR-2025 DEFINITION Chitodextrinase [Enterobacter rongchengensis]

    1 mnkrtllsvl vagacvapfm aqaaslqatt sepytikasd lakkekelts fplmasvket
   61 iqtldnaqve liepgraanp dnvkrvegiv kasdweylfp lraqaytysn flkavgkfpa
  121 lcktyndgrd sdaicrkela tmfahfaqet ggheswrpea ewrqalvhvr emgwsegqkg
  181 gyngecnpdv wqgqtwpcgk dkdgdflsyf grgakqlsyn ynygpfseam ygdvrtlldk
  241 pelvadtwln lasaifffay pqppkpsmlq vidgtwqpnd hdkanglvpg fgvttqiing
  301 gvecggptei aqsenrikyy kefanylkvp vpanevlgca nmkqfdegga galkiyweqd
  361 wgwsadtpsg atyscqlvgy qtpfsafkeg dyskcvqkff nvnivnddgs atpdqtpvtp
  421 tptpapsqde tpapapvpde tpaepaavnh apvadiagpi gavdagaqvs lsaegstdad
  481 gnaltytwrs qdgqtvtgqd kavvtfkape aataqqieis ltvsdgelss ttsyllnvka
  541 kaapsqdegt sgnyaawsan skykagdivn nhgklfqckp fpysgwcnna payyepgagl
  601 awadawtal

information retrieve from : https://www.ncbi.nlm.nih.gov/protein/CAM6841167.1 and originating from a French lab in Paris not super far from where I live!

elipse: I started my research of an intersting protein thinking about cafeine but it’s an alcaloïde. Then I though that i could be one at the origine of the smell of fresh rain : it come from geosmin a bacterian made metabolite (way smaller than proteines). I also though about melanine but it’s not so simple you have different type and they aren’t protein either. After that I thought about all the things we hears about the protein in our alimentation and remembered that the most protein dense “veggie” in french it’s considered a legumineuse is lentils and the 2nd most important protein in quantity in them is “Albumine” a protein playing an important role in human lymphatic systeme alowing a proper osmotique balance of the plasma.

So for bonus here is an Albumine sequence I founded too :

    1 mkwvtfisll flfssaysrg vfrrdahkse vahrfkdlge enfkalvlia faqylqqcpf
   61 edhvklvnev tefaktcvad esaencdksl htlfgdklct vatlretyge madccakqep
  121 ernecflqhk ddnpnlprlv rpevdvmcta fhdneetflk kylyeiarrh pyfyapellf
  181 fakrykaaft eccqaadkaa cllpkldelr degkassakq rlkcaslqkf gerafkawav
  241 arlsqrfpka efaevsklvt dltkvhtecc hgdllecadd radlakyice nqdsissklk
  301 eccekpllek shciaevend empadlpsla adfveskdvc knyaeakdvf lgmflyeyar
  361 rhpdysvvll lrlaktyett lekccaaadp hecyakvfde fkplveepqn likqncelfe
  421 qlgeykfqna llvrytkkvp qvstptlvev srnlgkvgsk cckhpeakrm pcaedylsvv
  481 lnqlcvlhek tpvsdrvtkc cteslvnrrp cfsalevdet yvpkefnaet ftfhadictl
  541 sekerqikkq talvelvkhk pkatkeqlka vmddfaafve kcckaddket cfaeegkklv
  601 aasqaalgl

2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence. using : https://www.bioinformatics.org/sms2/rev_trans.html I found the translation of my Chitodextrinase protein amino acid code to nucleotide.

reverse translation of Untitled to a 1827 base sequence of most likely codons. atgaacaaacgcaccctgctgagcgtgctggtggcgggcgcgtgcgtggcgccgtttatg gcgcaggcggcgagcctgcaggcgaccaccagcgaaccgtataccattaaagcgagcgat ctggcgaaaaaagaaaaagaactgaccagctttccgctgatggcgagcgtgaaagaaacc attcagaccctggataacgcgcaggtggaactgattgaaccgggccgcgcggcgaacccg gataacgtgaaacgcgtggaaggcattgtgaaagcgagcgattgggaatatctgtttccg ctgcgcgcgcaggcgtatacctatagcaactttctgaaagcggtgggcaaatttccggcg ctgtgcaaaacctataacgatggccgcgatagcgatgcgatttgccgcaaagaactggcg accatgtttgcgcattttgcgcaggaaaccggcggccatgaaagctggcgcccggaagcg gaatggcgccaggcgctggtgcatgtgcgcgaaatgggctggagcgaaggccagaaaggc ggctataacggcgaatgcaacccggatgtgtggcagggccagacctggccgtgcggcaaa gataaagatggcgattttctgagctattttggccgcggcgcgaaacagctgagctataac tataactatggcccgtttagcgaagcgatgtatggcgatgtgcgcaccctgctggataaa ccggaactggtggcggatacctggctgaacctggcgagcgcgatttttttttttgcgtat ccgcagccgccgaaaccgagcatgctgcaggtgattgatggcacctggcagccgaacgat catgataaagcgaacggcctggtgccgggctttggcgtgaccacccagattattaacggc ggcgtggaatgcggcggcccgaccgaaattgcgcagagcgaaaaccgcattaaatattat aaagaatttgcgaactatctgaaagtgccggtgccggcgaacgaagtgctgggctgcgcg aacatgaaacagtttgatgaaggcggcgcgggcgcgctgaaaatttattgggaacaggat tggggctggagcgcggataccccgagcggcgcgacctatagctgccagctggtgggctat cagaccccgtttagcgcgtttaaagaaggcgattatagcaaatgcgtgcagaaatttttt aacgtgaacattgtgaacgatgatggcagcgcgaccccggatcagaccccggtgaccccg accccgaccccggcgccgagccaggatgaaaccccggcgccggcgccggtgccggatgaa accccggcggaaccggcggcggtgaaccatgcgccggtggcggatattgcgggcccgatt ggcgcggtggatgcgggcgcgcaggtgagcctgagcgcggaaggcagcaccgatgcggat ggcaacgcgctgacctatacctggcgcagccaggatggccagaccgtgaccggccaggat aaagcggtggtgacctttaaagcgccggaagcggcgaccgcgcagcagattgaaattagc ctgaccgtgagcgatggcgaactgagcagcaccaccagctatctgctgaacgtgaaagcg aaagcggcgccgagccaggatgaaggcaccagcggcaactatgcggcgtggagcgcgaac agcaaatataaagcgggcgatattgtgaacaaccatggcaaactgtttcagtgcaaaccg tttccgtatagcggctggtgcaacaacgcgccggcgtattatgaaccgggcgcgggcctg gcgtgggcggatgcgtggaccgcgctg

3. Codon optimization.

Different organisms “prefer” different codons for the same amino acid, because their tRNA pools are tuned to their own typical codon usage. If I took a gene from another organism and used it as‑is in a new host, translation could be slow or inefficient, leading to low protein yield or even misfolded, non‑functional protein. Codon optimization rewrites the DNA sequence without changing the amino acid sequence, replacing rare codons with codons that are common in the chosen host to match its tRNA availability and boost expression. I chose to optimize the sequence for E. coli, because it is one of the most widely used and well‑characterized hosts in synthetic biology and industrial protein production, with many tools, protocols, and optimization resources available.

using https://en.vectorbuilder.com/tool/codon-optimization.html I found this codon optimization for E. coli

Improved DNA[1]: GC=78.96%, CAI=0.88 GCCACCGGCGCAGCGTGCGCGGCCGCGTGCGGTTGTGCGTGCTGCTGTACCGGCTGCACCGGCGCCGGCTGTGGCACAGGCTGCACCGGGGGCACCGGCGGCTGCGGCGGCGGTTGCGGCTGCGGCACCGGCTGCGGCACCGGCGGCTGCGGCTGCTGCGGTACAACCACCGCGACCGGGGGCTGTGGCTGTGCCGGCGGTTGCGGCGGCTGCGGCGCGGGCTGCTGCACCGGCTGCGCGGGCGGCTGCGGCGCCTGCTGTGCGTGTTGCGCGGGCTGCGGCGCAGCGTGCTGTGGCACTGCGACCGCCTGCTGTGCGACCACCGCCGCTGCGGGTTGTGGCGCGGGTTGCGGTGCCACCTGTACCGGCGGCTGCGGCGCGGCGGCGGCGGCGGCGGGTGCGGCGGCAGCCGCCGGCGCGGCGTGCACCGGCGCCTGCTGCGCCGGCTGTACCACCACTTGCTGCGGTTGCACGGGCGCGACTGGTGGCTGTGGTGCAGGCTGCGGCACCGGCGCAGCGGCAGGCGCCGCGGCCTGCTGCGCGACCACCTGCGCAGGCGCGTGTTGCTGCACCGGTGGAGCGACCGCCGCGTGCGGCTGTGGTTGCGCGGGTGGTACCGGCGGCGCCGCCTGTACCGGCGCCACCACTGGCGCGGCGTGTTGTGGCGGCGGCTGCTGTGGCTGCGGCTGCGGAGGTTGCGGTGCGGCGTGTTGCTGCGGCGGCGCGACCGCGGCGTGTGGTACCGGCGCAGCCGCTTGCGGTTGCGGCACCGGCGGCGCAGCCGGCGGCTGCGCCACCACCGGTACCGGCGCCGCGGCGGGCTGCGGCGCGGGTTGCGGCGCCACCACGGGCGGCGGCGCGGCCACCGCGACGTGCACCGGTACGACCACCTGCTGTGGCTGCACTGGTTGTGGCTGCGGTTGCGGTTGCGCCGGCGGTTGCGGCACCGCGACCGCGTGTTGTACCGCGACCGCGGGCTGCGCGGCGTGCACCACCACCTGCACCGGTGCGGCGGCGGGCTGCGGCGGCACGGGCGGCGGCTGCGCGGCGGCGACCACGACCTGCTGCGGTGGTTGCGGTTGTACCGGCACCGGCTGCGCGGCCGCGGCATGCTGTACAGCCACCGCGGCCTGCGGCGCGACCGGTGGGTGCTGCGGCTGCGGCGCAACCGCCGGCTGCGGTGCGACCGGCTGCGGTGCAACCACCACCGGTTGCTGCGGCTGCGCGGCCGCCGGCGCGGCATGCACGGGCGGCTGTGGCGCCTGCTGCGCCACCGGCACCACAACCGGGTGCGGCTGCGCAACGACCACCACCGGCTGCGGCTGTGCCGGTGGCGCGGCGGCCTGCTGTGGTGGCTGCGGTGGCTGTTGCGCGACCGGCGCCGCGGCGGGTTGTACCGGTGGTTGCGGTTGCTGCTGCGGCGGAGCGGCGGGCTGCGGCGGCGCGGCGACGGGCGGTTGCGGTTGCTGCGCGGGCGGCTGTGGTTGCACCGGCGGCACTGGCTGCGCGACCGGCACCGGCTGCGGCTGTGGCGCCGCCGCGACCGGCGGCGGCTGCACTGGCGGCGCGGGCTGTGGCGCCGCGGGTGGCTGCTGCGCGGGCGCGGCAGCCGGTGGTTGCGGCGGCTGCACTGCCACTGCGGCGTGCGGTGGCTGCGGCGCGGCGACCGGCTGTGCAGCCTGTTGTTGCGGTGGCGCGACCGGTACCGGCACTGGCGGCTGCGCGGGCGGCGGTTGTTGTGCGGGTGCGTGTTGTACCGGCGGCTGTTGCGGCACGGGCTGCGGCGGCTGTGCGGCGGCAGGCGCGACCGCGGCGGCGGGCGCGACCGGCGGCTGCGGTGCGACCACCACCACCTGCACGGGCGCGGGCTGCACCGCCACCACCACCACCGGCGGCTGTTGTGGCTGCGGCGGCTGCGGCTGCGGTGCCGCGGCCTGCGCGGGTTGCACCGGCGCGGGCTGTACCGCGACCGCGGCCTGTACCGCCACGGCGGCGTGCACTGCGACCGGCGGCTGCTGTTGCGGCACCACCACCGCCGGCTGCGGTGCCGCGGGCTGTGGTGCGACCGGCACCGCCACCGGCGGATGCGGCGCTACCGGCACCGGCTGCGGTTGTGCGTGCTGTTGTACCGGCTGCACGGGCGGCGCGACCGCAGCGGCCTGCTGTGGCGGGGCCGCGTGCACCGGCGGCACCGGCGGCTGCGGCGGCGCCACCGCCTGCTGTACCGGAGGTTGCACGGGCGCGGCGTGTTGCACCGGCGGCTGTGGCGCGGGCTGCGGTTGCGGCGCGACCACCACCACCACCACCACCACCACCACCACCGGCTGTGGTACCGCAACCTGTTGCGGCTGCGCCGGCTGCTGCGGCTGCTGCGGCGCCGCAGCATGTTGTGGCGCGGGTTGTGCAACGGGTTGTACAGGCTGCGCCGGTGGTACCGGCGCGACCACCGGCGCGACCGGTGGCTGTGCGTGCTGTACCGGCGGCTGTGCGGGCTGCTGCGGCGCCGCGTGCGGCGCCACCTGCGCCACCGGCGCCACCGCCGCGGCGGGCTGTGGCGCCGCATGCGGCGGCTGCTGTACCGGCGGCACCGGCTGCTGCGGCGGCGGATGCACCACGACCGGCGGCTGCGGCACCGGTGCGTGCTGCGCGTGCTGCTGCGCGGGCGCGACGACGGCGACCACCGCGGCGTGCGGCGGCTGCGGCGGCTGCGGCACCGGTGGTGCCGCCACCGGCTGTGGCGGTTGCGGCGGTTGTTGCTGTGGTGCCTGCTGTGGCGCGGCCGCCACCACCGGCTGCGGCTGTGCCGGCGCCGGCTGCGGCGCCGCCGCGGCCTGCTGCGGCTGCGCGACCACCGCAGCCGCCACCGCGACCACCGCAACAGCCGCGGCCGGCGCGGCCACCACCACAGGTTGTGGTGCCGCGTGCACCGCGACGTGCACGGGTGCCGCCGCCGGTACCGGCTGCTGCGGTGGCACCGGGTGTTGCGGCGGCTGCGGCGCCGCGTGCGGCGCCGCGGGCACCGGCTGTACCGGTGGCGGTTGCACCGGCTGCGGCTGCGGCGCGGCATGCGCGACCGGCGCGGCCGCATGCGCCGGCACCACCACCGGCGCCACGGGCGCGGCGGGCGGGTGCGGCGGTTGCGGCTGTGGCGGCGGCTGTGGCTGCGGCTGCACCGGAGCAGCCGCGGCGACCACCACCGCGACCACCGGTGGCGGCGCCGCGTGCGCAGGCGGCGCGACCACCGGCGGCGGCGGCTGCACCGGCGGCGCGGGCTGCGGCTGCGGCGGCGCCACCGCCTGCTGTTGCTGTGGCGCCGGTTGCGGTGGCTGTGGTTGCGGTGCGTGCTGTACGGCAACCGCGGGGTGTACCGGCTGCTGCGCGGGCTGCACCGGTGGCACCGGGGGCGGCTGCACCGCCACATGTGCCGGTGCGTGCTGCTGTTGTGGCACCACGACCGCGGGCTGTGGTTGCGGCACCACCACCGCGGCCGCGGGTGCGGCGGGCGGCTGCGGCGCGACCACCGCAACCGCGGGCTGTGCAGCGGCCACCGGCTGCGGCACCGGCTGTGCGGGCGCGGCGGCCACCACCACCACGACCACCGCCGCCTGTGGTACCGGTGCGGCCTGCGCCACCACCGGCACCGGCGCGGCCTGCGGCGCGACCGGGGCGACCGGCGGCTGTGCCGGCTGTGGCTGCGGCGCATGCTGCTGCTGCGGTGGTGCAACCTGCGCGGGCGCGTGCTGTTGTTGCGGCGGCACCGGTGCCTGCTGCTGCTGTGGCGCGTGCTGCTGCTGTGGCGCCTGCTGCTGCTGCGGCGGCTGCGGCTGTTGCGGTGCCGGCTGTTGCGCAGGCGGCGCGACCGGTGCCGCGGCGTGCTGCTGTTGTGGTGGTTGCGGTTGCTGCGGCGGTTGTGGCTGCTGCGGCGGTACCGGCTGTTGCGGCGGTGCCACCGGCGCCGCGGCATGCTGCTGCTGTGGCGGTTGCGGCGGCGCCGCGTGCTGCGGCGGTTGCGGCGGCTGTGGCGGTACAGGCGCCGCGTGCTGCGCAACCGGCTGTGGCTGCTGCGGCGGCACCGGCGGTTGCGGTGGTGCCACCGCCACCACCGGCTGCGGCGGCGGCTGCTGCTGCGGCGCGACCACCGGCGGCTGCGGTTGTGGTGGCACCGGCGGTGCGACCGGCTGTGGCGGCGGTTGTGGTTGCGGCTGCGCGGGCGGCACCGGCGCCGGCTGCTGCACCGGCGCGGGTTGCGGCTGCGGTGGCGCAGCCGGCGGCTGCGCGGGCTGCGCCTGCTGCGGCGCGACCGGCTGCGGTGGCGCCACCGGCGGCTGTGCGGCGTGCGGTTGCGGTTGCACCGGCGCGTGTTGTACCGCGACCGCGTGCTGCACCGGTGGCTGCGGCTGCGCCGGCTGCTGCGCGGGGGGCGCCACCGGCGGCTGCTGCGCGGGCGCCTGTTGCGGCACCGGCGCGTGTTGCGGCGGCTGCTGCGCCGGCGGTGCGACCGCCGCAGCCGGCTGCGGCGGTACCGGCGGCACCGGCGCGTGTTGCACCACCACCGCCGCGGCGGGCTGCGGTTGTTGCGGCGGTGCGGCCGGTTGTGGTGGCTGTGGCGCCTGCTGCGGCTGCGGTTGCGCGGGCTGTGCGGGCGCGACCACCGGTGCAGCCGCGACCACCGCGGGTTGCTGCACGGGTGCGTGCTGTGGCACCGGCGCCGGCTGTGGCGCGACCGGCGGTTGTGGCGCGGCGTGTACCGGCGCGGGTTGTGCGGGCTGCGCGTGCTGCGCGTGTTGCGCCGGCTGCACGGCGACGTGTACCGGTTGCACCGGCGCCGCGTGCGGTACCGGCGCGGCCGCGGGCTGCGGCGCCGCGGCGGGTTGCGGTGGTTGCGGTTGCTGTGGCGCAGGTTGTTGCGCGGGCGGCGCGACCGGCGCCGCGGGAGGCTGCGCCTGCTGCGCCGGCTGCGGCGGCTGCGCCGCGTGTACCGCCACGGGCTGCGGCGGTTGTGGCACCGGCGGCGCGGGTTGCGGCTGCGGCGCGGCCTGTGCAGGCTGCGCCGCCGCGACCGCGACCGCCGCGGCGGGTTGCGGCGGCGGCTGTGGCGCGACCGCGACCACCGGCACCGGCGCCGCGTGCGCGGCGTGCTGTGCAACCGGCGGTTGCGCAGCGGCGTGTACCGGCACCACCACCTGCGCCGGCACCGGCTGTGCGGCTGCGTGCTGCGGCACCACCACCTGTTGTGGCACCGCCACCGCGGGCTGTGGCGGCTGCACCGGCGGCACCGGCTGCGCGGCCTGCGCCGCGTGTGGCTGTGGCTGCTGTGGCGGCTGCGGCACCGCGACCACCGCGACGGGTGCCGCCTGTTGCGGCGGTGGCTGCGGTTGTGGTGGTGGTTGCTGCACCGGCGGCTGTGGCACCGGCGGCGGGTGCGGCGGCGCAACCGGCTGCGGCACCGGCGGTGCGTGTTGCGGTTGCGGTTGCACCGGCTAA

4. You have a sequence! Now what? 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.

You could produce the protein using recombinant expression in cells (like E. coli, yeast, insect, or mammalian cells) or by cell‑free translation systems that use cell extracts. In both cases, your DNA sequence is first transcribed into messenger RNA by RNA polymerase, using base pairing rules (A–U, C–G) to copy the gene. Then ribosomes translate the mRNA: every three nucleotides (a codon) are read in order and matched to a tRNA carrying the corresponding amino acid. As the ribosome moves along the mRNA, it links amino acids together into a polypeptide chain that folds into your functional protein

5. How does it work in nature/biological systems? Describe how a single gene codes for multiple proteins at the transcriptional level.

A single gene can give multiple proteins mainly through alternative splicing of its pre‑mRNA. During transcription, the whole gene (exons + introns) is copied into a pre‑mRNA. The spliceosome can then join exons together in different combinations (for example skipping an exon, using alternative 5′ or 3′ splice sites, or choosing mutually exclusive exons), producing several distinct mRNA isoforms from the same gene. Each mRNA has a different coding sequence, so translation produces related but different protein isoforms with altered domains or lengths.

4. Prepare a Twist DNA Synthesis Order

Whole process done around the exemple of “Constitutive sfGFP” .fasta and .gbk file in the folder

5. DNA Read/Write/Edit

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 metagenomic DNA extracted from the outer skin/surface of our biolab’s mycelium composite object. This would reveal the microbial communities (bacteria, fungi, etc.) colonizing the surface, helping us understand biofilm formation, contamination risks, and natural protection that mycelium provides against external microbes. Additionally, I’d sequence internal DNA from the mycelium-wood composite itself (after surface sterilization) to compare the native microbiome within the material versus surface colonizers. This contrast would show whether the mycelium creates a unique internal niche or if external contaminants penetrate.

(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?

I would use Illumina sequencing for the metagenomic DNA from our biolab’s mycelium composite surface and internal samples. Because Illumina provides high accuracy and important throughput (millions of reads), which is adapted for detecting low-abundance microbes on material surfaces and generating statistically robust community profiles. Short reads (150-300 bp) cover the hypervariable regions of 16S rRNA (bacteria/archaea) and ITS (fungi) needed for species identification.

• Is your method first-, second- or third-generation or other? How so? This method is considered second-generation. It sequences millions of DNA fragments simultaneously using PCR-amplified clusters on a flow cell, unlike first-gen Sanger (one fragment at a time) or third-gen single-molecule methods.

• What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps. For the imput and preparation the essentials steps are : Input: Total DNA (~10-100 ng) extracted from surface swabs and internal core samples.
  Essential preparation steps:

1. PCR amplification of 16S V3-V4 (~460 bp) and ITS1 (~250 bp) regions using universal primers
2. Adapter ligation to add Illumina sequencing handles
3. Library pooling and normalization
4. Cluster generation on flow cell via bridge amplification

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

1. Sequencing by synthesis: DNA polymerase adds one fluorescently labeled nucleotide (with reversible terminator) at a time
2. Each base (A/T/C/G) emits a unique fluorescent color
3. Imaging: Camera captures light emission after each incorporation
4. Cleavage: Fluorescent blocker removed, allowing next base addition
5. Base calling: Software converts fluorescence intensities → ACGT sequence

• What is the output of your chosen sequencing technology? a FASTQ files containing:
• Millions of short reads (150-300 bp) per sample
• Phred quality scores per base (Q30 = 99.9% accuracy)
• Processed via QIIME2/DADA2 → taxonomic profiles showing bacterial/archaeal/fungal relative abundances on mycelium surface vs. interior

2. DNA Write (i) What DNA would you want to synthesize (e.g., write) and why? To be fully honest i’m not so sure so far, I need to take time to do more research to understand more the possibilities and the outcomes it could bring.

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

(ii) What DNA synthesis technology would I use and why?

I could use phosphoramidite-based chemical DNA synthesis the industry standard employed by companies like Twist Bioscience and IDT. This method offers the highest reliability, scalability, and quality control for custom gene synthesis up to 3 kb, with >99% accuracy at the oligo level.

Essential steps:

1. Solid-phase phosphoramidite synthesis of short oligonucleotides (50-200 bp)
2. Gibson assembly or Golden Gate to stitch oligos into full-length genes
3. Error correction via enzymatic selection or hybridization capture
4. Cloning into a plasmid vector
5. Sanger sequencing verification

Key limitations:

• Error rates accumulate with length (>3 kb requires multi-fragment assembly)
• GC-rich, repetitive, or hairpin-forming sequences reduce yield
• Cost scales linearly with length (~$0.10-0.20/base)

Why it fits: Despite limitations, gene-scale synthesis (1-3 kb) is now routine and robust enough for most synthetic biology applications. For a caffeine pathway, multiple short genes can be synthesized separately then assembled.

Scaling reality check: Unlike chemical reactors, biological systems face oxygen gradients, metabolic stress, and contamination at scale. Lab success doesn’t guarantee industrial performance, so small-scale mastery comes first.

3. DNA Edit (i) What DNA would you want to edit and why? Same as for the Write,i’m not sure yet. I don’t think I have a big enough understanding.

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

I would use *CRISPR-Cas9 genome editing because it enables precise insertion or modification of DNA at specific genomic locations using programmable guide RNAs, making it far more flexible and efficient than older methods like TALENs or ZFNs.

How CRISPR edits DNA:

1. Design guide RNA (gRNA) to target specific 20-bp genomic sequence (adjacent to PAM site)
2. Deliver Cas9 protein + gRNA as RNP complex or plasmids into cells
3. Cas9 creates double-strand break at target site
4. Provide donor DNA template with desired gene flanked by homology arms
5. Homology-directed repair (HDR) integrates new sequence during break repair

Required inputs:

• Custom gRNA sequence
• Cas9 enzyme (plasmid or RNP)
• Donor DNA template with homology arms
• Competent cells + electroporation/chemical transformation

Key limitations:

• Low HDR efficiency in non-model organisms (<10%)
• Potential off-target cuts (mitigated by high-fidelity Cas9 variants)
• Delivery challenges in some cell types
• Large donors (>2 kb) reduce integration efficiency

Why CRISPR fits: Despite limitations, it remains the most precise, flexible, and accessible editing tool available, with continuous improvements (Cas12a, base editing, prime editing) addressing early shortcomings. For engineering caffeine pathways into bacteria or fungi, CRISPR provides the necessary precision at multiple loci.

Week 3 - Lab Automation

This week we get hands-on (or at least code-on) with pipetting robots

1. Python Script for Opentrons Artwork

LifeFab node at London only have 3 colors : Purple, Pink and Blue. Therefore I try making imaging working with just those ones.

Here is my first attempt: a reproduction of The Great Wave of Kanagawa from Hokusai. link : opentrons-art.rcdonovan.com/?id=faftr8467659586

My second attemps is a spirale: link : opentrons-art.rcdonovan.com/?id=d3599rykx19y474

I got and adapted the code to operate the opentrons to produce my image. I’ll stay listening to my node to know what are the possibilities, adapt my work and maybe make it.

2. Post-Lab Questions

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

Slowpoke: An Automated Golden Gate Cloning Workflow for Opentrons OT-2 and Flex, Malcı Koray Malcı et al. American Chemical Society, https://doi.org/10.1021/acssynbio.5c00629

This paper presents Slowpoke, an open-source, automated workflow for Golden Gate DNA assembly in synthetic biology. It is designed to run on affordable liquid-handling robots such as the Opentrons OT-2 and Opentrons Flex, making high-throughput cloning accessible to standard laboratories rather than only large biofoundries.

Slowpoke automates the full cloning pipeline: Golden Gate assembly of modular DNA parts, transformation into Escherichia coli, plating, and colony PCR screening. By precisely pipetting and handling dozens of DNA combinations in parallel, the robot enables rapid construction of large combinatorial genetic libraries with high accuracy and minimal human intervention. This automation allows researchers to quickly build and test multi-gene pathways, regulatory circuits, and standardized toolkit constructs, accelerating the design–build–test cycle.

2. Write a description about what you intend to do with automation tools for your final project.

I’m not really sure yet but I think I might be able to run experiment from a cloud lab.

3. Final Project Ideas

Synthetic Biology Based Solution for Caffeine Production

Caffeine has become one of the most widely consumed psychoactive substances in the world. The estimated global coffee consumption in 2023/2024 was 10.6 million tonnes (ICO). This level of production generates significant impacts on human labor, land use, water consumption, transportation, and other environmental and socio-economic factors.

Caffeine naturally occurs in plants such as coffee, tea, and guarana, and it can be chemically extracted from these sources or produced synthetically through chemical processes starting from compounds such as ammonia. It could be interesting to explore synthetic biology approaches to develop alternative production solutions that could be deployed closer to areas of consumption and scaled to reduce these environmental impacts.

Some research has already been conducted in this area and could be starting points:

• Engineered yeast strains for de novo synthesis of caffeine - https://pmc.ncbi.nlm.nih.gov/articles/PMC5107131/
• Overview of biosynthetic pathways and metabolic engineering approaches https://pmc.ncbi.nlm.nih.gov/articles/PMC11855574/

ref :

• A global trend of caffeine consumption over time and related-environmental impacts, https://www-sciencedirect-com.devinci.idm.oclc.org/science/article/pii/S0269749119335675
• International Coffee Organization : https://ico.org/what-we-do/world-coffee-statistics-database/
• Netherland governement https://www.cbi.eu/market-information/coffee/what-demand
• synthetic cafeine : https://www.decadentdecaf.com/blogs/decadent-decaf-coffee-co/174589383-ever-wondered-where-the-caffeine-comes-from-in-soda-or-energy-drinks-answer-synthetic-caffeine
• comparaison to natural extracted one : https://properwild.com/blogs/blog/how-is-caffeine-made-artificial-vs-natural
• NCBI cafeine page : https://www.ncbi.nlm.nih.gov/books/NBK507027/

Could Synth Bio Improve Concrete: Reduce C02 FootPrint and/or Self repairability ?

No need to present concret its usages and impacts. Quite self explanatory title. Exploration of the actual possibilities, research on the strain of bacteria used, how and what are key limitations. Could lead into trying to improve them and doing concrete experiments

Some research has already been conducted in this area and could be starting points:

• Microbial induced calcite precipitation on macrostructural properties of concrete: a review (2025) - https://link-springer-com.devinci.idm.oclc.org/article/10.1186/s43065-025-00158-8
• Using Bio-Based Self-Healing Agent for Durability and Environmentally Friendly Concrete Development (2025) - https://link-springer-com.devinci.idm.oclc.org/article/10.1186/s40069-025-00823-y

Synthetic Biology for Fermentation and Probiotics Improvements

Fermentation as been a technique to keep for for longer and even get more nutrients out of them though almost symbiotic relationship with bacterias and yeast. It could be interesting to take a closer looks on it and maybe try to characterise best levain colony and evaluate the value they brings.

It could also be done around other type of fermentation processes, we could optimize the good nutrients and probiotics creation of lactic acid based fermentation or pickling of different vegetables and seed as: cabbage, peppers, mustard and many more

Some research has already been conducted in this area and could be starting points:

• Genomics and synthetic community experiments uncover the key metabolic roles of acetic acid bacteria in sourdough starter microbiomes - https://pubmed.ncbi.nlm.nih.gov/39287380/
• Probiotic characterisation of lactic acid bacteria isolated from pickles and their potential application as presumptive probiotic starter culture in cucumber pickles - https://link-springer-com.devinci.idm.oclc.org/article/10.1007/s11694-025-03101-3

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project