Week 1 HW: Principles and Practices

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

As a Biologist, I have a macro-scale perspective on life, from organisms to ecosystems to planetary systems, and have always been drawn to technological innovations. However, I am now curious about the fundamental question of what constitutes life at a micro-scale, and what does engineering its core principles entail. Still interested in biocomputational methods, I want to learn more about the intersection of bio-artificial intelligence and synthetic biology.

My initial research led me to concepts such as distributed computing, logic gates and perceptron-based learning algorithms. Then, I first encountered the term “biocomputer”, which I understand is analysing how living systems perform computation functions, and in some cases the living systems are used to perform those functions as well. In the research paper by Sarkar et al. (2021) titled “Engineered Bacteria Computationally Solve Chemically Generated 2X2 Maze Problems”, the authors programmed E.coli with genetic circuits to solve maze problems within a chemical mixture introduced inside the tubes where the bacterias were incubated in (Siobhan Roberts, 2021). They observed that the bacteria were able to solve the maze problems by analysing different maze configurations.

Inspired by this and other similar research, I would like to further explore the problem-solving capacities of other microorganisms. I am curious to see if similar genetic programming can be applied to other microbial species to solve maze problems and hopefully translate these results in a way that helps us understand new ways to optimize human-made machines.

I am excited to learn more about this in my HTGAA journey, especially knowing that Neuromorphic circuits/computing is part of the course’s curriculum. If I find new topics that spark my interest, I will add them to the list below: Biocomputers, logic gates, learning algorithms

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.

For an “ethical” future in relation to biocomputer and bio-artificial intelligence research, I propose three main principles:

• Non-malfeasance ✮

• Safety ⚘

• Respect ☀

• Transparency ✿

I propose the following goals, encompassed within one or more of the main principles mentioned previously:

A. Prevent creation/release of harmful organisms ⚘: When collaborating or working with living microorganisms, researchers should always avoid creating and/or releasing pathogenic organisms. This involves a thorough previous investigation on the particular species’ characteristics and potential risks of it being engineered and exposed to different lab procedures.

B. Minimize harm and resource use in experimentation ✮ ☀: Firstly, researchers should aspire to always minimize harm to all living organisms when working with them inside and out of the lab. Additionally, they should also avoid using more resources than they need, this requires a well thought out initial plan and constant readjustments of materials, time and procedures throughout the experimental portion of the research.

C. Ensure accurate public and scientific understanding ✿: Science has to be more democratized, especially when it is cutting-edge innovations like synthetic biology. I believe a way of doing so is by open communication with the general public using accessible friendly language.

D. Promote constructive applications of the technology ✮ ✿: True innovation should inspire applications that are ethical, fair, and beneficial for both human and more-than-human life. Achieving this requires active collaboration among diverse groups and expertise. By integrating diverse perspectives, we can better study expectations and needs, hopefully creating shared, mutualistic goals for our collective future.

Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Try to outline a mix of actions (e.g. a new requirement/rule, incentive, or technical strategy) pursued by different “actors” (e.g. academic researchers, companies, federal regulators, law enforcement, etc). Draw upon your existing knowledge and a little additional digging, and feel free to use analogies to other domains (e.g. 3D printing, drones, financial systems, etc.). Purpose: What is done now and what changes are you proposing? Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc) Assumptions: What could you have wrong (incorrect assumptions, uncertainties)? Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

1. Previous risk analysis: the projects should be reviewed and approved by an Institutional Biosafety Committee (IBC) (Institutional Biosafety Committee, n.d.) and/or an established Ethics Review Board after presenting a thorough risk assessment of the chosen organism and genetic programming for biocomputational research.

2. Establishing welfare margins: there could be an international guideline created by a wide community of academics from science to ethics where there is an established welfare margin for microbial stress in experimental designs to minimize demonstrable harm without scientific necessity. These guidelines would be based on known and measurable physiological indicators, and would help promote a duty of care for all living systems, including microorganisms.

(I recognize this can be considered unnecessary as it is ambitious and could involve an almost philosophic discussion on the care for microorganisms in scientific research. However, I feel that as researchers we should prioritize not generating stress and/or pain to any living organism.)

3. Bioethics compliance: as a condition for publication, scientific journals should require a statement/certificate of ethical review by the researcher team and an established Ethics Review Board. This certificate states that the research methods are compliant with international bioethical laws and guidelines (such as the Universal Declaration on Bioethics and Human Rights or Oviedo Convention in Europe) (Fondation Brocher, 2023). Peer reviewers are also encouraged to revise and comment on the bioethical approaches of the experimental procedures.

4. Research efficiency and sustainability standards: Synthetic biology labs (and all research institutions in general) should focus on research efficiency and establishing sustainability standards. I propose a series of documents that would provide a skeleton for periodic resource efficiency check-ins during lab meetings. To motivate research teams to adhere to this strategy, institutions could create an annual recognition for research teams that demonstrate a responsible use of resources and waste while maintaining rigorous science. Also, being awarded previously could increase the chances of acquiring further funding for the research.

5. Public engagement and education: A portion of research funding must be used for the researchers to actively engage with the public using (or teaming up with) scientific communication initiatives (public forums, workshops, interactive talks, etc.), explaining the key takeaways from their research and the limits of biocomputation to avoid sensationalism or misinterpretation.

Key actors summary:

• Research team
• Institutional Biosafety Committee (IBC)
• Ethics Review Board
• Scientific journal
• Peer reviewers
• Funding agencies
• The general public
• Scientific communicators
• Institutions

Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

Governance actions are scored 1 (least effective) to 3 (most effective).

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. For this, you can choose one or more relevant audiences for your recommendation, which could range from the very local (e.g. to MIT leadership or Cambridge Mayoral Office) to the national (e.g. to President Biden or the head of a Federal Agency) to the international (e.g. to the United Nations Office of the Secretary-General, or the leadership of a multinational firm or industry consortia). These could also be one of the “actor” groups in your matrix.

Based on the scoring matrix, my top priorities are: the previous risk analysis, the bioethics compliance and the public engagement and education. I believe these three address the most critical breaking points. Risk analysis is non-negotiable because it prevents harmful microorganisms from spreading and endangering other living forms; bioethics compliance legitimizes research and promotes duty of care for all living organisms; and public engagement and education helps build public trust and accurate understanding necessary for the field’s long-term survival. The other governance options, on the other hand, while important, are not critical. They should be encouraged as best practices as they address less immediate risks.

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. This should be included on your class page for this week.

This week’s discussion on a collaborative bio-future and the role of trust was interesting. I agree trust is essential for ethical progress, but it raised a practical concern for me: I realized I don’t fully understand the current, specific mechanisms and laws for it. I wonder what specific laws, committees, and step-by-step procedures actually check research ethics today? To address this knowledge gap I think there should be more scientific communication around this. It would be a road to strengthen trust and general understanding of ethics as a key priority for scientific research.

References:

Fondation Brocher. (2023, March 16). Bioethics and laws - Brocher. https://fondation-brocher.ch/bioethics-and-laws/#weglot_switcher

Institutional Biosafety Committee. (n.d.). National Institute of Environmental Health Sciences. https://www.niehs.nih.gov/about/boards/ibc

Roberts, S. (2021, November 11). An E. coli biocomputer solves a maze by sharing the work. MIT Technology Review. https://www.technologyreview.com/2021/11/09/1039107/e-coli-maze-solving-biocomputer/

Sarkar, K., Bonnerjee, D., & Bagh, S. (2021). Engineered Bacteria Computationally Solve Chemically Generated 2X2 Maze Problems. Homi Bhabha National Institute (HBNI). https://doi.org/10.1101/2021.06.16.448778

Week 2 Lecture Prep

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?

According to Albertson & Preston (2006), the estimation for errors that error-prone DNA polymerase is once every 104–105 nucleotides polymerized, it can be lower for polymerases that have proofreading activity and can correct mistakes. For example, “twelve of the 15 known human DNA polymerases have no proofreading activity and are error-prone” (Albertson & Preston, 2006). Compared to the human genome, which is 3.2 billion base pairs long, an error-prone polymerase would make approximately 32,000 errors per cell division. However, there are ways to correct mistakes and significantly lower this statistic: error correcting polymerases, mismatch repair, recombination repair, or double-strand break repair (Dav University, n.d.).

3. 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?

In double-stranded DNA, there are six possible reading frames: three reading from the top strand, and three reading from the bottom strand. However, just one of the six frames is used to code for a protein, the rest of them do not work because a start codon is necessary to define the frame, and the ribosome binds specifically to the correct initiation site, determining that reading frame for the gene.

Homework Questions from Dr. LeProust:

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

Phosphoramidite synthesis

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

Chemical synthesis methods, including the phosphoramidite process, cannot reliably produce oligonucleotides longer than 200 nucleotides. This limitation is due to accumulating errors with each synthetic cycle (Hoose et al. 2023, cited in Yin et al., 2024).

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

This is because the length is superior to the 200nt that can be reliably created during phosphoramidite synthesis. So to achieve the 2000bp gene, you would have to do multiple rounds of smaller oligos and then stitch them together.

Homework Question from George Church: Choose ONE of the following three questions to answer; and please cite AI prompts or paper citations used, if any.

1. What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

The main 10 aminoacids are: Arginine, Isoleucine, lysine, Methionine, Phenylalanine, Histidine, Leucine, Threonine, Tryptophan and Valine. Now, according to the Jurassic Park Wiki, the Lysine Contingency “is intended to prevent the spread of the animals in case they ever got off the island. Dr. Wu inserted a gene that creates a single faulty enzyme in protein metabolism. The animals can’t manufacture the amino acid lysine. Unless they’re continually supplied with lysine by us, they’ll slip into a coma and die”

It seems logical, because it is a second barrier of security in case the animals escape off the island. However, it is mostly a flawed hypothesis. Lysine is already an essential amino acid for all animals, meaning it must be obtained through diet, not synthesized internally. The dinosaurs would have needed to consume lysine-rich foods (meat, legumes, etc.) regardless of their engineering. So in the case of the dinosaurs escaping, other animals or plants would provide them with the necessary lysine, allowing them to survive. Although, maybe another hypothesis could be that the genetic modification may have created an exaggerated dependency on lysine, requiring amounts far greater than any natural diet could provide. In this scenario, Dr. Wu could have supplied a specially concentrated lysine supplement on the island to meet this particular need. If they escaped, even consuming lysine-rich foods in the wild would fail to meet their requirement, which would be a more clever (yet still very science-fiction oriented) option.

References:

Albertson, T. M., & Preston, B. D. (2006). DNA Replication Fidelity: proofreading in Trans. Current Biology, 16(6), R209–R211. https://doi.org/10.1016/j.cub.2006.02.031

Dav University. (n.d.). DNA proofreading and repair. DAV UNIVERSITY. https://davuniversity.org/images/files/study-material/DNA%20proofreading,EDU161.pdf

Hoose A. Vellacott R. Storch M. Freemont P. S. Ryadnov M. G. DNA synthesis technologies to close the gene writing gap. Nat. Rev. Chem. 2023;7:144–161. doi: 10.1038/s41570-022-00456-9. https://dx.doi.org/10.1038/s41570-022-00456-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

Yin Y, Arneson R, Yuan Y, Fang S. Long oligos: direct chemical synthesis of genes with up to 1728 nucleotides. Chem Sci. 2024 Dec 18;16(4):1966-1973. doi: 10.1039/d4sc06958g. PMID: 39759933; PMCID: PMC11694485.

Week 2 HW: DNA Read, Write, & Edit

Part 1: Benchling & In-silico Gel Art

My original idea was to create two sister chromatids, since most of the patterns from the Enzymes were scattered vertical lines, and they kind of looked like alleles inside a chromosome. I had some trouble creating the centromere of the chromosome because none of the enzymes alone created just one line in the middle of the ladder (so around 800 bp), so I picked SacI and SalI and ignored the top line at 12.0 kb.

Original design using restriction enzymes

Sister chromatids highlighted from the original design

Part 3: DNA Design Challenge

3.1. Choose your protein.

In relation to my interest in genetic logic gates, Sarkar et al. ’s (2021) research uses a 4-output genetic logic where each output is a fluorescent readout corresponding to a maze solution pattern. The protein I will be using is sfGFP (superfolder GFP) because it is widely used, monomeric, very well characterized, and has a strong fluorescence (Chiu & Jiang, 2017).

This is the protein’s sequence obtained from FPbase:

MSKGEELFTG VVPILVELDG DVNGHKFSVR GEGEGDATNG KLTLKFICTT GKLPVPWPTL VTTLTYGVQC FSRYPDHMKR HDFFKSAMPE GYVQERTISF KDDGTYKTRA EVKFEGDTLV NRIELKGIDF KEDGNILGHK LEYNFNSHNV YITADKQKNG IKANFKIRHN VEDGSVQLAD HYQQNTPIGD GPVLLPDNHY LSTQSVLSKD PNEKRDHMVL LEFVTAAGIT HGMDELYK

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

I introduced the Amino acid sequence in BioInformatics Sequence Manipulation Suite and obtained this:

reverse translation to a 714 base sequence of most likely codons.

atgagcaaaggcgaagaactgtttaccggcgtggtgccgattctggtggaactggatggc gatgtgaacggccataaatttagcgtgcgcggcgaaggcgaaggcgatgcgaccaacggc aaactgaccctgaaatttatttgcaccaccggcaaactgccggtgccgtggccgaccctg gtgaccaccctgacctatggcgtgcagtgctttagccgctatccggatcatatgaaacgc catgatttttttaaaagcgcgatgccggaaggctatgtgcaggaacgcaccattagcttt aaagatgatggcacctataaaacccgcgcggaagtgaaatttgaaggcgataccctggtg aaccgcattgaactgaaaggcattgattttaaagaagatggcaacattctgggccataaa ctggaatataactttaacagccataacgtgtatattaccgcggataaacagaaaaacggc attaaagcgaactttaaaattcgccataacgtggaagatggcagcgtgcagctggcggat cattatcagcagaacaccccgattggcgatggcccggtgctgctgccggataaccattat ctgagcacccagagcgtgctgagcaaagatccgaacgaaaaacgcgatcatatggtgctg ctggaatttgtgaccgcggcgggcattacccatggcatggatgaactgtataaa

3.3. Codon optimization. Using NovoProLabs Codon Optimization tool, here’s the optimized sequence:

Optimized for E.coli

ATGTCTAAAGGCGAAGAACTGTTCACCGGCGTAGTACCTATCCTGGTTGAACTGGATGGCGACGTTAACGGTCACAAGTTTTCTGTCCGTGGCGAAGGTGAAGGCGACGCTACTAACGGCAAACTGACTCTGAAATTCATCTGTACCACGGGTAAACTGCCGGTACCTTGGCCTACCCTGGTTACCACTCTGACCTACGGCGTTCAGTGTTTCTCCCGTTATCCAGACCATATGAAACGCCATGACTTCTTCAAAAGCGCGATGCCGGAAGGTTACGTACAGGAGCGTACCATCTCTTTCAAGGACGATGGCACCTACAAAACCCGTGCTGAAGTTAAGTTCGAAGGCGACACCCTGGTTAACCGTATCGAACTGAAGGGCATCGACTTCAAAGAAGACGGCAACATTCTGGGTCACAAGCTGGAGTACAACTTCAACAGCCACAACGTGTACATCACTGCCGACAAACAGAAAAACGGTATTAAAGCGAACTTCAAAATCCGTCATAACGTCGAAGATGGTTCCGTTCAACTGGCTGATCATTACCAGCAGAACACTCCGATCGGTGATGGCCCGGTTCTGCTGCCGGACAACCACTACCTGTCTACGCAATCCGTGCTGTCTAAAGACCCGAACGAAAAGCGTGACCACATGGTACTGCTGGAATTTGTTACCGCCGCTGGTATCACTCACGGCATGGATGAACTGTACAAATGA

Codon optimization is important because organisms can have different preferences for codon usage, which means that when introducing a gene sequence on a host organism, its own codon usage preferences may affect gene expression or protein synthesis. When doing codon optimization, you are modifying the sequence to enhance protein expression in the host organism.

For this codon optimization I chose E.coli, because most genetic logic gates experiments involve this bacteria due to its simplicity to engineer and wide usage.

3.4. You have a sequence! Now what? What technologies could be used to produce this protein from your DNA?

Technologies like Twist’s Silicon-based DNA Synthesis allows for high precision protein synthesis. You just design a custom sequence on the Twist’s website and order the custom gene synthesis from them.

Part 4: Prepare a Twist DNA Synthesis Order

Checking the protein is going to express correctly

Adding the Promoter, RBS, Start Codon, Coding Sequence, His Tag, Stop Codon, and Terminator sequences in the beginning and end of my optimized sequence.

Linear Map of the annotated sequence. Here’s the shared link to it!

I then downloaded the fasta file for the sequence and uploaded it on Twist. Then, I picked the pTwist Amp High Copy - (2221bp) circular vector.

And here’s the circular construct viewer of the sequence + the vector

And here’s the plasmid on Benchling after uploading the downloaded construct from Twist:

Plasmid close-up:

5.1 DNA Read

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

I would like to sequence the plasmids from three engineered bacterial strains: green, red and yellow responder. This is the first step to verify that the genetic circuits are assembled correctly, and they don’t have mutations, premature stop codons, or show unwanted recombination errors. The sequences will come from E.coli’s DNA, specifically engineered E. coli DH5α strains.

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

Ilumina MiSeq, which offers high accuracy at a lower cost for verifying multiple plasmids. In theory, the short reads from this method are okay because I would know the expected sequence and just need to confirm it before continuing with the project methodology.

Also answer the following questions:

3. Is your method first-, second- or third-generation or other? How so?

Second generation because it can sequence multiple DNA fragments simultaneously, so it is more efficient instead of doing multiple runs to sequence the plasmids DNA.

4. What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.

The DNA sequence of the E. coli DH5α strains (green, red and yellow responders) would be the input. In order to prepare it, I would need to: Extract the plasmid DNA from each strain. Quantify the DNA (can be done using Nanodrop) Fragment the DNA (can be done using enzymes) to approximately 500 bp Repair the sticky ends and create DNA with blunt ends Prevent the fragments from ligating to each other during the adapter ligation reaction by A-tailing Add sequencing adapters with barcodes by adapter ligation Amplify the sequence using PCR Pool multiple libraries into a flow cell

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

After preparing the input, the steps for sequencing are: Binding DNA fragments to the flow cell, the bridge amplification creates clusters of the identical copies of DNA. Add fluorescently labeled reversible terminators Capture which base was added to each cluster of identical copies by laser excitation Use a software for base calling (could be Dorado by Oxford Nanopore Technologies) Assign Phred quality scores to each base

6. What is the output of your chosen sequencing technology?

After the sequencing, I would have multiple FASTQ files with the raw reads with the Phred quality scores and BAM files showing variants, to see any mutations in the sequences.

5.2 DNA Write

1. 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. As described in class thus far, applications could range from therapeutics and drug discovery (e.g., mRNA vaccines and therapies) to novel biomaterials (e.g. structural proteins), to sensors (e.g., genetic circuits for sensing and responding to inflammation, environmental stimuli, etc.), to art (DNA origamis). If possible, include the specific genetic sequence(s) of what you would like to synthesize! You will have the opportunity to actually have Twist synthesize these DNA constructs! :)

I would synthesize three genetic circuits for the bacterial pattern recognizer: Green responder:

5’- [EcoRI] - [pBAD promoter] - [RBS] - [GFP gene] - [Terminator] - [pLac promoter (reverse)] - [RBS] - [LacI gene] - [Terminator] - [Ampicillin resistance] - [pUC origin] - [XhoI] -3'

Red responder:

5’- [EcoRI] - [pLac promoter] - [RBS] - [RFP gene] - [Terminator] - [pBAD promoter (reverse)] - [RBS] - [AraC gene] - [Terminator] - [Chloramphenicol resistance] - [p15A origin] - [XhoI] -3'

Yellow responder:

Combining the green + red responder plasmids in one cell

By synthesizing circuits instead of assembling, I could ensure more accuracy in the sequences.

2. What technology or technologies would you use to perform this DNA synthesis and why?

Twist could be very useful for this step, in order to achieve array-based oligo synthesis.

Also answer the following questions:

3. What are the essential steps of your chosen sequencing methods?

Key steps are:

1. Using FASTA format for the sequences
2. Using Twist to optimize codon usage, ensure higher accuracy, high parallelism and quality control
3. Do oligo synthesis on silicon chip
4. Cleave and release the oligos from the chip
5. Assemble the oligos into longer fragments using PCR or Gibson assembly
6. Clone longer fragments by introducing them into vectors
7. Do a full-length Sanger verification

4. What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?

This sequencing methodology has many steps and could take weeks to do, especially because it depends on multiple steps with different shipping times. Also, the cost increases when creating longer fragments.

5.3 DNA Edit

1. What DNA would you want to edit and why? In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why?

For my final project, I would need to edit the genome of the E. coli DH5α strain because it has LacI and AraC genes, which could intervene with the synthetic circuits that will be introduced later on. Also, removing these genes allow for real-world applications, as antibiotic-free systems are widely used for environmental or medical uses.

2. What technology or technologies would you use to perform these DNA edits and why? CRISPR-Cas9 because it can cut the genome in precise places, facilitating the extraction of the unwanted genes, and then can stitch back the fragments together.

Also answer the following questions:

3. How does your technology of choice edit DNA?

First, sgRNA guides the Cas9 enzyme to target the DNA sequence. Then, Cas9 creates a double-strand cut in the desired place of the unwanted genes. Finally, the cell repairs the break through NHEJ or HDR.

4. What are the essential steps?

First, design the RNA map to highlight and target the prophages. Second, prepare the DNA template to be edited. Third, introduce the RNA map and the DNA template in E.coli cells.

5. 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?

Steps for editing the DNA:

1. Prepare the input: E.coli DH5α strain, create the pCas plasmid (that encodes Cas9, Lambda Red and sgRNA for the CRISPR-Cas reaction), donor DNA fragments (from the synthesized PCR products), and the editing oligos for sgRNA cloning
2. Clone the sgRNAs
3. Transform pCas into target strain by electroporation and selection with kanamycin
4. Induce Lambda Red by growth with arabinose (which induces recombination proteins)
5. Add donor DNA and the transformed pCas, then electroporate
6. Amplify sequence using colony PCR
7. Grow pCas plasmid and test for loss of kanamycin resistance, to ensure it grew without antibiotic resistance

6. What are the limitations of your editing methods (if any) in terms of efficiency or precision?

CRISPR-Cas9 is not always 100% effective, as there is a small risk it will accidentally cut the DNA in the wrong places (these are called off-target effects). To avoid this, the guide RNA has to be very carefully designed. Also, another “limiting factor is the fact that dCas9 is a shared resource amongst the different gates which needs to be continuously expressed at very high concentrations, and this leads to high toxicity for the host cells” (Al-Radhawi et al., 2020).

References:

• Chiu TY, Jiang JR. Logic Synthesis of Recombinase-Based Genetic Circuits. Sci Rep. 2017 Oct 9;7(1):12873. doi: 10.1038/s41598-017-07386-3. PMID: 28993615; PMCID: PMC5634492.

• Sarkar, K., Bonnerjee, D., & Bagh, S. (2021). Engineered Bacteria Computationally Solve Chemically Generated 2X2 Maze Problems. Homi Bhabha National Institute (HBNI). https://doi.org/10.1101/2021.06.16.448778

• Zhang, H., Lin, M., Shi, H. et al. Programming a Pavlovian-like conditioning circuit in Escherichia coli. Nat Commun 5, 3102 (2014). https://doi.org/10.1038/ncomms4102

• Chen J, Li Y, Zhang K, Wang H2018.Whole-Genome Sequence of Phage-Resistant Strain Escherichia coli DH5α. Genome Announc6:10.1128/genomea.00097-18.https://doi.org/10.1128/genomea.00097-18

• Rath, D., Amlinger, L., Rath, A., & Lundgren, M. (2015). The CRISPR-Cas immune system: biology, mechanisms and applications. Biochimie, 117, 119-128.

• Al-Radhawi, M. A., Tran, A. P., Ernst, E. A., Chen, T., Voigt, C. A., & Sontag, E. D. (2020). Distributed implementation of boolean functions by transcriptional synthetic circuits. ACS Synthetic Biology, 9(8), 2172-2187.

Week 3 HW: Lab Automation

Opentrons Artwork

For this activity, I decided to do Majora’s Mask from The Legend of Zelda:

To view the design in the Opentrons page, follow this link: https://opentrons-art.rcdonovan.com/?id=7f6p4e5817h6ar0

To view the full code in Google Colab, follow this link: https://colab.research.google.com/drive/1FpWo3znYjL6Z7uWbc_opzQbmgbDo33rL?usp=sharing

Post Lab Questions

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

Fedorec et al. (2024) developed a biocomputer where bacterial colonies perform logic operations, eliminating the need for complex genetic engineering of individual cells: instead of building circuits inside the bacteria, they engineered “receiver” strains that respond to chemical concentration thresholds, then located the engineered strains at specific distances from some chemical input sources. The chemical gradients that overlap create concentrations at each colony location, and by changing the bacteria colony’s locations, they are programming them to perform AND and OR logic gates. The researchers used Opentrons OT2 handling robot to dispense the cultures onto agar plates and then add the chemical gradients. This approach is very interesting, because it treats physical space as the programmable medium.

Reference:

• Fedorec, A. J., Treloar, N. J., Wen, K. Y., Dekker, L., Ong, Q. H., Jurkeviciute, G., … & Barnes, C. P. (2024). Emergent digital bio-computation through spatial diffusion and engineered bacteria. Nature Communications, 15(1), 4896.

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

Since we had to think about two other final project ideas to add to the slide deck, here are my three final project ideas and how I could use automation tools as methodology:

1. Biofilm Filters for Amphibian Pathogens

For the automation tools, a handling robot can inoculate wells with different bacterial communities on small carrier materials; and add standardised Bd zoospore suspensions, incubate, and sample over time.

2. Bacterial Pattern Recognition

For the automation tools, a handling robot (such as the OT2) could be used to dispense the E.coli cultures and to then add the chemical gradients.

3. Beneficial Biofilm Coats for Corals

In terms of automation, an automated handling robot can assemble different combinations of candidate strains in wells with CaCO₃ chips or shell fragments to act as the coral.

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project