Part 1: Benchling & In-silico Gel Art Figure 1. Simulated agarose gel showing the LAMCG plasmid digested with different restriction enzymes. Figure 2. Screenshot of the online tool used to simulate restriction digests and generate the virtual gel pattern for LAMCG.
Biological engineering application or tool to develop and why Idea
Mi idea its a biological biosensor designed to detect early changes in dependence on the molecular chaperone HSP90 in KIT mutant gastrointestinal stromal tumors (GIST), as functional indicators of cellular adaptation preceding the development of resistance to imatinib.
Figure 1. Conceptual schematic of the proposed biological biosensor. Image generated using AI for graphical representation purposes only.
Generate an artistic design using the GUI at opentrons-art.rcdonovan.com. Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons. You may use AI assistance for this coding — Google Gemini is integrated into Colab (see the stylized star bottom center); it will do a good job writing functional Python, while you probably need to take charge of the art concept. If you’re a proficient programmer and you’d rather code something mathematical or algorithmic instead of using your GUI coordinates, you may do that instead. Google colab link: https://colab.research.google.com/drive/1CuzPdG5pSYIgSalc1o3C1FA-yUbnaW0_?usp=sharing
Subsections of Homework
Week 02 HW:DNA read write and edit
Part 1: Benchling & In-silico Gel Art
Figure 1. Simulated agarose gel showing the LAMCG plasmid digested with different restriction enzymes.
Figure 2. Screenshot of the online tool used to simulate restriction digests and generate the virtual gel pattern for LAMCG.
Testing update
I was arranging the bands to try to form the letter “M.”
Part 3: DNA Design Challenge
3.1. Choose your protein.
I choose protein HSP90
Original sequence:
*>sp|P07900|HS90A_HUMAN Heat shock protein HSP 90-alpha OS=Homo sapiens OX=9606 GN=HSP90AA1 PE=1 SV=5
MPEETQTQDQPMEEEEVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEKERDKEVSDDEAEEKEDKEEEKEKEEKESEDKPEIEDVGSDEEEEKKDGDKKKKKKIKEKYIDQEELNKTKPIWTRNPDDITNEEYGEFYKSLTNDWEDHLAVKHFSVEGQLEFRALLFVPRRAPFDLFENRKKKNNIKLYVRRVFIMDNCEELIPEYLNFIRGVVDSEDLPLNISREMLQQSKILKVIRKNLVKKCLELFTELAEDKENYKKFYEQFSKNIKLGIHEDSQNRKKLSELLRYYTSASGDEMVSLKDYCTRMKENQKHIYYITGETKDQVANSAFVERLRKHGLEVIYMIEPIDEYCVQQLKEFEGKTLVSVTKEGLELPEDEEEKKKQEEKKTKFENLCKIMKDILEKKVEKVVVSNRLVTSPCCIVTSTYGWTANMERIMKAQALRDNSTMGYMAAKKHLEINPDHSIIETLRQKAEADKNDKSVK
DLVILLYETALLSSGFSLEDPQTHANRIYRMIKLGLGIDEDDPTADDTSAAVTEEMPPLEGDDDTSRMEEVD
3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.
reverse translation of sp|P07900|HS90A_HUMAN Heat shock protein HSP 90-alpha OS=Homo sapiens OX=9606 GN=HSP90AA1 PE=1 SV=5 to a 2196 base sequence of most likely codons.
atgccggaagaaacccagacccaggatcagccgatggaagaagaagaagtggaaacctttgcgtttcaggcggaaattgcgcagctgatgagcctgattattaacaccttttatagcaacaaagaaatttttctgcgcgaactgattagcaacagcagcgatgcgctggataaaattcgctatgaaagcctgaccgatccgagcaaactggatagcggcaaagaactgcatattaacctgattccgaacaaacaggatcgcaccctgaccattgtggataccggcattggcatgaccaaagcggatctgattaacaacctgggcaccattgcgaaaagcggcaccaaagcgtttatggaagcgctgcaggcgggcgcggatattagcatgattggccagtttggcgtgggcttttatagcgcgtatctggtggcggaaaaagtgaccgtgattaccaaacataacgatgatgaacagtatgcgtgggaaagcagcgcgggcggcagctttaccgtgcgcaccgataccggcgaaccgatgggccgcggcaccaaagtgattctgcatctgaaagaagatcagaccgaatatctggaagaacgccgcattaaagaaattgtgaaaaaacatagccagtttattggctatccgattaccctgtttgtggaaaaagaacgcgataaagaagtgagcgatgatgaagcggaagaaaaagaagataaagaagaagaaaaagaaaaagaagaaaaagaaagcgaagataaaccggaaattgaagatgtgggcagcgatgaagaagaagaaaaaaaagatggcgataaaaaaaaaaaaaaaaaaattaaagaaaaatatattgatcaggaagaactgaacaaaaccaaaccgatttggacccgcaacccggatgatattaccaacgaagaatatggcgaattttataaaagcctgaccaacgattgggaagatcatctggcggtgaaacattttagcgtggaaggccagctggaatttcgcgcgctgctgtttgtgccgcgccgcgcgccgtttgatctgtttgaaaaccgcaaaaaaaaaaacaacattaaactgtatgtgcgccgcgtgtttattatggataactgcgaagaactgattccggaatatctgaactttattcgcggcgtggtggatagcgaagatctgccgctgaacattagccgcgaaatgctgcagcagagcaaaattctgaaagtgattcgcaaaaacctggtgaaaaaatgcctggaactgtttaccgaactggcggaagataaagaaaactataaaaaattttatgaacagtttagcaaaaacattaaactgggcattcatgaagatagccagaaccgcaaaaaactgagcgaactgctgcgctattataccagcgcgagcggcgatgaaatggtgagcctgaaagattattgcacccgcatgaaagaaaaccagaaacatatttattatattaccggcgaaaccaaagatcaggtggcgaacagcgcgtttgtggaacgcctgcgcaaacatggcctggaagtgatttatatgattgaaccgattgatgaatattgcgtgcagcagctgaaagaatttgaaggcaaaaccctggtgagcgtgaccaaagaaggcctggaactgccggaagatgaagaagaaaaaaaaaaacaggaagaaaaaaaaaccaaatttgaaaacctgtgcaaaattatgaaagatattctggaaaaaaaagtggaaaaagtggtggtgagcaaccgcctggtgaccagcccgtgctgcattgtgaccagcacctatggctggaccgcgaacatggaacgcattatgaaagcgcaggcgctgcgcgataacagcaccatgggctatatggcggcgaaaaaacatctggaaattaacccggatcatagcattattgaaaccctgcgccagaaagcggaagcggataaaaacgataaaagcgtgaaagatctggtgattctgctgtatgaaaccgcgctgctgagcagcggctttagcctggaagatccgcagacccatgcgaaccgcatttatcgcatgattaaactgggcctgggcattgatgaagatgatccgaccgcggatgataccagcgcggcggtgaccgaagaaatgccgccgctggaaggcgatgatgataccagccgcatggaagaagtggat
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.
R/:Once I have the DNA sequence, I would use a cell-free expression system to produce the protein. In this approach, the DNA is added directly to a reaction mixture that contains all the molecular components required for transcription and translation, such as RNA polymerase, ribosomes, nucleotides, amino acids, tRNAs, and necessary enzymes.
First, the DNA sequence is transcribed into mRNA by RNA polymerase within the reaction mixture. Then, ribosomes bind to the mRNA and translate it into the corresponding amino acid sequence, forming the protein. Because this process occurs in vitro (outside of living cells), it allows rapid protein production without the need for cell transformation, growth, or cloning into a host organism.
Cell-free systems are particularly useful for fast prototyping, testing gene constructs, or producing proteins that may be toxic to living cells. The entire process still follows the central dogma (DNA → RNA → Protein), but it happens in a controlled biochemical environment rather than inside a living cell.
3.5. How does it work in nature/biological systems?
R/:In nature, gene expression follows the central dogma of molecular biology. First, the DNA sequence of a gene is transcribed into messenger RNA (mRNA) by RNA polymerase. In eukaryotic cells, the initial transcript (pre-mRNA) undergoes processing, including 5’ capping, polyadenylation, and splicing to remove introns.
Once mature mRNA is formed, it is transported to the cytoplasm, where ribosomes bind to it and translate the nucleotide sequence into an amino acid chain. Transfer RNAs (tRNAs) bring the appropriate amino acids according to codon–anticodon pairing. The resulting polypeptide then folds into its functional three-dimensional structure, sometimes with the help of molecular chaperones.
This tightly regulated process ensures that proteins are produced at the right time, in the right amount, and in the appropriate cellular context.
Describe how a single gene codes for multiple proteins at the transcriptional level.
R/:A single gene can produce multiple proteins at the transcriptional level mainly through alternative splicing. In eukaryotic cells, genes are composed of exons and introns. After transcription, the initial RNA transcript (pre-mRNA) contains both regions. During RNA processing, introns are removed, and exons are joined together. However, the cell does not always combine exons in the same way. Different exons can be included or excluded, generating distinct mRNA transcripts from the same gene. These different mRNAs are then translated into different protein isoforms. This mechanism increases protein diversity without increasing the total number of genes in the genome.
Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein.
(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).
R/:I would like to sequence DNA from gastrointestinal stromal tumor (GIST) samples, specifically focusing on mutations in the KIT and PDGFRA genes. Mainly because KIT mutations cause the receptor to be constitutively activated, meaning it continuously signals cell proliferation even without an external signal. This is why sequencing this gene is important, understanding the specific mutation helps select the most effective existing treatment, such as tyrosine kinase inhibitors like imatinib, for each specific patient. Additionally, since mutations can vary between patients, sequencing improves the precision of medicine by allowing more personalized and targeted therapies.
(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?
R/: I would use Illumina
Also answer the following questions:
Is your method first-, second- or third-generation or other? How so?
R/: It’s second generation because it uses massively parallel sequencing, reading millions of fragments simultaneously, unlike Sanger which sequences one fragment at a time.
What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.
R/: The input it’s DNA from GIST samples.The indispensable steps are first fragmentation because we have a long DNA and illumin reads short fragments, after we do adapter ligation so the machine recognize each fragment and lastly a PCR amplification so we have enough material to read.
What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)?
R/:After preparation, the fragments are attached to a flow cell where cluster generation occurs each fragment is copied thousands of times to amplify the signal. Then, sequencing by synthesis begins: fluorescently labeled nucleotides are added one at a time, each base (A, T, C, G) emitting a different color. A camera captures an image after each incorporation, and the computer translates each color into a base this is called base calling. This process repeats for each position along the fragment
What is the output of your chosen sequencing technology?
R/:The output of Illumina sequencing is millions of short reads sequences of bases (A, T, C, G) typically 150-300 base pairs long. These reads are then aligned to a reference genome using bioinformatics tools to identify mutations in the KIT gene, such as substitutions or insertions/deletions, that may be driving GIST.
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. 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! :)
R/: I would like to synthesize a genetic biosensor circuit designed to detect early increases in HSP90 dependence in KIT-mutant GIST cells. The biosensor would consist of a synthetic DNA sequence that, when inserted into tumor cells, monitors HSP90 activity levels and produces a detectable signal (such as a fluorescent protein) when levels rise above a threshold. This is clinically relevant because increased HSP90 dependence is an early indicator of cellular adaptation preceding imatinib resistance, meaning the biosensor could alert clinicians before full resistance develops, allowing earlier treatment adjustments. The specific sequence would include a HSP90-responsive promoter driving a reporter gene, designed to activate transcription proportionally to HSP90 activity levels in KIT-mutant cells.
(ii) What technology or technologies would you use to perform this DNA synthesis and why?
R/:For synthesizing my biosensor genetic circuit, I would use array-based phosphoramidite synthesis, as offered by Twist Bioscience. This technology is ideal because it allows precise, high-throughput synthesis of specific designed sequences at relatively low cost, without needing a biological template.
Also answer the following questions:
What are the essential steps of your chosen sequencing methods?
R/:The essential steps are: (1) the DNA sequence is designed computationally; (2) synthesis occurs on a chip where nucleotides are added one at a time chemically to growing DNA chains; (3) each nucleotide is protected by a chemical group that is removed before the next base is added, allowing controlled sequential addition; (4) the completed sequences are cleaved from the chip and assembled into longer constructs if needed through Gibson assembly or similar methods.
What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?
R/:The main limitations are accuracy, errors can accumulate as the sequence gets longer, making synthesis of very long sequences challenging. Additionally, longer sequences are more expensive and slower to produce. Array-based synthesis improves scalability but error rates remain a concern, often requiring sequence verification after synthesis using Sanger or Illumina sequencing.
5.3 DNA Edit
(i) What DNA would you want to edit and why? In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why?
R/:I would want to edit the KIT gene in GIST tumor cells using CRISPR-Cas9 technology. Specifically, I would correct the activating mutations in KIT that cause constitutive activation of the tyrosine kinase receptor, driving uncontrolled cell proliferation. By editing the mutated sequence back to its wildtype form, the receptor would only signal when appropriate, stopping uncontrolled tumor growth at its source rather than just blocking it pharmacologically as imatinib does. This approach is particularly compelling because imatinib resistance is a major clinical challenge in GIST patients eventually develop secondary mutations that render the drug ineffective. A CRISPR-based correction of the primary KIT mutation could offer a more permanent solution, potentially eliminating the tumor’s ability to develop resistance. Additionally, since different patients carry different KIT mutations, CRISPR could be personalized to target each patient’s specific mutation, aligning with the precision medicine approach.
(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?
R/:I would use CRISPR-Cas9 to correct the activating KIT mutations in GIST tumor cells, as it allows precise, targeted editing of specific DNA sequences and can be personalized for each patient’s mutation. CRISPR-Cas9 edits DNA through the following steps: (1) the guide RNA (gRNA) directs the Cas9 protein to the specific mutated KIT sequence; (2) Cas9 creates a double-strand break at that exact location; (3) a correct DNA template is provided and the cell repairs the break using homology-directed repair (HDR), replacing the mutated sequence with the corrected wildtype sequence.
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?
R/:The preparation steps include: (1) designing a gRNA that matches the specific KIT mutation of the patient — this is done computationally; (2) preparing the Cas9 protein or encoding it in a plasmid; (3) preparing a HDR template containing the correct wildtype KIT sequence; (4) delivering all components into the tumor cells via a viral vector or nanoparticles. The inputs are therefore: the gRNA, Cas9 enzyme, HDR DNA template, and the target tumor cells.
What are the limitations of your editing methods (if any) in terms of efficiency or precision?
R/:The main limitations are: (1) off-target edits Cas9 may cut unintended genomic locations causing unwanted mutations; (2) low HDR efficiency, especially in non-dividing cells; (3) delivery challenges getting CRISPR machinery efficiently into tumor cells in vivo remains technically difficult; (4) since KIT mutations vary between patients, a new gRNA must be designed for each case, making large-scale application complex.
Week 1 HW: Principles and Practices
1. Biological engineering application or tool to develop and why
Idea
Mi idea its a biological biosensor designed to detect early changes in dependence on the molecular chaperone HSP90 in KIT mutant gastrointestinal stromal tumors (GIST), as functional indicators of cellular adaptation preceding the development of resistance to imatinib.
Figure 1. Conceptual schematic of the proposed biological biosensor.
Image generated using AI for graphical representation purposes only.
Gastrointestinal Stromal Tumors (GIST)
Gastrointestinal stromal tumors (GIST) are rare mesenchymal neoplasms but represent the most common subtype of sarcoma of the gastrointestinal tract. The majority of GISTs harbor activating mutations in the receptor tyrosine kinases KIT or PDGFRA, which has enabled the development of targeted therapies such as imatinib. Despite these advances, progression to metastatic disease remains a major clinical challenge, even among patients who share similar mutational profiles (Hemming et al., 2018a; Antonescu et al., s. f.).
Imatinib and KIT Mutations
Imatinib mesylate is a selective tyrosine kinase inhibitor that binds to the ATP binding site of receptor tyrosine kinases, thereby blocking their phosphorylation and downstream proliferative and anti apoptotic signaling cascades. In GIST, its primary therapeutic targets are KIT and, in specific subgroups, PDGFRA, resulting in inhibition of constitutive oncogenic signaling and induction of cell cycle arrest and apoptosis (Fletcher & Rubin, 2007). This approach represented one of the earliest successes of molecularly targeted therapy in oncology, leading to substantial improvements in survival in patients with advanced or metastatic disease (Fletcher & Rubin, 2007).
Approximately 70–85% of GISTs carry activating mutations in KIT, most frequently in exon 11, followed by exons 9, 13, and 17. These mutational subtypes strongly influence tumor biology and therapeutic response to imatinib. Tumors harboring exon 11 mutations exhibit the highest response rates, whereas other mutational subtypes often require dose escalation or develop secondary resistance through additional kinase domain mutations, ultimately leading to clinical progression after an initial response (Fletcher & Rubin, 2007; Killock, 2022).
Figure 2. Molecular structure of imatinib, highlighting its role as a tyrosine kinase inhibitor in KIT-driven tumors and the biological context in which resistance mechanisms may emerge.
Role of HSP90 in GIST and Therapeutic Resistance
Heat shock protein 90 (HSP90) is a molecular chaperone essential for the stability and functional activity of multiple oncogenic proteins. In GIST, KIT both wild type and mutant critically depends on HSP90 to maintain its active conformation. Inhibition of HSP90 results in destabilization and proteasomal degradation of KIT, including variants harboring mutations associated with imatinib resistance, positioning HSP90 as an alternative or complementary therapeutic target to conventional tyrosine kinase inhibitors (NIHMS212509; Frontiers in Immunology, 2024).
In addition, HSP90 is involved in biological processes linked to tumor progression and therapeutic resistance, which has driven interest in combination strategies for patients with advanced or refractory GIST (Frontiers in Immunology, 2024).
Acquired Resistance to Imatinib: A Central Clinical Problem
Despite the initial efficacy of imatinib in most patients with GIST, a substantial proportion eventually develops acquired resistance, leading to tumor progression and limited therapeutic options. This resistance is driven by heterogeneous molecular mechanisms that cannot always be explained solely by the emergence of new KIT or PDGFRA mutations, complicating both prediction and timely clinical monitoring.
Biosensor Design Concept
The proposed biosensor is conceived as a functional molecular sensor designed to detect cellular states associated with acquired resistance to imatinib in GIST, rather than relying solely on static mutational information. While current clinical stratification primarily focuses on the presence or absence of mutations in KIT or PDGFRA, resistance to imatinib frequently arises through dynamic molecular adaptations that are not fully captured by genomic profiling alone (Fletcher & Rubin, 2007; Killock, 2022). This biosensor aims to bridge that gap by translating complex intracellular signaling states into a measurable and interpretable output, reflecting functional tumor behavior beyond mutational status.
Conceptual Design
At a conceptual level, the biosensor would operate by coupling a biological recognition element sensitive to KIT associated signaling activity or HSP90 dependent protein stability to a reporter output that reflects the functional response of the cell to imatinib exposure. Rather than detecting specific mutations, the sensor would respond to changes in intracellular conditions indicative of loss of KIT dependency, activation of alternative survival pathways, or increased reliance on molecular chaperones such as HSP90, all of which have been implicated in imatinib resistance in GIST (Fletcher & Rubin, 2007; NIHMS212509; Frontiers in Immunology, 2024).
Need for New Functional Analytical Tools
In this context, there is an unmet need for tools capable of capturing the functional state of tumor cells and their dependence on KIT-associated oncogenic pathways, beyond static mutational characterization. A biosensor designed to detect dynamic changes in molecular activity related to imatinib sensitivity or resistance could complement current genomic approaches and provide a pathway toward earlier identification of therapeutic resistance.
2. Governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm).
Clinical safety and non-malfeasance
How to ensure the biosensor is used strictly to support cancer treatment and does not cause harm?
-How to prevent its use as a standalone diagnostic tool, and ensure that clinical decisions are not made solely based on biosensor readouts without proper medical interpretation?
Responsible clinical integration
How should the biosensor be integrated into clinical workflows so that it provides additional biological information rather than replacing clinician judgment?
Who is authorized to interpret the results, and under what conditions should the data influence treatment decisions?
Equitable access
Who will have access to this technology?
How can barriers related to cost, infrastructure, or healthcare disparities be minimized so that the benefits of early adaptation detection are available to a broader patient population and not only to a privileged few?
Research integrity and prevention of misuse
How can the biosensor be governed to ensure it is used to better understand KIT-mutant tumor biology and mechanisms of drug adaptation, rather than being applied in ways that could intentionally or unintentionally cause harm?
3. Different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”)
Governance Action 1: Clinical use restriction policy
Purpose: Currently, emerging biosensors may be interpreted as direct indicators of treatment response or resistance. This action proposes restricting the use of the biosensor strictly as a supportive clinical and research tool, not as a standalone diagnostic or decision making instrument.
Design: This would require hospitals and clinical research institutions to implement internal guidelines stating that biosensor outputs must be interpreted by trained oncologists or molecular pathologists. Ethics committees and hospital review boards would approve its use, and researchers would include clear disclaimers in publications and clinical protocols.
Assumptions: This action assumes that clinicians will follow institutional guidelines and that proper training will be available. It also assumes that misuse mainly occurs at the interpretation stage rather than during data generation.
Risks of Failure & “Success”: If the policy fails, clinicians might still rely too heavily on biosensor readouts. If it succeeds too well, overly strict rules could slow down clinical adoption or discourage innovation, delaying potential patient benefits.
Governance Action 2: Mandatory validation and transparency requirements (technical strategy)
Purpose: At present, early biomarkers may be applied before their limitations are fully understood. This action proposes requiring rigorous validation and transparency regarding uncertainty, sensitivity, and limitations of the biosensor.
Design: Academic researchers and biotech developers would be required to publish validation data, uncertainty ranges, and known limitations alongside biosensor results. Regulatory agencies or funding bodies could mandate this as a condition for approval or funding.
Assumptions: This assumes that validation metrics can adequately capture real-world biological variability and that transparency will lead to more responsible interpretation rather than confusion.
Risks of Failure & “Success”: Failure could occur if validation data are incomplete or misleading. If successful, excessive emphasis on uncertainty might reduce clinician confidence or limit the biosensor’s perceived usefulness.
Governance Action 3: Equity oriented access and deployment incentives
Purpose: Advanced molecular tools often benefit only well-resourced institutions. This action proposes promoting equitable access so the biosensor does not widen existing healthcare disparities.
Design: Public research institutions, funding agencies, or non-profit organizations could subsidize deployment in public hospitals or low-resource settings. Open research protocols or cost-reduction strategies could be encouraged at early development stages.
Assumptions: This assumes that cost and infrastructure are the main barriers to access, and that equitable deployment is feasible once the technology is validated.
Risks of Failure & “Success”: If unsuccessful, the technology may remain inaccessible to most patients. If overly successful, rapid deployment without adequate training or infrastructure could increase misinterpretation or misuse
4. Score each of the governance actions against the rubric of policy goals.
Does the option:
Option 1
Option 2
Option 3
Enhance Biosecurity
• By preventing incidents
1
1
3
• By helping respond
2
1
n/a
Foster Lab Safety
• By preventing incident
2
1
n/a
• By helping respond
3
1
n/a
Protect the environment
• By preventing incidents
n/a
n/a
n/a
• By helping respond
n/a
n/a
n/a
Other considerations
• Minimizing costs and burdens to stakeholders
1
2
3
• Feasibility?
1
1
2
• Not impede research
1
2
2
• Promote constructive applications
2
1
1
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.
Based on the scoring, I would prioritize a combination of Option 1 (clinical-use restriction policy) and Option 2 (mandatory validation and transparency requirements), as these most directly support non-malfeasance by preventing harmful clinical misuse and reducing misinterpretation of biosensor outputs. While Option 3 (equitable access incentives) is ethically important, prioritizing it too early could increase risks if the technology is deployed before sufficient validation. This approach assumes that clinicians and researchers will follow institutional guidelines and that validation data will be communicated clearly. A key trade-off is that stricter rules and validation requirements may slow adoption and increase administrative burden, but this is justified to protect patient safety. This recommendation is primarily directed toward academic research institutions, clinical research hospitals, and institutional review boards (IRBs), which play a central role in approving, overseeing, and guiding the ethical use of emerging biomedical technologies. This exercise also highlighted the ethical risk of over-relying on early biological signals, even when technologies are developed with good intentions, as well as the tension between promoting access and ensuring safety. As a result, governance actions that clearly define scope of use, emphasize transparency, and support phased deployment are essential to prevent unintended harm.
Questions from lecture’s 2
Homework Questions from Professor Jacobson: [Lecture 2 slides]
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?
R/:DNA polymerase is the enzyme that copies DNA in cells. It is very accurate, but not perfect. It usually makes one mistake every 100 million to 10 billion bases (DNA letters). The human genome is about 3 billion bases long, so if there were no corrections, we could get a few errors each time the genome is copied. But biology has special systems that fix those mistakes. These systems include proofreading (checking as the DNA is copied) and DNA repair mechanisms. Thanks to these, most errors are fixed, and the final copy is very accurate.
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?
R/: There are many different ways to write the DNA code for a human protein. That’s because the genetic code is redundant most amino acids can be coded by more than one set of three DNA letters (called codons). For example, the amino acid leucine has six different codons. So, a protein with 300 amino acids can have many thousands of possible DNA sequences that all make the same protein. But in practice, not all of these versions work well. Some codons are rare and slow down protein production. Some DNA sequences can fold into weird shapes that block the process. Also, each organism has codon preferences, meaning it uses some codons more than others. That’s why scientists choose special codons when they want the protein to be made properly in a specific cell.
Homework Questions from Dr. LeProust: [Lecture 2 slides]
What’s the most commonly used method for oligo synthesis currently?
R/: The most common way to make short pieces of DNA (called oligos) is by using a chemical method that adds one DNA letter at a time. This method is called phosphoramidite synthesis. It’s like building a word by adding one letter at a time, very carefully. Scientists do this on a small surface in the lab. It works well for making short DNA pieces, which are used in many experiments.
Why is it difficult to make oligos longer than 200nt via direct synthesis?
R/: It’s hard to make DNA oligos longer than 200 bases because the process isn’t perfect. Each time a new DNA letter is added, there’s a small chance it goes wrong or doesn’t stick. If you’re only adding a few letters, most of the strands are correct. But if you try to add 200 letters, those small mistakes add up and many of the strands end up incomplete or with errors. That’s why longer DNA pieces are usually made by putting together shorter, more accurate pieces.
Why can’t you make a 2000bp gene via direct oligo synthesis?
R/: You can’t make a 2000bp gene directly with oligo synthesis because the process isn’t accurate enough for something that long. When scientists make DNA, they add one letter at a time, and each step has a small chance of making a mistake. For short pieces of DNA (like 100–200 bases), the process works well. But if you try to make something as long as 2000 bases all at once, too many mistakes happen, and most of the DNA ends up broken or wrong. So instead, scientists make shorter pieces and then join them together to build the full gene.
Homework Question from Professor George Church: [Lecture 2 slides]
Choose ONE of the following three questions to answer; and please cite AI prompts or paper citations used, if any.
[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”?
R/: The 10 essential amino acids in all animals are: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arginine (which is considered essential in some conditions, like during growth or illness). These amino acids are called “essential” because animals cannot make them on their own and must obtain them from food sources.
The concept of the “Lysine Contingency,” as described in Professor George Church’s lecture (slide #4), is a biosafety strategy in synthetic biology. It involves engineering organisms to be dependent on lysine, meaning they cannot survive without an external supply of it. This acts as a safety switch if the organism escapes into the environment, where lysine is not available, it cannot grow or survive.
Understanding that lysine is essential and not naturally produced by the organism itself makes this strategy seem very effective and logical. It adds a layer of control to prevent genetically modified organisms from spreading outside the lab, which is an important concern in biotechnology research.
Sources:
Prof. George Church – Lecture 2, Slide #4: “The Genetic Code”
Generate an artistic design using the GUI at opentrons-art.rcdonovan.com.
Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons.
You may use AI assistance for this coding — Google Gemini is integrated into Colab (see the stylized star bottom center); it will do a good job writing functional Python, while you probably need to take charge of the art concept.
If you’re a proficient programmer and you’d rather code something mathematical or algorithmic instead of using your GUI coordinates, you may do that instead.
This paper addresses a key limitation in GPCR signaling measurement: typically, signals can only be measured one at a time and at a single time point, making it impossible to observe multiple signaling pathways or their dynamics over time. The authors used the Opentrons to automate drug delivery and imaging, allowing them to measure various signaling pathways simultaneously and track their changes over time rather than only capturing a final readout. This automation enabled the discovery that GPR68 cAMP responses are pH-dependent in a kinetic way, and that Ogerin has an unexpected off-target effect at high concentrations through phosphodiesterase inhibition.
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.
While your description/project idea doesn’t need to be set in stone, we would like to see core details of what you would automate. This is due at the start of lecture and does not need to be tested on the Opentrons yet.
For my final project, I would automate the process of exposing GIST cells to different imatinib concentrations to observe how HSP90 dependence changes in response to the drug. I would automate this part because it requires testing many concentrations repeatedly, and automating it would reduce pipetting errors that could compromise the dilution series, while also saving time. Additionally, I would automate the biosensor signal readout across all wells, allowing me to track changes in HSP90 activity over time rather than capturing only a single final measurement
