Week 1 HW: Principles and Practices

Part1: Assignment

1. The Project Concept:

Integrated Plant-Based Bone Scaffolds The field of regenerative medicine currently relies heavily on static bone scaffolds that provide structural support but lack the ability to interact with the biological environment. I propose the development of a 3D bioprinted smart scaffold designed from sustainable, plant-based materials. This system will serve a dual purpose by providing a physical matrix for bone growth and integrating biosensors for real-time physiological monitoring. By using materials like alginate or cellulose, this approach offers a personalized and environmentally responsible alternative to traditional synthetic implants.

Technical Phases:

• Phase 1: Structural Foundation. The scaffold is bioprinted using biodegradable plant polymers tailored to the specific geometry of a patient’s bone defect. This provides the necessary mechanical integrity to support new tissue formation.

• Phase 2: Biological Intelligence. Biosensors are embedded within the matrix to monitor variables such as pH levels, calcium concentration, and mechanical strain. Simultaneously, a controlled delivery system releases growth factors to promote rapid vascularization and bone density.

• Phase 3: Controlled Degradation. As natural bone tissue regenerates and takes over the load-bearing responsibilities, the scaffold undergoes programmed biodegradation. This leaves behind only healthy, natural bone without the need for secondary surgeries to remove permanent hardware.

2. Governance Goals for Ethical Bioengineering

To ensure this technology aligns with safety and ethical standards, the following governance goals have been established.

Goal A: Environmental Sustainability and Non-Toxicity

The project must ensure that the transition to plant-based materials does not result in unintended ecological or biological consequences.

• Sub-goal: Utilize biodegradable materials that break down into inert metabolites to avoid systemic toxicity.

• Sub-goal: Standardize sourcing methods to ensure that plant extraction does not disrupt local ecosystems or biodiversity.

Goal B: Clinical Efficacy and Patient Protection

The integration of active growth factors requires strict oversight to prevent adverse biological reactions.

• Sub-goal: Validate the biocompatibility of all plant-derived components to eliminate the risk of chronic inflammation or immune rejection.

• Sub-goal: Implement precise delivery protocols for growth factors to prevent unregulated cellular proliferation.

3. Proposed Governance Actions

Action 1: Regulatory Frameworks for Bio-Hybrid Materials

The primary purpose is to establish clear safety benchmarks for plant-based medical devices that do not fit into existing regulatory categories. This involves collaboration with the MHRA and FDA to define specific testing protocols for the degradation rates of cellulose-based implants. The design of this action requires rigorous longitudinal studies to confirm that the breakdown of these materials is safe over several years. One significant risk is that high regulatory hurdles may delay the delivery of these life-changing treatments to patients in need.

Action 2: Data Security Protocols for In-Vivo Biosensors

As these scaffolds generate continuous streams of patient health data, it is vital to establish ethical data handling practices. The design of this action includes the development of encrypted transmission standards to ensure that sensitive biological information is only accessible to authorized medical personnel. A key assumption is that patient data can be transmitted wirelessly without compromising the physical integrity of the scaffold. The risk of failure involves potential cybersecurity vulnerabilities that could expose private health metrics.

Action 3: Global Sustainability Certification

This action focuses on creating a “Green Biotech” certification to encourage the use of eco-friendly materials in the medical industry. By working with the United Nations Environment Programme, we can set international standards for the carbon footprint of medical manufacturing. This assumes that a global market exists for sustainable medical products. However, a potential risk is that the cost of obtaining such certifications could increase the final price of the scaffold, potentially limiting access for lower-income healthcare systems.

4. Scoring of Governance Actions

(Note: 1 represents the highest alignment with the goal)

5. Prioritization and Ethical Considerations

Upon reviewing the scores, Action 1 (Regulation of Biodegradable Biomaterials) is the highest priority. Without a validated safety profile and regulatory approval, the clinical and environmental benefits of the scaffold cannot be realized. While Action 3 is easier to implement, it remains secondary to the fundamental safety of the patient. During the development of this proposal, an important ethical concern arose regarding “Biotelemetry Equity.” If smart scaffolds become the gold standard, there is a risk that only patients in high-resource settings will benefit from real-time healing monitoring. To address this, governance actions should include incentives for companies to develop “passive” versions of the scaffold that provide high-quality structural support at a lower cost for global distribution. Relevant Audiences The recommendations for these governance actions are directed toward the FDA and the World Health Organization. These bodies are essential for establishing the international safety and sustainability standards required to bring 3D bioprinted plant-based scaffolds into mainstream clinical practice.

6. References

10.1109/SENSORS56945.2023.10325163

10.1002/adhm.202102807

https://cordis.europa.eu/project/id/101177877

Part2: Lab Preparation

It was not applicable for Committed Listeners

Part3: Week 2 Lecture Prep

Questions from Professor Jacobson

Q1: Even though it is not perfect, the precision of nature’s machinery for copying DNA is actually quite staggering. The intrinsic error rate of DNA polymerase is approximately one mistake for every million base pairs copied (10^(−6)). For context, the human genome comprises around 3.2 billion base pairs. If we were to depend solely on polymerase, each and every cell division would give rise to innumerable arbitrary mutations. This would have catastrophic consequences for the stability of life over many generations, but biology handles this massive discrepancy through a multi-layered proofreading and repair system. First, the polymerase itself has a ‘delete’ function whereby it can sense a mismatch, back up and correct it. Secondary systems, such as the MutS repair complex, then scan the DNA afterwards to detect any rare mistakes that have slipped through the first net. This combined effort brings the final error rate down to approximately one in a billion. This makes it reliable enough to maintain the blueprint of a human being.

Q2: When it comes to coding proteins, there is an incredible amount of flexibility because the genetic code is redundant. Since most amino acids are linked to several different three letter codons, you could theoretically write the DNA sequence for an average human protein in more ways than there are atoms in the universe. In practice, however, most of these sequences just do not work in a living cell. A major reason for this is the physical shape the RNA takes. If a sequence accidentally folds into a tight hairpin or a complex secondary structure, the cellular machinery gets physically stuck, much like a zipper hitting a snag in fabric. There are also issues with sequences having extreme GC ratios, which makes them too unstable or difficult for the cell to handle. Plus, cells have internal “cleavage rules” where they recognize certain patterns as signals to chop up the genetic instructions before they can even be translated. So, while the theoretical options are infinite, the actual biological grammar needed to express a protein is much more restrictive.

Questions from Dr. LeProust

Q1: The standard approach is the phosphoramidite method, which follows a four-step cycle. It starts with coupling the phosphoramidite to the chain, followed by capping any unreacted sites to prevent errors. The link is then oxidized to stabilize it, and finally, the growing chain is deblocked to prepare it for the next nucleotide addition.

Q2: The main issue is the cumulative effect of coupling efficiency. Even with a very high success rate for each step, small errors add up quickly over many cycles. By the time you reach 200 nucleotides, these compounding errors and the accumulation of truncated or incorrect sequences make it nearly impossible to retrieve a pure, full-length product.

Q3: Synthesizing a 2000bp gene directly would require 2000 consecutive coupling cycles without a single mistake, which is chemically unrealistic with current technology. The yield of the correct full-length molecule would be effectively zero. Beyond the chemistry, the sheer cost and the buildup of chemical damage over such a long process make it much more practical to assemble smaller fragments rather than trying to print the whole gene at once. The ten amino acids that are generally considered to be essential for animals are: arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan and valine. They are classified as essential because animals cannot synthesise sufficient quantities of their carbon skeletons, meaning they must be obtained through diet or from symbiotic relationships with microbes.

Questions from George Church

The ’lysine contingency’ refers to the fact that animals have lost the ability to produce lysine independently. From an evolutionary perspective, this seems less like a biological flaw and more like a way for ecosystems to create a reliance between different species. Our specific need for this amino acid has shaped the world as we know it, creating massive agricultural systems and complex food webs that would not exist if we could produce lysine ourselves. For example, the industrial production of lysine for livestock feed is a significant global enterprise centred on optimising animal growth. Without this essential amino acid, the entire economic and agricultural infrastructure might not exist, and we might not have moved towards such extensive farming practices. I wonder if, over millions of years, animals became dependent on lysine as a kind of self-imposed evolutionary trade-off. Perhaps it was once non-essential, but because it was so abundant in the environment, our ancestors eventually ’turned off’ the expensive metabolic machinery needed to produce it. In that sense, what we call a contingency is really just nature’s efficient way of outsourcing production to the surrounding environment.

References;

https://www.ncbi.nlm.nih.gov/books/NBK557845/

https://www.ncbi.nlm.nih.gov/books/NBK234922/

Week 2 HW: DNA Read Write and Edit

✨Part 1: Benchling & In silico Gel Art

I simulated a restriction digest on λ DNA in Benchling using enzymes like EcoRI, HindIII, and BamHI, EcoRV, Kpnl. By comparing the band patterns, I could visualize how different enzymes cut the DNA into fragments of varying sizes. This simulation helped me understand how we verify DNA fingerprints before moving to synthesis.

✨ Part 3: DNA Design Challenge

3.1. Choose your protein

My Choice: For this assignment, I chose Lysostaphin, a glycylglycine endopeptidase enzyme. This protein is naturally produced by Staphylococcus simulans to kill rival bacteria.

Why I chose it:

My background is in dentistry and tissue engineering, where peri-implantitis (infection around dental implants) is a critical failure mode. These infections are often caused by antibiotic-resistant Staphylococcus aureus (MRSA) forming biofilms on the titanium surface. Lysostaphin is capable of slicing through the cell wall of S. aureus, destroying the biofilm effectively where traditional antibiotics fail. It represents a potential “biological scalpel” for saving failing implants.

Sequence:

Using UniProt, I obtained the amino acid sequence for Lysostaphin:

sp|P10547|LSTP_STASI Lysostaphin OS=Staphylococcus simulans OX=1286 GN=lss PE=1 SV=2 MKKTKNNYYTRPLAIGLSTFALASIVYGGIQNETHASEKSNMDVSKKVAEVETSKAPVEN TAEVETSKAPVENTAEVETSKAPVENTAEVETSKAPVENTAEVETSKAPVENTAEVETSK APVENTAEVETSKAPVENTAEVETSKAPVENTAEVETSKAPVENTAEVETSKAPVENTAE VETSKAPVENTAEVETSKAPVENTAEVETSKAPVENTAEVETSKAPVENTAEVETSKALV QNRTALRAATHEHSAQWLNNYKKGYGYGPYPLGINGGMHYGVDFFMNIGTPVKAISSGKI VEAGWSNYGGGNQIGLIENDGVHRQWYMHLSKYNVKVGDYVKAGQIIGWSGSTGYSTAPH LHFQRMVNSFSNSTAQDPMPFLKSAGYGKAGGTVTPTPNTGWKTNKYGTLYKSESASFTP NTDIITRTTGPFRSMPQSGVLKAGQTIHYDEVMKQDGHVWVGYTGNSGQRIYLPVRTWNK STNTLGVLWGTIK

3.2. Reverse Translate

Using the online resource at https://www.bioinformatics.org/, I converted the amino acid sequence(taken from https://www.uniprot.org) of the Lysostaphin protein back into its potential DNA sequence. This technique follows the Central Dogma of Molecular Biology, which outlines the flow of genetic information from DNA to RNA and finally to protein. By reversing this sequence, the tool creates a logical nucleotide chain capable of producing that specific protein.

Converted Sequence:

reverse translation of Untitled to a 1479 base sequence of most likely codons. atgaaaaaaaccaaaaacaactattatacccgcccgctggcgattggcctgagcaccttt gcgctggcgagcattgtgtatggcggcattcagaacgaaacccatgcgagcgaaaaaagc aacatggatgtgagcaaaaaagtggcggaagtggaaaccagcaaagcgccggtggaaaac accgcggaagtggaaaccagcaaagcgccggtggaaaacaccgcggaagtggaaaccagc aaagcgccggtggaaaacaccgcggaagtggaaaccagcaaagcgccggtggaaaacacc gcggaagtggaaaccagcaaagcgccggtggaaaacaccgcggaagtggaaaccagcaaa gcgccggtggaaaacaccgcggaagtggaaaccagcaaagcgccggtggaaaacaccgcg gaagtggaaaccagcaaagcgccggtggaaaacaccgcggaagtggaaaccagcaaagcg ccggtggaaaacaccgcggaagtggaaaccagcaaagcgccggtggaaaacaccgcggaa gtggaaaccagcaaagcgccggtggaaaacaccgcggaagtggaaaccagcaaagcgccg gtggaaaacaccgcggaagtggaaaccagcaaagcgccggtggaaaacaccgcggaagtg gaaaccagcaaagcgccggtggaaaacaccgcggaagtggaaaccagcaaagcgctggtg cagaaccgcaccgcgctgcgcgcggcgacccatgaacatagcgcgcagtggctgaacaac tataaaaaaggctatggctatggcccgtatccgctgggcattaacggcggcatgcattat ggcgtggatttttttatgaacattggcaccccggtgaaagcgattagcagcggcaaaatt gtggaagcgggctggagcaactatggcggcggcaaccagattggcctgattgaaaacgat ggcgtgcatcgccagtggtatatgcatctgagcaaatataacgtgaaagtgggcgattat gtgaaagcgggccagattattggctggagcggcagcaccggctatagcaccgcgccgcat ctgcattttcagcgcatggtgaacagctttagcaacagcaccgcgcaggatccgatgccg tttctgaaaagcgcgggctatggcaaagcgggcggcaccgtgaccccgaccccgaacacc ggctggaaaaccaacaaatatggcaccctgtataaaagcgaaagcgcgagctttaccccg aacaccgatattattacccgcaccaccggcccgtttcgcagcatgccgcagagcggcgtg ctgaaagcgggccagaccattcattatgatgaagtgatgaaacaggatggccatgtgtgg gtgggctataccggcaacagcggccagcgcatttatctgccggtgcgcacctggaacaaa agcaccaacaccctgggcgtgctgtggggcaccattaaa

3.3. Codon optimization

1. Why do we optimize codons?

I need to ensure my DNA “reads” fluently in the host organism. If the codons are rare in the host, protein production will stall. Optimization replaces these rare codons with the host’s preferred ones without changing the final protein structure.

2. Which organism did you choose and why?

I chose Escherichia coli (E. coli) for codon optimization. While my final application is for dental patients, E. coli is the industrial standard for manufacturing proteins. By optimizing for E. coli, I can grow large vats of bacteria, induce them to produce Lysostaphin, and then purify the enzyme to be applied as a dental gel or coating for implants.

Optimization Result:

3.4. You have a sequence! Now what?

Now that I have the optimized DNA sequence, the goal is recombinant protein production to create a therapeutic solution for peri-implantitis.

1. Cloning

I will insert the optimized Lysostaphin gene into an expression vector (plasmid). This plasmid acts as the delivery vehicle, containing a strong promoter that signals the host cell to begin producing the protein.

2. Transformation

The recombinant plasmid is put into $E. coli$ bacteria. This is achieved through a process called transformation (such as heat-shock), which allows the bacterial cells to take up the foreign DNA and host it within their own systems.

3. Expression

The bacteria act as biological factories, following the Central Dogma of Molecular Biology. The $E. coli$ cells read the optimized DNA instructions to produce mRNA via transcription, which is then translated into the Lysostaphin Protein. Because the codons were optimized for $E. coli$ (K12), the translation process is highly efficient with a high protein yield.

4. Purification Finally, I will extract the protein from the bacterial culture. Through a series of filtration and chromatography steps, the Lysostaphin is isolated from other bacterial proteins. The result is a pure protein that can be formulated into a bioactive gel designed to target and eliminate $Staphylococcus$ biofilms in patients with peri-implantitis.

3.5. [Optional] How does it work in nature/biological systems?

1. Describe how a single gene codes for multiple proteins at the transcriptional level.

A gene is first transcribed into a long RNA molecule called pre-mRNA. This pre-mRNA contains both coding regions (exons) and non-coding regions (introns). Through a process called Alternative Splicing, the cell can cut out the introns and stitch the exons together in different combinations. Just like editing a movie scene in different ways, different combinations of exons create different final mRNA molecules. $$Different \ mRNA \ variants \rightarrow Different \ Proteins$$ Because the mRNA sequence changes, the resulting amino acid sequence changes too. This allows a single gene to code for multiple different protein isoforms, maximizing the efficiency of the genome.

2. Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!

In nature, the enzyme RNA Polymerase reads the DNA template strand and synthesizes a single-stranded RNA molecule based on base complementarity.

• A pairs with U (Uracil replaces Thymine in RNA).

• T pairs with A.

• G pairs with C.

• C pairs with G.

After transcription, the Ribosome reads the mRNA in groups of three nucleotides called codons. Each codon corresponds to one specific amino acid.

Alignment for Lysostaphin (Start of Sequence):

Here is the flow of information for the first 6 amino acids of my Lysostaphin protein (MTTTPD…).

• DNA (Coding Strand): ATG ACC ACC ACC CCG GAT

• mRNA (Transcription): AUG ACC ACC ACC CCG GAU

• Protein (Translation): M T T T P D

Key:

• M (Methionine): The “Start” signal.

• T (Threonine): A polar amino acid.

• P (Proline): Adds structural rigidity.

• D (Aspartic Acid): Negatively charged.

Part 4: Prepare a Twist DNA Synthesis Order

I created a new sequence in Benchling named Lysostaphin_e.coli. I combined my optimized gene with the standard parts required for E. coli expression:

Promoter (BBa_J23106):

TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGC

RBS (BBa_B0034):

CATTAAAGAGGAGAAAGGTACC

Start Codon:

ATG

Coding Sequence:

ATGAAAAAAACGAAAAACAATTACTATACCCGCCCGCTGGCCATTGGCCTGAGCACTTTTGCGCTGGCGAGCATCGTGTACGGCGGCATTCAGAACGAAACCCATGCGAGCGAAAAAAGCAATATGGATGTAAGCAAAAAAGTGGCGGAAGTTGAAACCAGCAAAGCGCCGGTCGAAAACACCGCGGAAGTGGAAACTAGCAAAGCGCCGGTCGAAAACACCGCCGAAGTGGAAACCAGCAAAGCGCCGGTTGAAAACACCGCCGAAGTGGAGACCAGCAAAGCGCCGGTGGAAAATACCGCCGAAGTAGAAACCAGCAAAGCCCCGGTGGAAAATACCGCGGAAGTGGAGACCTCAAAAGCGCCGGTTGAAAACACCGCGGAAGTGGAAACGAGCAAAGCACCGGTGGAGAATACCGCGGAAGTGGAAACCAGCAAAGCGCCGGTGGAAAATACCGCGGAAGTGGAAACGAGCAAAGCCCCAGTTGAAAATACGGCCGAGGTGGAAACCAGCAAAGCGCCGGTGGAAAACACCGCCGAAGTTGAAACCTCCAAAGCCCCGGTTGAAAATACCGCGGAAGTAGAAACCTCGAAAGCACCGGTGGAAAACACCGCCGAAGTGGAAACCTCAAAAGCCCCGGTGGAAAACACCGCGGAAGTTGAAACCTCTAAAGCGCCGGTGGAAAATACGGCGGAAGTGGAAACCAGCAAAGCCCTGGTCCAGAACCGCACCGCGCTGCGCGCGGCAACCCATGAACATAGCGCGCAGTGGCTGAATAACTACAAAAAAGGCTATGGCTATGGCCCGTATCCGCTGGGCATTAATGGCGGCATGCATTATGGTGTCGACTTTTTCATGAACATCGGCACCCCGGTTAAAGCGATTTCGAGCGGTAAAATCGTGGAAGCCGGCTGGAGCAACTACGGCGGCGGCAACCAGATTGGTCTGATTGAAAATGATGGCGTGCATCGCCAGTGGTACATGCATCTGAGCAAATACAACGTCAAAGTGGGTGATTATGTGAAAGCAGGTCAGATTATTGGCTGGAGCGGCAGCACCGGCTACAGCACCGCACCGCACCTGCATTTCCAGCGTATGGTGAATAGCTTCAGCAATAGCACCGCGCAGGATCCGATGCCGTTTCTGAAATCAGCGGGCTATGGCAAAGCGGGCGGCACCGTGACCCCGACCCCGAATACCGGCTGGAAAACCAACAAATATGGCACCCTGTATAAAAGCGAAAGCGCGAGCTTTACCCCGAACACCGATATCATTACCCGCACCACCGGCCCGTTCCGCAGCATGCCGCAGTCAGGCGTGCTGAAAGCGGGCCAGACCATTCATTATGATGAAGTGATGAAACAGGATGGCCATGTGTGGGTGGGTTATACCGGCAACTCGGGCCAGCGCATCTACCTGCCGGTGCGCACCTGGAACAAAAGCACCAACACCCTGGGTGTACTGTGGGGTACCATTAAA

7x His Tag:

CATCACCATCACCATCATCAC

Stop Codon:

TAA

Terminator (BBa_B0015):

CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTG TCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

Part 5: DNA Read/Write/Edit

5.1 DNA Read

What to Read: I would sequence the biofilm microbiome found in the pockets of failing dental implants.

Why:

Current treatment for peri-implantitis is often “blind” mechanical cleaning. By sequencing the DNA of the infection site, we can identify exactly which pathogens are present (e.g., P. gingivalis vs. S. aureus) and detect if they carry Antibiotic Resistance Genes (AMR). This allows for precision dentistry—choosing the right treatment rather than guessing.

Technology:

I would use the Oxford Nanopore MinION. • Reason: It is portable and rapid. I could theoretically bring it into a dental clinic, swab an implant, and get sequencing data in real-time to guide surgery. • Process: Extract DNA from plaque $\rightarrow$ Load into MinION $\rightarrow$ Nanopore reads electrical signals of DNA strands $\rightarrow$ Output is the pathogen profile.

5.2 DNA Write

What to Write:

I want to synthesize the gene for Lysostaphin (as designed in Part 3).

Why:

Nature provided S. simulans with this weapon, but we need to mass-produce it to use it as a medicine. By writing (synthesizing) this DNA, we can create a pure, high-concentration anti-biofilm agent that dissolves the cell walls of MRSA, saving titanium implants that would otherwise need to be removed.

Technology:

I would use Twist Bioscience silicon-based synthesis. • Reason: It allows me to order the exact “Expression Cassette” I designed, ensuring the sequence is perfect for my E. coli factories.

5.3 DNA Edit

What to Edit:

I would use CRISPR to edit commensal oral bacteria (like Streptococcus salivarius) to naturally secrete Lysostaphin.

Why:

Instead of applying a gel, we could introduce a “guardian bacteria” into the patient’s mouth. This edited bacteria would live on the gums and constantly produce small amounts of Lysostaphin, preventing the dangerous S. aureus from ever forming a biofilm on the implant in the first place.

Week 3 HW: Lab Automation

Week 3 – Lab Automation

✨ Week 3 - Homework ✨

You can view my Automation Art design here: Opentrons Art Link

After creating this shell pattern using Opentrons Art, I duplicated the provided Colab notebook to develop a Python protocol. To program the Opentrons robot to physically recreate the artwork on a plate, I systematically entered the coordinate data from my design step-by-step into the script. Once the protocol was complete, it successfully generated the images shown below.

Digital Shell Design

✨ Post-Lab Questions ✨

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

Article Title: An Automation Workflow for High-Throughput Manufacturing and Analysis of Scaffold-Supported 3D Tissue Arrays

Authors: Ruonan Cao, Nancy T. Li, Simon Latour, Jose L. Cadavid, Cassidy M. Tan, Ari Forman, Hartland W. Jackson, Alison P. McGuigan

Year: 2023

DOI: 10.1002/adhm.202202422

Article 1

This paper tackles a real bottleneck in advanced 3D culture: patient-derived organoids and complex co-cultures are powerful, but hard to scale and hard to analyze at single-cell resolution when manufacturing and handling are manual. The authors focus on the SPOT platform (Scaffold-supported Platform for Organoid-based Tissues), which generates flat, thin, dimensionally controlled microtissues in 96- and 384-well plate formats compatible with longitudinal imaging—yet historically limited by manual fabrication.

What’s automated with Opentrons OT-2 (and what makes it novel):

• Automated 3D microtissue manufacturing (seeding): They use the Opentrons OT-2 to dispense a cell–gel mixture into 96/384-SPOT plates and optimize the process so automated manufacturing is comparable to manual consistency.

• Temperature control + custom hardware for reliability: Two temperature modules set to 4 °C keep the SPOT plate and cell-gel cold during seeding, and a custom aluminum plate improves support and heat conduction for the thin plate—showing how automation often needs small mechanical/thermal design choices to work well.
  ]

• Automation beyond seeding (screening + single-cell endpoints): The OT-2 also supports drug/reagent addition and culture maintenance, and it automates gel digestion to recover single cells for high-throughput flow cytometry.

• Multiplexed CyTOF enablement: A particularly strong “novel application” angle is that OT-2 is used to generate a barcode master plate and automate parts of the barcoding/washing/pooling workflow to reduce manual errors—enabling scalable CyTOF proteomic readouts.

• Proof-of-value biology: They generate 3D complex tissues with different tumor/stromal ratios and show the workflow can incorporate primary patient-derived organoids, supporting scalable, patient-relevant screening and analysis.

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

Across all three ideas, the Opentrons OT-2 is my core “execution engine” for repeatable, programmable liquid handling—reducing variability, scaling to multi-sample workflows, and producing clean run logs/plate maps. Where formats aren’t standard labware, I’d use custom 3D-printed holders.

Idea 1 — Automated Seeding of Patient-Specific Bone Scaffolds

Goal: Improve cell distribution and viability deep inside porous bone scaffolds by replacing static pipetting with automated, repeatable dynamic “drip-seeding.”

What I would automate on the OT-2

• A custom 3D-printed scaffold holder mounting multiple scaffolds on the OT-2 deck.
• A timed protocol dispensing cell suspension (e.g., MSCs) + osteogenic media cues across scaffolds in multi-pass patterns.
• Optional scheduled media refresh + standardized sampling for assays.

Example pseudocode (conceptual)

# Conceptual workflow: dynamic drip-seeding across multiple scaffolds
scaffolds = load_custom_holder(num_scaffolds=8)
cell_source = reservoir("MSC_suspension")
media_source = reservoir("osteogenic_media")

for round in range(N_seed_rounds):
    for scaf in scaffolds:
        drip_dispense(cell_source, scaf, volume=V_cell, pattern="multi-point")
    wait(minutes=settle_time)

for day in culture_days:
    for scaf in scaffolds:
        exchange_media(scaf, media_source, volume=V_media)
    log_run(day)

Idea 2 — Anti-Biofilm “Guardian Bacteria” (high-throughput screening)

Goal: Run a high-throughput anti-biofilm screen on titanium-relevant surfaces using a plate-based assay format.

What I would automate on the OT-2

• A 96-well screening layout (controls + variants + replicates).
• Automated mixing, dispensing, wash steps, and readout reagent handling.
• Standardized timing + plate map + run log for comparability.

Example pseudocode (conceptual)

# Conceptual workflow: plate-based biofilm screen automation
plate = load_labware("96_well_plate")
variants = load_conditions("variant_plate_map.csv")

for well, condition in variants.items():
    dispense_inoculum(plate[well], condition)

incubate(plate, hours=incubation_time)

for wash in range(n_washes):
    wash_plate(plate)

apply_readout_reagent(plate)   # e.g., stain
final_wash_and_dispense(plate)
export_plate_map_and_log()

Idea 3 — Bioprinted Tooth-on-a-Chip Biosensor (automated long-term culture + exposure)

Goal: Improve dental material testing realism using a chip that includes a dentin barrier + engineered reporter pulp cells for real-time toxicity/biocompatibility readouts.

What I would automate on the OT-2

• Daily/recurring media exchange across multiple chips.
• Controlled dosing/exposure scheduling for different materials.
• Optional sampling workflow into plates for downstream measurements.

Example pseudocode (conceptual)

# Conceptual workflow: chip maintenance + condition dosing
chips = load_custom_chip_holder(n=6)
media = reservoir("pulp_media")
test_conditions = load_conditions("cement_conditions.csv")

for day in range(total_days):
    for chip in chips:
        media_exchange(chip, media)

    if day in dosing_days:
        for chip in chips:
            condition = test_conditions[chip.id]
            dispense_condition(chip, condition)

    log_run(day)

✨ Final Project Ideas ✨

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Week 4 HW: Protein Design