Week 1 HW: Principles and Practices

1. 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.

Biosensors for Animal Health

I aim to develop molecular diagnostic biosensors for veterinary medice. Specifically, I am interested in creating biofluorescent biosensor kits capable of detecting animal pathogens in rural and remote areas. This application is particularly relevant in countries such as Peru, where many communities lacated far from urban centers depend on livestock for their livelihood but face limited access to laboratory diagnostic services. This often leads to delayed diagnoses and significant economic losses due to infectius diseases.

The biosensor would integrate a specific biological recognition element with a luciferase-based reporter system. When the target pathogen or its genetic material is present in the sample, it specifically interacts with the biological component of the biosensor. This interaction actives the luciferase enzyme, which catalizes a reaction that produces light. The chemical reaction underlying the proposed biosensor is based on a bioluminescence process in whitch an enzyme catalyzes the transformation of substrate, releasing energy in the form of light. To generate this luminescent signal, the luciferase enzyme is activated in the presence of a pathogen or biomolecule. This mechanism offers several advantages, including high sensitivity and rapid results compared to other molecular techniques. Furthermore, it enable non-invasive observation of biological processes and the detection of pathogens in animals.

Figure 1. AI-generated image created using Ideogram

2. Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals. Below is one example framework (developed in the context of synthetic genomics) you can choose to use or adapt, or you can develop your own. The example was developed to consider policy goals of ensuring safety and security, alongside other goals, like promoting constructive uses, but you could propose other goals for example, those relating to equity or autonomy.

Objective 1: Minimizing Harm Throught Safe Biosensor Design and Deployment This objective is essential to ensuring the principle of non-maleficience, meaning that the design and implementation of the biosensor must prioritize the prevention of harm to animals and the environment. Specific objectives - Establish biosafety guidelines for the use of pathogenic biological components and containment strategies. - Promote responsible use and ensure that biosensor is use for veterinary diagnostic purposes through user guidelines and target training,

Objective 2: Promoting fair and Inclusive Access Policies should promote the affordability and accessibility of the technology for small-scale livestock farmers.

3. 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.).
   1. Purpose: What is done now and what changes are you proposing?
   2. Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc)
   3. Assumptions: What could you have wrong (incorrect assumptions, uncertainties)?
   4. Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

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

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. 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.

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.

🌿 My Reflection

Completing this task allowed me to become more aware of the ethical considerations involved in implementing a technology within a specific context. I came to understand that such implementing a technology within a specific context. I come to understand that such implementation requires close collaboration between researchers and government institutions to establish safaty protocols that ensure animal health.In addition, effective public policies are necessary to promote appropriate and equitable use in order to fulfill the mission of applicability in rural settings.

References:

• Biosensor Technology:https://doi.org/10.3390/vetsci12010023

• Advanced biosensors for detection of pathogens related to livestock and poultry :https://doi.org/10.1186/s13567-017-0418-5

Use of IA: deep reasearch : https://chatgpt.com/s/dr_698ab1fc72a88191b55e54fda9f92527 ChatGPT (OpenAI) was used as a support tool to analyze and organize the following prompts:

• Identification of the most common diseases in domestic animals in Peru that require molecular diagnosis and present high clinical demand.

• Exploration of current global solutions for these diseases within the fields of synthetic biology, biotechnology, and biological engineering.

• Detailed explanation of the molecular and biological mechanisms underlying these technological solutions.

• Comparative evaluation of isothermal molecular diagnostics (RPA and LAMP) and biosensor-based diagnostic approaches.

• Identification of relevant scientific literature supporting isothermal amplification methods and biosensor technologies.

  All interpretations, critical analysis, and conclusions derived from these prompts are the author’s own.

Assignment (Final Project) – Due as part of your Final Project presentation (not Feb 10)

As part of your final project, design one or more strategies to ensure that your project, and what it enables, contributes to growing an ethical biological future.

Assignment (Lab Preparation) — DUE BY START OF FEB 10 LECTURE

Lab Training (failure to complete this will jeopardize your acceptance into the course)

• Complete Lab Specific Training in Person.
• Complete Safety Training in Atlas
  • Navigate to atlas.mit.edu and on the right-hand side, click “Learning Center”
  • Head to the Course Catalog and find the following two courses:
    • General Biosafety for Researchers (EHS00260w)
    • Managing Hazardous Waste (EHS00501w)

Assignment (Week 2 Lecture Prep) — DUE BY START OF FEB 10 LECTURE

In preparation for Week 2’s lecture on “DNA Read, Write, and Edit," please review these materials:

1. Lecture 2 slides as posted below.
2. The associated papers that are referenced in those slides.

In addition, answer these questions in each faculty member’s section:

Homework Questions from Professor Jacobson: [Lecture 2 slides]

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?

The error rate of natural polymerase is aproximatly 1 in 10^6 nucleotides. When this is compared to the human genome (3 billion base pairs) the discrepancy is significativa. It is important to emphasize the mechanisms used by cells, particularly enzymes such as exonuclease 3-5 and exonuclease 5-3

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

There are 20 amino acids, which can generate a large number of comabinations. Nevertheless, some factors limit functionality, such as extreme GC content homopolymers, and free energy.

Homework Questions from Dr. LeProust: [Lecture 2 slides]

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

   Currently, the phosphoramidite method is the most commonlty used approach for oligonucleotide synthesis. This technique was introduced by Caarathers in 1981. This chemestry process involves the sequential addition of nucleotides onto a polymer-supported chain.

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

The first limitation is yield. In some cases, the error rate can reach approximately 1 in 100. As a result, the yield decreases exponentially with each additional base. Another important limitation is error accumulation. Chemical synthesis has a higher error rate than synthesis performed by biological polymerases.

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

Las principales razones se deben a la complejidad de la secuencia de 2000 pb que las regiones poco visibles tienen contenido GC muy alto o bajo. Otro aspecto es el rendimiento ya que el metodo de fosforamidita no funcionaria con eficiencia.

Homework Question from 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.

1. [Using Google & Prof. Church’s slide #4]   What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

The 10 essential amino acids are Lysine(K),Leucine(L),Arginine (R), Histidine (H), Methionine(M), Isoleucine (I),Tryptophan (W), Threonine (T), Valine (V), Phenylalanine (F). The lysine contingency is Lysine Contingency functions as a genetic safaty mechanism. In my view, this idea presents interesting implications. First, desingning an organism that lacks an essential amino acid, as it would be considered biologically fragile from the outset.That is, it creates the illusion of a biological switch; howeve, even in the hypothetical case that it works,the organism would be too fragile under normal conditions, as it would depend entirely on dietary availability, leading to imbalance and massive metabolic constraints. From a synthetic biology perspective, it is important to incorporate additional safety features such as dependence on non-standard amino acids for engineered organisms.

References:

• USE OF IA : https://chat.deepseek.com/share/akupvqutdgu3uhjvif

• Amino acids: metabolism, functions, and nutrition:https://pubmed.ncbi.nlm.nih.gov/19301095/

3. [Given slides #2 & 4 (AA:NA and NA:NA codes)]   What code would you suggest for AA:AA interactions?
4. [(Advanced students)]   Given the one paragraph abstracts for these real 2026 grant programs sketch a response to one of them or devise one of your own:
   • https://arpa-h.gov/explore-funding/programs/boss
   • https://www.darpa.mil/research/programs/smart-rbc
   • https://www.darpa.mil/research/programs/go

Assignment (Your HTGAA Website) — DUE BY START OF FEB 10 LECTURE

1. Begin personalizing your HTGAA website in in https://edit.htgaa.org/, starting with your homepage — fill in the template with information about yourself, or remove what’s there and make it your own. Be creative!
2. As with all assignments in HTGAA, be sure to write up every part of this Homework on your HTGAA website in order to receive credit.

Week 2 HW: DNA Read, Write, & Edit

Part 0: Basics of Gel Electrophoresis

I reviewed the recorded class recitation.

Part 1: Benchling & In-silico Gel Art

See the Gel Art: Restriction Digests and Gel Electrophoresis protocol for details. Overview:

Make a free account at benchling.com Import the Lambda DNA. Simulate Restriction Enzyme Digestion with the following Enzymes: EcoRI HindIII BamHI KpnI EcoRV SacI SalI Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks. You might find Ronan’s website a helpful tool for quickly iterating on designs!

After experimenting with the simulator, I successfully generated three figures using different restriction enzymes.

HEART

CIRCLE

FLOWER

Part 3: DNA Design Challenge

3.1. Choose your protein.

In recitation, we discussed that you will pick a protein for your homework that you find interesting. Which protein have you chosen and why? Using one of the tools described in recitation (NCBI, UniProt, google), obtain the protein sequence for the protein you chose.

P0A7X3

I choose the Small Ribosomal Subunit Protein uS9 (P0A7X3) from Escherichia coli K-12 because it is a well-characterized and fundamental biological role in bacterial translation. Studying this protein is relevant for studies in ribosome engineering structural biology, and synthetic biology applications.

Ribosomal engineering is the targeted redesign of ribosomes to control how genetic information is translated into proteins. It has direct implications for biotechnology, pharmaceutical innovation and industrial bioproduction.

I used Uniprot:

sp|P0A7X3|RS9_ECOLI Small ribosomal subunit protein uS9 OS=Escherichia coli (strain K12) OX=83333 GN=rpsI PE=1 SV=2 MAENQYYGTGRRKSSAARVFIKPGNGKIVINQRSLEQYFGRETARMVVRQPLELVDMVEK LDLYITVKGGGISGQAGAIRHGITRALMEYDESLRSELRKAGFVTRDARQVERKKVGLRK ARRRPQFSKR

d’Aquino AE, Kim DS, Jewett MC. Engineered Ribosomes for Basic Science and Synthetic Biology. Annu Rev Chem Biomol Eng. 2018 Jun 7;9:311-340. doi: 10.1146/annurev-chembioeng-060817-084129. Epub 2018 Mar 28. PMID: 29589973.

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

The Central Dogma discussed in class and recitation describes the process in which DNA sequence becomes transcribed and translated into protein. The Central Dogma gives us the framework to work backwards from a given protein sequence and infer the DNA sequence that the protein is derived from. Using one of the tools discussed in class, NCBI or online tools (google “reverse translation tools”), determine the nucleotide sequence that corresponds to the protein sequence you chose above.

Central Dogma

The Central Dogma of molecular biology describes the flow of genetic information from DNA to ARNA and ultimately to protein. Given a known protein sequence, once can infer a plausible nucleotide sequence by considering the degeneracy of the genetic code.Because the genetic code is redundant, several diffirent codons can encode the same amino acid.

My sequence

reverse translation of sp|P0A7X3|RS9_ECOLI Small ribosomal subunit protein uS9 OS=Escherichia coli (strain K12) OX=83333 GN=rpsI PE=1 SV=2 to a 390 base sequence of most likely codons. atggcggaaaaccagtattatggcaccggccgccgcaaaagcagcgcggcgcgcgtgttt attaaaccgggcaacggcaaaattgtgattaaccagcgcagcctggaacagtattttggc cgcgaaaccgcgcgcatggtggtgcgccagccgctggaactggtggatatggtggaaaaa ctggatctgtatattaccgtgaaaggcggcggcattagcggccaggcgggcgcgattcgc catggcattacccgcgcgctgatggaatatgatgaaagcctgcgcagcgaactgcgcaaa gcgggctttgtgacccgcgatgcgcgccaggtggaacgcaaaaaagtgggcctgcgcaaa gcgcgccgccgcccgcagtttagcaaacgc

I used this website: https://www.bioinformatics.org/sms2/rev_trans.html , I generated a 390- base pair DNA sequence corresponding to the amino acid sequence.

3.3. Codon optimization.

Once a nucleotide sequence of your protein is determined, you need to codon optimize your sequence. You may, once again, utilize google for a “codon optimization tool”. In your own words, describe why you need to optimize codon usage. Which organism have you chosen to optimize the codon sequence for and why?

ATG GCC GAA AAC CAG TAT TAT GGC ACC GGA CGG AGG AAA AGC TCT GCC GCA CGC GTG TTC ATT AAA CCA GGC AAT GGG AAG ATT GTG ATC AAT CAG AGA TCC TTG GAA CAG TAT TTT GGG CGG GAG ACT GCT AGG ATG GTG GTC AGA CAG CCT CTG GAA CTG GTG GAC ATG GTT GAG AAG CTG GAT CTG TAT ATT ACC GTG AAG GGA GGG GGC ATC TCC GGG CAG GCC GGC GCA ATC CGG CAT GGA ATT ACT CGA GCC CTT ATG GAG TAC GAC GAG TCC CTC CGC AGC GAG CTG AGA AAG GCG GGC TTC GTG ACC AGA GAT GCT CGA CAG GTG GAG AGG AAA AAG GTG GGA CTT CGC AAA GCT AGA AGA AGG CCA CAG TTT AGT AAA CGC

The following restriction enzyme sites have been found in the selected reading frame: BclI (TGATCA) MluI (ACGCGT) MlyI (GAGTC) NaeI (GCCGGC) XhoI (CTCGAG)

I used this website: https://www.idtdna.com/CodonOpt

3.4. You have a sequence! Now what?

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

1. Describe how a single gene codes for multiple proteins at the transcriptional level.
2. Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!!! See example below

a) Cell-free systems to produce RS9

• Chemical synthesis The protein is small (80aa), so it could be chemically synthesized in a specialized laboratory. In chemical synthesis , Solid -Phase Peptide Synthesis (SPPS) is employed, in which the first amino acid is anchored to resin, subsequent amino acids are added one at a time to create sequential peptide bonds,and the synthesized protein is later detached and purified. This technique is particulary advantage because it grants full control over the peptide sequence and facilitates the integration of artificial molecular modifications

• Cell free transcription system Conversely, the cell-free transcription strategy requires the prior design of a plasmid harboring a strong promoter, an optimized RBS and rpsl gene, which is subsequently added to the cell-free system to anable mRNA production.

  Althought this system allows for fast protein production and rapid experimental turnaround,its implementation can be economically demanding.

Whittaker J. W. (2013). Cell-free protein synthesis: the state of the art. Biotechnology letters, 35(2), 143–152. https://doi.org/10.1007/s10529-012-1075-4

Part 4: Prepare a Twist DNA Synthesis Order

During the class, I learned how to design the plasmid to complete the exercise. After completing the tutorial, I understood the full process required to place a DNA synthesis order at Twist Bioscience.

As it was my first time making a vector, with the help of ChatGPT and DeepSeek I was able to create these pieces.

I used the same basic structure to represent my synthetic circuit with the Rs9 protein.

The most complicated part was fitting my sequence with the vector. but in the end I chose this vector: pTwist Amp High Copy - (2221bp) and I got to this part:

Part 5: DNA Read/Write/Edit

5.1 DNA Read

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

I want to sequence the DNA of South American camelids in Peru, such as alpacas, vicuñas, huanacos and llamas with the aim of identiying genes that confer resistance to extreme cold events (known as friajes). Knowledge of these sequences, it would be possible to identify high-value biactive molecules, such as nanobodies or inmune-related proteins, antimicrobial peptides, for development of therapeutic strateigies and vaccine design, especifically for diseases affecting the respiratory system.

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

Also answer the following questions: Is your method first-, second- or third-generation or other? How so? What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps. What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)? What is the output of your chosen sequencing technology?

To sequence the DNA of South American camelid and study genes involved in cold resistance, I would use a hybrid sequencing strategy is employed because cold-adaptation genes include regulatory regions and gene duplications and mammalian genomes are large and repetitive.

• ChatGPT was used with the following prompts to assist in data organization and text refinement varuety of sequencing technologies such as Oxford Nanopore, PacBio, and Ilumina.
• SPPS adicional: Peptide synthesis: a review of classical and emerging methods (Zhangping Cai et al., 2025).
• Cell-free adicional: Cell-Free Protein Synthesis: Pros and Cons of Prokaryotic and Eukaryotic Systems (Anne Zemella et al., 2015).

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! :)

I want to synthesize a modular synthetic cold-adaptation operon optimized for E.coli. The construct would include: The cold-shock gene cspA, an antifreeze protein (AFP) coding sequence and the trehalose biosynthesis genes otsA and otsB enhance osmoprotection, a strong double terminator These genes would be assembled under a cold-inducible promoter to create a synthetic operon capable of enhancing cellular survival and function at low temperatures. Cold environments impose multiple molecular stresses, including RNA secondary structure stabilization, reduced enzymatic kinetics, membrane rigity.

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

Also answer the following questions: What are the essential steps of your chosen sequencing methods? What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?

a) Phosphoramidite solid-phase DNA synthesis for oligonucleotides This standard method for producing short oligonucleotides (20-200 nucleotides). DNA is synthesized base by base on a solid support through repeatec cycles of deprotection,coupling, oxidation and washing. While this technique provides precise control over sequence composition and enables chemical modifications.

b) Gibson Assembly to assemble fragments Gibson Assembly is an efficient DNA assembly method that allowas multiple fragments ti joined simultaneously in a single-tube, isothermal reaction. It uses a combination of exonuclease, DNA polymerase, and DNA ligase to join fragments that share overlapping homologous regios.

c) Commercial gene synthesis services in Twist Bioscience These synthesis services, such as those offered by Twist Bioscience provide end-to-end solutios for gene construcction, and sequencing based validation. Also provide sequence optimization options, such as codon optimization and restriction site removal, reducing experimental workload while increasing reliabity

Limitations

• Detection sensitivity threshold: There is an inherent limit to the sensitivity of error deteccion during queality control.

• False positives due:Errors introduced during PCR amplification, sequencing, or DNA assembly can be incorrectly interpreted as true mutations.

• Lenght-dependent error rates : Making long sequences more prone to insertions, deletions, and substitutions and necessitating assembly from shorter fragments.

• GC-rich sequence limitations: GC-rich regions reduce synthesis ans assembly efficency due to the formation of stable secondary structures, often requiring sequence redesing.

• Cost constraints: Gene synthesis costs increase with sequence length, GC content, and desing complexity, as longer and more complex genes require additional synthesis, assembly, and quality steps.

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?

I propose editing genes implicated in maize abiotic stress responses, with a focus on drought and elevanted temperature tolerance. These genetic modifications would be direted toward regulatory genes and molecular pathways responsible for efficient water use, osmotic regulation, and oxidative strees defense. It represents a potential response to climate change, which is increasing the frequency of extreme conditions that reduce agricultural yields in high-Andean regions.

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

Also answer the following questions: How does your technology of choice edit DNA? What are the essential steps? What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing? What are the limitations of your editing methods (if any) in terms of efficiency or precision?

I would employ CRISPR-CAS9 in combination with base editing or prime editing to achieve precise genome modifications without the insertion of exogenous DNA. The editing process include candidate gene identification, the desing of CRISPR guides, delivery of editing system into maize cells, and the regeneration of edited plants.

The main limitations of this approach are varible editing efficiency and the genetic complex genetic architecture underlying these traits.

REFERENCES:

• Molla, K. A., Sretenovic, S., Bansal, K. C., & Qi, Y. (2021). Precise plant genome editing using base editors and prime editors. Nature Plants, 7, 1166–1187. https://doi.org/10.1021/acsagscitech.2c00090

• The following prompts were employed in ChatGPT to organize and refine the information:

• “Explain Gibson Assembly step by step, highlighting the roles of exonuclease, DNA polymerase, and ligase, as well as common sources of assembly errors.”

• “Compare Gibson Assembly with restriction enzyme cloning for synthetic gene construction, including advantages, limitations, and typical use cases.

• Explain how CRISPR-Cas9 combined with base editing or prime editing enables precise genome modifications without introducing exogenous DNA.”

• Discuss the main technical limitations of CRISPR-based genome editing in plants, including delivery, efficiency, and polygenic traits.

• “Identify and explain categories of genes involved in abiotic stress tolerance in maize, including water-use efficiency, osmotic balance, and oxidative stress pathways.

Week 3 HW: Lab Automation

Assignment: Python Script for Opentrons Artwork — DUE BY YOUR LAB TIME! Your task this week is to Create a Python file to run on an Opentrons liquid handling robot.

Post-Lab Questions — DUE BY START OF FEB 24 LECTURE

To create the design, I employed several tests:

Part 1.

I initially performed some tests by modifying the color in the first example provided in Google Colab.

Part 2.

Then I tried to design a florwer based on the images generated on: https://opentrons-art.rcdonovan.com/.

3. From there , I generated these images with assistance from ChatGPT and DeepSeek, ultimately achieving this design. Below are the codes I used.

from opentrons import types import matplotlib.pyplot as plt import numpy as np from IPython.display import clear_output import time

metadata = { ‘protocolName’: ‘HTGAA Opentrons Lab - Letter K in Red’, ‘author’: ‘HTGAA’, ‘source’: ‘HTGAA 2022’, ‘apiLevel’: ‘2.20’ }

##############################################################################

Robot deck setup constants - don’t change these

##############################################################################

TIP_RACK_DECK_SLOT = 9 COLORS_DECK_SLOT = 6 AGAR_DECK_SLOT = 5 PIPETTE_STARTING_TIP_WELL = ‘A1’

well_colors = { ‘A1’ : ‘Red’, ‘B1’ : ‘Yellow’, ‘C1’ : ‘Green’, ‘D1’ : ‘Cyan’, ‘E1’ : ‘Blue’ }

Class to simulate the Opentrons environment

class OpentronsMock: def init(self, well_colors): self.well_colors = well_colors self.dots = [] # List to store drawn points self.current_position = (0, 0, 0) self.tip_attached = False self.labware = {} # Dictionary to store labware self.modules = {} # Dictionary to store modules self.instruments = {} # Dictionary to store instruments

def load_labware(self, name, slot, label=None):
    labware = MockLabware(name, slot, label, self)
    self.labware[slot] = labware
    return labware

def load_instrument(self, name, mount, tip_racks):
    instrument = MockPipette(name, mount, tip_racks, self)
    self.instruments[mount] = instrument
    return instrument

def load_module(self, name, slot):
    module = MockModule(name, slot, self)
    self.modules[slot] = module
    return module

def visualize(self):
    """Visualize the points drawn on the agar plate with black background"""
    # Configure style with black background
    plt.style.use('dark_background')
    
    fig, ax = plt.subplots(figsize=(10, 10), facecolor='black')
    ax.set_facecolor('black')
    
    # Draw the agar plate outline (90mm diameter circle)
    circle = plt.Circle((0, 0), 45, fill=False, color='white', linewidth=2, alpha=0.7)
    ax.add_patch(circle)
    
    # Draw a faint grid for reference
    for i in range(-40, 41, 10):
        ax.axhline(y=i, color='gray', linestyle=':', alpha=0.2)
        ax.axvline(x=i, color='gray', linestyle=':', alpha=0.2)
    
    # Draw the plate center
    ax.plot(0, 0, 'w+', markersize=10, linewidth=2, label='Center', alpha=0.7)
    
    # Draw the points
    if self.dots:
        x_coords = [dot[0] for dot in self.dots]
        y_coords = [dot[1] for dot in self.dots]
        colors = [dot[2] for dot in self.dots]
        
        # Color mapping (adjusted for black background)
        color_map = {'Red': 'red', 'Yellow': 'yellow', 'Green': 'lime', 
                    'Cyan': 'cyan', 'Blue': 'dodgerblue'}
        
        for x, y, color in zip(x_coords, y_coords, colors):
            ax.plot(x, y, 'o', color=color_map.get(color, 'white'), 
                   markersize=20, markeredgecolor='white', markeredgewidth=1.5)
    
    # Connect the points to show the K shape
    if len(self.dots) >= 13:  # If we have all points
        # Sort points to draw the K lines
        # Vertical points (sorted by y)
        vertical = sorted([(x, y) for x, y, _ in self.dots if x == 0], key=lambda p: p[1])
        if len(vertical) >= 7:
            v_x, v_y = zip(*vertical)
            ax.plot(v_x, v_y, 'red', alpha=0.5, linewidth=3, linestyle='-')
        
        # Lower diagonal points
        diagonal_lower = [(x, y) for x, y, _ in self.dots if x > 0 and y < 0]
        if diagonal_lower:
            d_x, d_y = zip(*sorted(diagonal_lower, key=lambda p: p[0]))
            ax.plot(d_x, d_y, 'red', alpha=0.5, linewidth=3, linestyle='-')
        
        # Upper diagonal points
        diagonal_upper = [(x, y) for x, y, _ in self.dots if x > 0 and y > 0]
        if diagonal_upper:
            d_x, d_y = zip(*sorted(diagonal_upper, key=lambda p: p[0]))
            ax.plot(d_x, d_y, 'red', alpha=0.5, linewidth=3, linestyle='-')
    
    # Configure the plot
    ax.set_xlim(-50, 50)
    ax.set_ylim(-50, 50)
    ax.set_aspect('equal')
    
    # Customize axes for black background
    ax.spines['bottom'].set_color('white')
    ax.spines['top'].set_color('white')
    ax.spines['left'].set_color('white')
    ax.spines['right'].set_color('white')
    ax.tick_params(colors='white')
    ax.xaxis.label.set_color('white')
    ax.yaxis.label.set_color('white')
    ax.title.set_color('white')
    
    ax.set_title('Agar Plate Visualization - Letter K in Red', 
                fontsize=16, fontweight='bold', color='white')
    ax.set_xlabel('X (mm)', fontsize=12, color='white')
    ax.set_ylabel('Y (mm)', fontsize=12, color='white')
    
    # Add legend with light colors
    from matplotlib.patches import Patch
    legend_elements = [
        Patch(facecolor='red', edgecolor='white', label='Red drops'),
        plt.Line2D([0], [0], color='red', alpha=0.5, linewidth=3, label='K connections'),
        plt.Line2D([0], [0], marker='+', color='white', linestyle='None', 
                  markersize=10, markeredgewidth=2, label='Center')
    ]
    legend = ax.legend(handles=legend_elements, loc='upper right', facecolor='black', 
                      edgecolor='white', labelcolor='white')
    for text in legend.get_texts():
        text.set_color('white')
    
    plt.tight_layout()
    plt.show()
    
    # Print information
    print(f"\n{'='*50}")
    print("📊 EXPERIMENT SUMMARY")
    print(f"{'='*50}")
    print(f"🎨 Color used: Red")
    print(f"🔴 Total drops deposited: {len(self.dots)}")
    print(f"📏 Shape: Letter K")
    print(f"📐 Drop spacing: 4 mm")
    print(f"📏 Dimensions: 24 mm high x 12 mm wide")
    
    # Show point coordinates
    print(f"\n📍 POINT COORDINATES (x, y) in mm:")
    print(f"{'-'*40}")
    for i, (x, y, color) in enumerate(self.dots, 1):
        print(f"   Point {i:2d}: ({x:+5.1f}, {y:+5.1f})")

class MockLabware: def init(self, name, slot, label, mock_env): self.name = name self.slot = slot self.label = label self.mock_env = mock_env self.wells_dict = {}

    # Create simulated wells
    if 'agar_plate' in name:
        # For agar plate, we only need A1
        self.wells_dict['A1'] = MockWell('A1', slot, mock_env)
    else:
        # For other plates, create some wells
        for well_name in ['A1', 'B1', 'C1', 'D1', 'E1']:
            self.wells_dict[well_name] = MockWell(well_name, slot, mock_env)

def wells(self):
    return list(self.wells_dict.values())

def __getitem__(self, well_name):
    """Make the object subscriptable"""
    if well_name in self.wells_dict:
        return self.wells_dict[well_name]
    else:
        # If it doesn't exist, create a new one
        self.wells_dict[well_name] = MockWell(well_name, self.slot, self.mock_env)
        return self.wells_dict[well_name]

class MockWell: def init(self, name, slot, mock_env): self.name = name self.slot = slot self.mock_env = mock_env

def top(self):
    return MockLocation(0, 0, 0, self.mock_env)

class MockLocation: def init(self, x, y, z, mock_env): self.point = types.Point(x, y, z) self.mock_env = mock_env

def move(self, point):
    new_x = self.point.x + point.x
    new_y = self.point.y + point.y
    new_z = self.point.z + point.z
    return MockLocation(new_x, new_y, new_z, self.mock_env)

class MockModule: def init(self, name, slot, mock_env): self.name = name self.slot = slot self.mock_env = mock_env

def load_labware(self, name, label):
    labware = MockLabware(name, self.slot, label, self.mock_env)
    return labware

class MockPipette: def init(self, name, mount, tip_racks, mock_env): self.name = name self.mount = mount self.tip_racks = tip_racks self.mock_env = mock_env self.current_volume = 0

def pick_up_tip(self):
    self.mock_env.tip_attached = True
    print("🔄 Tip picked up")
    
def drop_tip(self):
    self.mock_env.tip_attached = False
    print("🔄 Tip discarded")
    
def aspirate(self, volume, location):
    self.current_volume = volume
    if hasattr(location, 'name'):
        print(f"💧 Aspirating {volume}uL from {location.name}")
    else:
        print(f"💧 Aspirating {volume}uL")
    
def dispense(self, volume, location):
    self.current_volume -= volume
    # Register the point for visualization
    if hasattr(location, 'point'):
        self.mock_env.dots.append((location.point.x, location.point.y, 'Red'))
        print(f"💧 Dispensing {volume}uL at ({location.point.x:+5.1f}, {location.point.y:+5.1f})")
    
def move_to(self, location):
    if hasattr(location, 'point'):
        self.mock_env.current_position = (location.point.x, location.point.y, location.point.z)

def run(protocol): ############################################################################## ### Load labware, modules and pipettes ##############################################################################

# Tips
tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')

# Pipettes
pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])

# Modules
temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)

# Temperature Module Plate
temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul',
                                                    'Cold Plate')
# Choose where to take the colors from
color_plate = temperature_plate

# Agar Plate
agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')

# Get the top-center of the plate
center_location = agar_plate['A1'].top()

##############################################################################
###   Patterning
##############################################################################

###
### Helper functions for this lab
###

def location_of_color(color_string):
    for well, color in well_colors.items():
        if color.lower() == color_string.lower():
            return color_plate[well].top()
    raise ValueError(f"No well found with color {color_string}")

def dispense_and_detach(pipette, volume, location):
    """
    Move laterally 5mm above the plate; then drop down to the plate,
    dispense, move back up 5mm to detach drop.
    """
    assert(isinstance(volume, (int, float)))
    if hasattr(location, 'point'):
        above_location = location.move(types.Point(z=location.point.z + 5))
        pipette.move_to(above_location)
        pipette.dispense(volume, location)
        pipette.move_to(above_location)

###
### YOUR CODE HERE to create your design
###

###
### Code to create the letter K in red
###

print("\n" + "="*50)
print("🚀 STARTING PROTOCOL: Letter K in Red")
print("="*50 + "\n")

# Aspirate red color
pipette_20ul.pick_up_tip()

# Calculate required volume (13 points x 1uL = 13uL)
pipette_20ul.aspirate(13, location_of_color('Red'))

# Define points to form the letter K
# Letter size (spacing between points in mm)
spacing = 4

# Points for the vertical line (7 points)
vertical_points = []
for i in range(-3, 4):
    vertical_points.append((0, i * spacing))

# Points for the upper diagonal (3 points)
diagonal_upper = []
for i in range(1, 4):
    diagonal_upper.append((i * spacing, i * spacing))

# Points for the lower diagonal (3 points)
diagonal_lower = []
for i in range(1, 4):
    diagonal_lower.append((i * spacing, -i * spacing))

print("🎨 Drawing letter K...")

# Draw the vertical line
print("\n📏 Vertical line (7 points):")
print("   " + "─" * 35)
for i, (x, y) in enumerate(vertical_points):
    adjusted_location = center_location.move(types.Point(x=x, y=y))
    dispense_and_detach(pipette_20ul, 1, adjusted_location)
    print(f"   Point {i+1:2d}: ({x:+5.1f}, {y:+5.1f})")

# Draw the lower diagonal
print("\n📐 Lower diagonal (3 points):")
print("   " + "─" * 35)
for i, (x, y) in enumerate(diagonal_lower):
    adjusted_location = center_location.move(types.Point(x=x, y=y))
    dispense_and_detach(pipette_20ul, 1, adjusted_location)
    print(f"   Point {i+1:2d}: ({x:+5.1f}, {y:+5.1f})")

# Draw the upper diagonal
print("\n📐 Upper diagonal (3 points):")
print("   " + "─" * 35)
for i, (x, y) in enumerate(diagonal_upper):
    adjusted_location = center_location.move(types.Point(x=x, y=y))
    dispense_and_detach(pipette_20ul, 1, adjusted_location)
    print(f"   Point {i+1:2d}: ({x:+5.1f}, {y:+5.1f})")

# Clean up
pipette_20ul.drop_tip()

print("\n" + "="*50)
print("✅ PROTOCOL COMPLETED!")
print("="*50)
print(f"🎯 Total drops: {len(vertical_points) + len(diagonal_lower) + len(diagonal_upper)}")
print(f"🎨 Color: Red")
print(f"📏 Shape: Letter 'K'")

Create the simulation environment

print("🦾 Initializing Opentrons simulation environment…") protocol = OpentronsMock(well_colors)

Execute the protocol

run(protocol)

Visualize the results

print("\n" + “="*50) print("📊 GENERATING VISUALIZATION WITH BLACK BACKGROUND”) print("="*50) protocol.visualize()

My script: https://colab.research.google.com/drive/1rqh-nW1sT1y1mbWqTG84T_RVmF2-BDl_#scrollTo=bpfBlyR0VVJq

Post-Lab Questions — DUE BY START OF FEB 24 LECTURE

One of the great parts about having an automated robot is being able to precisely mix, deposit, and run reactions without much intervention, and design and deploy experiments remotely.

For this week, we’d like for you to do the following:

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

The article “Automated High-Throughtput Flow Cytometry for High-Content”

Describe the development of a fully integrated automated designed to enhance antibody discovery thorugh high-throughput cytometric analysis.

In this study, the researchers integrated robotic liquid handling with automated flow cytometry. The platform automated sample preparation, dilution steps, incubation procedures, and data acquisition.

Automated Tasks

Plate Initialization

• Dispense 100 µL of cell suspension into each well of a 96-well plate.

• Ensure uniform cell density (e.g., 1 × 10⁵ cells per well).

  Serial Dilution of Antibodies

• Perform 1:3 or 1:5 serial dilutions across designated wells.

• Transfer precise microliter volumes (e.g., 20 µL) to generate standardized concentration gradients.

  Antibody Incubation Setup

• Add diluted antibody solutions to corresponding wells.

• Mix gently using programmed pipette mixing cycles.

• Incubate for a defined period (e.g., 30 minutes at room temperature).

Wash Steps

Aspirate supernatant without disturbing the cell pellet.

Add wash buffer (e.g., PBS + 1% BSA).

Repeat wash cycle two to three times.

Secondary Antibody Addition (if required)

Dispense fluorescently labeled secondary antibody.

Incubate under controlled timing conditions.

Final Preparation for Flow Cytometry

Resuspend cells in analysis buffer.

Transfer samples to cytometry-compatible plates or tubes.

Aplication example:

This application focuses on the high-throughput screening of single-domain antibodies (VHH nanobodies) derived from South American camelids, such as llamas and alpacas. These antibodies are smaller and structurally simpler than conventional IgG molecules, making them highly suitable for recombinant expression and screening workflows. Using Opentrons automation, the robot can perform serial dilutions of VHH candidates, dispense them into 96-well plates containing target cells expressing the antigen of interest, and execute standardized wash and incubation steps prior to flow cytometry analysis. Because nanobody screening requires precise concentration gradients to evaluate binding affinity, automated liquid handling significantly improves reproducibility and quantitative reliability. REFERENCES:

• The table was generated with the assistance of ChatGPT (OpenAI) to summarize the key advantages described in the referenced article.
• Wang Y, Yoshihara T, King S, Le T, Leroy P, Zhao X, Chan CK, Yan ZH, Menon S. Automated High-Throughput Flow Cytometry for High-Content Screening in Antibody Development. SLAS Discov. 2018 Aug;23(7):656-666. doi: 10.1177/2472555218776607. Epub 2018 Jun 13. PMID: 29898633.