Week 1 HW: Principles and Practices

Chapter 1: The Idea

“Are there any sterile tips left?”

Most of us have asked this question at some point! It is heard dozens of times a day in labs, and followed by the sound of a plastic box opening. The issue: life sciences rely heavily on single-use materials. To maintain experimental sterility, laboratories operate within a consumption model that generates large amounts of waste (tubes,tips, etc.). For instance, Howes (2019) reported that a postgraduate student in molecular biology can produce approximately 230 g of plastic waste per day from consumables such as pipette tips and tubes, which corresponds to nearly 60 kg of plastic waste per laboratory each year.

To address this, I propose exploring the design of laboratory-ware through the convergence of DNA origami and biomaterial engineering. Instead of inert plastics, we can use DNA origami as a programmable scaffold to guide the fabrication of materials from the bottom up. By functionalizing these surfaces with reconfigurable DNA nanostructures (Luu et al., 2024). We can engineer an ultra-hydrophobic, anti-adherent lattice. Much like nanobots regrouping to form a protective suit, these DNA lattices can undergo conformational changes to actively repel proteins and nucleic acids. This molecular reset ensures absolute cleanliness, allowing us to replace disposable plastics with intelligent, self-decontaminating, and truly reusable bio-architectures.

Chapter 2: Policy Goals

Policy Goal 1: Safety & Biosecurity

Ensure that the DNA origami scaffolds used to replace plastic do not pose a biological risk themselves, preventing biological pollution or unintended interactions with the experiments they hold.

Sub-goal 1:

Develop strict standards for the chemical stability of the DNA lattice. Under extreme conditions, DNA nanostructures must not shed sequences into experimental samples, as this could lead to false-positive results or genetic contamination.

Sub-goal 2:

Establish a molecular kill-switch policy. All bio-architected labware must include a standardized chemical- or UV-based deactivation method to ensure that, at the end of its lifecycle, the programmable DNA is rendered inert and cannot be taken up by environmental microbes.

Policy Goal 2: Non-Maleficence & Data Integrity

Prevent harm to the scientific process. The primary form of harm is the compromise of experimental data, which can result in retracted publications or failed clinical trials.

Sub-goal 1:

Define a gold standard for cleanliness. Policy must require that self-decontaminating surfaces undergo rigorous validation—such as fluorescence-based detection assays—to demonstrate that the molecular reset is fully effective against residual proteins or PCR carry-over prior to reuse.

Sub-goal 2:

Collaborate with international standard-setting bodies (e.g., ISO) to establish new categories for intelligent bio-materials. This ensures that the design of a DNA-origami pipette tip adheres to global performance and safety standards, meeting the same criteria across laboratories of different biosafety levels and preventing regional safety gaps.

Policy Goal 3: Equity & Technological Sovereignty

Ensure that the transition to reusable intelligent labware does not introduce new economic barriers for laboratories in developing countries or smaller institutions.

Sub-goal 1:

Promote policies requiring that DNA sequences—the functional code of these labware scaffolds—be maintained in open-access repositories. This prevents market monopolization through restrictive patents and enables local manufacturing, including in countries such as Ecuador.

Sub-goal 2:

Encourage institutional policies that prioritize long-term sustainability over short-term cost savings. Governments should offer subsidies or tax incentives to support the transition from low-cost disposables to higher-cost but durable DNA-architected tools, ensuring that high initial investment costs do not exclude researchers with limited funding.

Chapter 3: Actions

Action 1: Technical Strategy and Validation Standard

Implementation of a Molecular Reset Validation Protocol integrated through fluorescence biosensors within the DNA origami structure itself.

Purpose:
To replace the sterility guarantee provided by single-use models with active, in situ verification. The DNA structure undergoes a conformational change to eject contaminants and, through an optical sensor, confirms to the researcher that the surface is 100% free of residues before the next experiment.

Actors:
Academic researchers (developers), biotechnology companies (manufacturers of scanning hardware), and laboratory accreditation bodies.

Expected Result:
Elimination of preemptive material waste. A direct reduction in plastic footprint is achieved by allowing a single pipette tip or test tube to be validated and reused with the same safety as a brand-new item.

Action 2: Regulatory and Safety Requirement

Creation of a Digital Life Cycle Passport based on unique identification sequences for each batch of bio-material.

Purpose:
To monitor wear and molecular fatigue of the DNA scaffold. The system records each reset cycle and automatically blocks further use once structural integrity limits are reached, ensuring the material does not fail during critical experiments.

Actors:
Government regulators (e.g., biosafety agencies), inventory management software developers, and laboratory biosafety officers.

Expected Result:
Full traceability of biotechnological inventory. This prevents incidents caused by material degradation and enables responsible waste management, in which exhausted DNA is deactivated and recycled under controlled conditions.

Action 3: Economic and Sustainability Incentive

Implementation of Green Lab Credits programs and government subsidies linked to reductions in plastic biomass.

Purpose:
To financially incentivize the transition toward reusable laboratory infrastructure. Institutions adopting DNA origami technology receive reduced waste management fees and preferential access to research funding by meeting circular economy criteria.

Actors:
Ministries of Science and Technology (e.g., SENESCYT), international funding bodies (e.g., NIH, EU programs), and financial departments.

Expected Result:
Democratization of the technology. By subsidizing initial investments, laboratories with limited budgets can transition away from linear plastic consumption models, making sustainability the most economically viable option.

Chapter 4: Rubrics

Chapter 5: Local Strategy

To address the governance of DNA-origami labware in Ecuador, I prioritize a combination of the Technical Strategy (Action 1) and the Economic Incentive (Action 3). While the Regulatory Requirement (Action 2) offers superior biosecurity oversight, its high bureaucratic burden and low feasibility risk stifling innovation within the national research ecosystem. By focusing on Actions 1 and 3, we address the two most critical barriers for Ecuadorian laboratories: scientific trust and financial accessibility. Action 1 serves as the heart of the project, providing the fluorescence-based validation necessary to prove that the molecular reset is effective, thereby ensuring lab safety and environmental protection. Meanwhile, Action 3 acts as the engine, utilizing Green Lab Credits or tax incentives to lower the high entry costs of imported DNA synthesis reagents. This ensures that the transition to sustainable labware is not a luxury reserved for wealthy institutions, but a viable path for public universities and research centers across the country.

But by de-prioritizing the strict Digital Passport of Action 2, we intentionally sacrifice some traceability to maximize feasibility and user adoption, assuming that Ecuadorian researchers respond better to incentives than to heavy policing from centralized agencies. However, a significant uncertainty remains regarding the material’s scale and durability under local conditions.

References

Howes, L. (2019). Can laboratories move away from single-use plastic? ACS Central Science, 5(12), 1904–1905. https://pubs.acs.org/doi/10.1021/acscentsci.9b01249

Luu, M. T., Shi, X., & Weizmann, Y. (2024). Reconfigurable nanomaterials folded from multicomponent chains of DNA origami voxels. Science Robotics, 9(92), eadp2309. https://doi.org/10.1126/scirobotics.adp2309

Week 2 HW: DNA Read, Write and Edit

Part 1: Benchling & In-silico Gel Art

It was definitely more difficult than I thought!

First Attempt: Letter A

Reference Image	In-silico Gel Result

Second Attempt: A Face

Reference Image	In-silico Gel Result

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

Protein: TP53 - tumor protein p53

The p53 protein, often referred to as “the guardian of the genome,” is a critical transcription factor that maintains cellular integrity by regulating the cell cycle and genomic stability. In response to cellular stress—such as DNA damage, hypoxia, or oncogene activation—p53 initiates a cascade of signals that can halt the cell cycle in the G1 phase, allowing time for DNA repair mechanisms to fix errors. If the damage proves too extensive to be repaired, p53 triggers apoptosis (programmed cell death), effectively eliminating potentially pre-cancerous cells and preventing the propagation of harmful mutations.From a clinical and molecular perspective, the TP53 gene is the most frequently mutated gene in human cancers, appearing in over 50% of all cases. When mutations occur, particularly in the DNA-binding domain, the protein loses its ability to function as a tumor suppressor, allowing cells with genomic instability to proliferate unchecked. This loss of control is a fundamental step in carcinogenesis and a primary focus for modern research into gene therapies and precision medicine aimed at restoring p53 activity to combat tumor growth.

MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD

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 backward 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.

reverse translation TP53 - tumor protein p53

atggaagaaccgcagagcgatccgagcgtggaaccgccgctgagccaggaaacctttagc gatctgtggaaactgctgccggaaaacaacgtgctgagcccgctgccgagccaggcgatg gatgatctgatgctgagcccggatgatattgaacagtggtttaccgaagatccgggcccg gatgaagcgccgcgcatgccggaagcggcgccgccggtggcgccggcgccggcggcgccg accccggcggcgccggcgccggcgccgagctggccgctgagcagcagcgtgccgagccag aaaacctatcagggcagctatggctttcgcctgggctttctgcatagcggcaccgcgaaa agcgtgacctgcacctatagcccggcgctgaacaaaatgttttgccagctggcgaaaacc tgcccggtgcagctgtgggtggatagcaccccgccgccgggcacccgcgtgcgcgcgatg gcgatttataaacagagccagcatatgaccgaagtggtgcgccgctgcccgcatcatgaa cgctgcagcgatagcgatggcctggcgccgccgcagcatctgattcgcgtggaaggcaac ctgcgcgtggaatatctggatgatcgcaacacctttcgccatagcgtggtggtgccgtat gaaccgccggaagtgggcagcgattgcaccaccattcattataactatatgtgcaacagc agctgcatgggcggcatgaaccgccgcccgattctgaccattattaccctggaagatagc agcggcaacctgctgggccgcaacagctttgaagtgcgcgtgtgcgcgtgcccgggccgc gatcgccgcaccgaagaagaaaacctgcgcaaaaaaggcgaaccgcatcatgaactgccg ccgggcagcaccaaacgcgcgctgccgaacaacaccagcagcagcccgcagccgaaaaaa aaaccgctggatggcgaatattttaccctgcagattcgcggccgcgaacgctttgaaatg tttcgcgaactgaacgaagcgctggaactgaaagatgcgcaggcgggcaaagaaccgggc ggcagccgcgcgcatagcagccatctgaaaagcaaaaaaggccagagcaccagccgccat aaaaaactgatgtttaaaaccgaaggcccggatagcgat

3.3. Codon optimization.

Once the 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”.

• Tool: Codon Optimization IDT https://www.idtdna.com/CodonOpt

In your own words, describe why do you need to optimize codon usage. Which organism have you chose to optimize the codon sequence for and why?

Codon optimization is essential because the genetic code is degenerate, meaning multiple codons can encode the same amino acid. However, different organisms—and even different tissues—display a “codon bias,” where certain tRNAs are more abundant than others. By strategically selecting the most efficient codons for a specific host, we can increase translational speed, ensure proper protein folding, and significantly boost the overall yield of the target protein. Without this optimization, the translation process might stall due to the scarcity of rare tRNAs, leading to truncated or misfolded proteins. For this project, I have chosen to optimize the codon sequence for human cells (Homo sapiens). My primary reason for this choice is that the target protein is p53, the “guardian of the genome.” Given its critical role in human cancer biology and its complex post-translational modifications, using a human expression system is vital to ensure that the protein retains its native conformation and biological functionality. Optimizing for the human host allows for a more accurate study of how p53 interacts with other endogenous tumor suppressors and provides a more clinically relevant model for developing potential gene therapies or molecular interventions.

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.

To produce the p53 protein from an optimized DNA sequence, two primary technological approaches can be utilized: cell-dependent systems and cell-free systems. In a cell-dependent approach, such as using mammalian cell lines like HEK293T or CHO, the optimized DNA is inserted into a plasmid vector and introduced into the cells via transfection or viral transduction. This method is ideal for p53 because these living factories possess the complex machinery required for essential post-translational modifications, ensuring the protein folds correctly into its functional tetrameric state. Alternatively, Cell-Free Protein Synthesis (CFPS) utilizes a biological soup or lysate (often derived from rabbit reticulocytes) containing ribosomes and tRNAs without the constraints of a living cell. This is particularly advantageous for p53 production because high concentrations of the protein can be toxic to living hosts, whereas a cell-free system allows for rapid, direct synthesis from a DNA template in a matter of hours. The conversion of this DNA sequence into a functional protein follows the fundamental biological pathway of transcription and translation. During transcription, the enzyme RNA polymerase binds to a promoter region on the DNA template and “reads” the optimized sequence to synthesize a complementary strand of messenger RNA (mRNA), which serves as a portable blueprint of the genetic information. This mRNA then enters the translation phase, where it is processed by the ribosome. The ribosome reads the mRNA in sets of three nucleotides, or codons, while transfer RNA (tRNA) molecules deliver the corresponding amino acids. As the ribosome moves along the mRNA strand, it catalyzes the formation of peptide bonds between these amino acids, building a polypeptide chain that eventually folds into the specific three-dimensional structure of the p53 protein, ready to fulfill its role in genomic maintenance.

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.

In nature, a single gene can diversify its output at the transcriptional level primarily through alternative splicing, a process where different combinations of exons from the same pre-mRNA molecule are joined together. While the initial transcription process produces a primary RNA transcript containing both coding (exons) and non-coding (introns) regions, the spliceosome selectively removes introns and stays flexible on which exons to retain. By including or skipping specific exons, the cell can generate multiple distinct mRNA variants from one DNA sequence, each of which is subsequently translated into a protein with different functional domains or properties.

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

Part 4: Twist DNA Synthesis Order

Linear Map of my preview TP53 sequence expression cassette

My final plasmid

Part 5: DNA Read/Write/Edit

5.1 DNA Read

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

To analyze the genetic diversity of the Andean bear (Tremarctos ornatus), I would sequence the mitochondrial COI (Cytochrome c Oxidase I) gene, as it serves as a robust molecular barcode for identifying population variations in endangered species.

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

I would use Oxford Nanopore Sequencing (ONT), which is a third-generation technology because it performs real-time sequencing of single DNA molecules without the need for massive PCR amplification. The input consists of genomic DNA purified from non-invasive samples (hair). Preparation involves adapter ligation with motor proteins and optional barcoding for multiplexing. The essential steps involve the DNA strand passing through a protein nanopore embedded in a polymer membrane; the system performs base calling by measuring disruptions in the ionic current caused by the unique chemical composition of each nucleotide as it transits the pore. The output is a series of FAST5 or FASTQ files containing long-read sequences.

5.2 DNA Write

(i) What DNA would you want to synthesize (e.g., write) and why?

I would synthesize a lactate-responsive genetic circuit (utilizing the lldPR operon) to monitor metabolic stress in wildlife. The sequence would include a lactate-sensitive promoter, a ribosome binding site (RBS), and a Green Fluorescent Protein (GFP) reporter gene.

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

I would use semiconductor-based DNA synthesis, which leverages silicon-based technology to write thousands of genes in parallel with high precision. The essential steps include in silico design, oligonucleotide synthesis on silicon microchips, PCR-based fragment assembly, and cloning into vectors. The primary limitations of this method include the high cost for extremely long sequences and technical difficulties in synthesizing regions with high GC content or complex repetitive elements, though its scalability is far superior to traditional column-based methods.

5.3 DNA Edit

(i) What DNA would you want to edit and why?

I would choose to edit the TP53 gene in mammalian cell lines to restore its tumor-suppressor function, given its critical role in cancer biology.

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

I would use CRISPR-Cas9 technology due to its high specificity and versatility. This technology edits DNA using a Cas9 nuclease directed by a synthetic guide RNA (sgRNA) to create a double-strand break at a precise genomic location. Preparation requires designing an sgRNA complementary to the target site, and the input consists of the ribonucleoprotein (RNP) complex or plasmids encoding Cas9 and the guide. The essential steps include PAM motif recognition, guide-DNA hybridization, and enzymatic cleavage, followed by Homology-Directed Repair (HDR) if a repair template is provided. The main limitations involve off-target effects (unintended cuts) and the variable efficiency of HDR-mediated repair in post-mitotic cells.

Week 2 HW: Lecture Prep

Homework Questions from Professor Jacobson

What is the error rate of polymerase?
DNA polymerase copies DNA with very high accuracy. When proofreading is active, the error rate is approximately 1 in 10⁶ base pairs, meaning one mistake per million bases added. This is much more accurate than chemical DNA synthesis, which has an error rate of about 1 in 10². DNA polymerase is also fast, adding one base roughly every 10 ms.

How does this compare to the length of the human genome?
The human genome is approximately 3.2 billion base pairs long. At an error rate of 10⁻⁶, a single full replication would theoretically introduce around 3,200 errors across the genome.

How does biology deal with that discrepancy?
Biology uses multiple error-correction mechanisms. DNA polymerase corrects errors during synthesis through proofreading, and additional mistakes are fixed by mismatch repair systems (such as MutS, MutL, and MutH) that detect and replace incorrectly paired bases.

How many different ways are there to code for an average human protein?
Due to the redundancy of the genetic code, an average human protein—encoded by roughly 1,036 base pairs—can be specified by an extremely large number of different DNA sequences without changing the resulting amino acid sequence.

In practice, why don’t all of these different codes work?
Many DNA sequences fail in practice because they form secondary structures that block transcription or translation, have high GC content that makes them difficult to process, are cleaved by RNA-processing enzymes, or generate assembly errors that prevent proper protein expression.

Homework Questions from Dr. LeProust

What’s the most commonly used method for oligo synthesis currently?
The most widely used method is the phosphoramidite DNA synthesis cycle, which consists of four main steps: deprotection, base coupling, optional capping, and oxidation. While traditionally performed on solid-phase supports using acid-based deprotection, modern next-generation approaches apply the same chemistry on high-density DNA chips using technologies such as inkjet printing or photolithography.

Why is it difficult to make oligos longer than 200 nt via direct synthesis?
The main limitation is the cumulative loss of yield over many synthesis cycles. Even with high per-step efficiency, overall yield decreases exponentially as length increases. In addition, chemical synthesis has a relatively high error rate (approximately 1 in 10²), so as sequences grow longer, most molecules accumulate deletions or insertions, making the recovery of a correct full-length oligo highly inefficient beyond ~200 nt.

Why can’t you make a 2000 bp gene via direct oligo synthesis?
Direct chemical synthesis lacks the error-correction mechanisms present in biological systems. Unlike DNA polymerase, which achieves error rates of 1 in 10⁶ through proofreading, chemical synthesis accumulates errors rapidly. As a result, long genes must be built by assembling shorter oligos using methods such as PCR assembly, Gibson assembly, or PCA, with intermediate error-filtering steps to ensure sequence accuracy.

Homework Question from George Church

The ten essential amino acids for most animals are phenylalanine, valine, threonine, tryptophan, isoleucine, methionine, histidine, arginine, leucine, and lysine. These compounds are classified as essential because animals lack the metabolic pathways required for their endogenous synthesis and must therefore obtain them through diet (Wu, 2009). In humans and other vertebrates, the inability to synthesize lysine is a natural, preexisting biological trait rather than an engineered modification (Bender, 2014).

This biological reality undermines the premise of the Lysine Contingency presented in Jurassic Park. Dinosaurs, as vertebrates, would already have been incapable of producing lysine independently. As a result, the proposed safety mechanism was redundant and ineffective, particularly because lysine is abundant in natural food sources such as meat and legumes, enabling the animals to obtain it through normal foraging behavior (Church & Regis, 2012).

For effective biological containment, modern synthetic biology instead proposes the use of non-canonical amino acids that do not occur in nature. By engineering organisms to depend on these synthetic compounds, survival becomes strictly contingent on a controlled laboratory supply, providing a more robust and reliable containment strategy (Mandell et al., 2015).

Academic References

• Bender, D. A. (2014). Amino Acid Metabolism. Wiley-Blackwell.
• Church, G. M. (2012). Regenesis: How Synthetic Biology Will Reinvent Nature and Ourselves.
• Mandell, D. J., Lajoie, M. J., Mee, M. T., et al. (2015). Biocontainment of genetically modified organisms via episomal and genomic engineering. Nature, 518(7537), 55–60.
• Wu, G. (2009). Amino acids: metabolism, functions, and nutrition. Amino Acids, 37(1), 1–17.

Week 3 HW

Part 1: Code

from opentrons import types

import string

metadata = { ‘protocolName’: ‘{YOUR NAME} - Opentrons Art - HTGAA’, ‘author’: ‘HTGAA’, ‘source’: ‘HTGAA 2026’, ‘apiLevel’: ‘2.20’ }

Z_VALUE_AGAR = 2.0 POINT_SIZE = 1

egfp_points = [(-12.65,31.05), (-10.35,31.05), (-8.05,31.05), (-5.75,31.05), (-3.45,31.05), (-1.15,31.05), (1.15,31.05), (3.45,31.05), (5.75,31.05), (8.05,31.05), (10.35,31.05), (12.65,31.05), (14.95,31.05), (-24.15,28.75), (-17.25,28.75), (-14.95,28.75), (-12.65,28.75), (-10.35,28.75), (-8.05,28.75), (-5.75,28.75), (-3.45,28.75), (-1.15,28.75), (1.15,28.75), (3.45,28.75), (5.75,28.75), (8.05,28.75), (10.35,28.75), (12.65,28.75), (14.95,28.75), (17.25,28.75), (-21.85,26.45), (-19.55,26.45), (19.55,26.45), (21.85,26.45), (33.35,17.25), (-31.05,14.95), (31.05,14.95), (-33.35,12.65), (-3.45,12.65), (-1.15,12.65), (1.15,12.65), (3.45,12.65), (31.05,12.65), (-35.65,10.35), (-5.75,10.35), (-3.45,10.35), (-1.15,10.35), (1.15,10.35), (3.45,10.35), (5.75,10.35), (31.05,10.35), (33.35,10.35), (-35.65,8.05), (35.65,8.05), (-10.35,5.75), (10.35,5.75), (37.95,5.75), (-37.95,3.45), (37.95,3.45), (-37.95,1.15), (-12.65,1.15), (14.95,1.15), (37.95,1.15), (-37.95,-1.15), (-19.55,-1.15), (-17.25,-1.15), (-14.95,-1.15), (17.25,-1.15), (19.55,-1.15), (21.85,-1.15), (37.95,-1.15), (-37.95,-3.45), (37.95,-3.45), (-37.95,-5.75), (37.95,-5.75), (-37.95,-8.05), (37.95,-8.05), (-37.95,-10.35), (37.95,-10.35), (35.65,-12.65), (-35.65,-14.95), (35.65,-14.95), (-35.65,-17.25), (35.65,-17.25)] mlychee_tf_points = [(-26.45,28.75), (26.45,28.75), (-28.75,26.45), (-26.45,26.45), (-24.15,26.45), (24.15,26.45), (26.45,26.45), (28.75,26.45), (-31.05,24.15), (24.15,24.15), (26.45,24.15), (31.05,24.15), (-33.35,21.85), (-31.05,21.85), (26.45,21.85), (31.05,21.85), (33.35,21.85), (-33.35,19.55), (-31.05,19.55), (28.75,19.55), (31.05,19.55), (33.35,19.55), (-31.05,17.25), (-14.95,17.25), (-12.65,17.25), (-10.35,17.25), (10.35,17.25), (12.65,17.25), (14.95,17.25), (31.05,17.25), (-17.25,14.95), (-14.95,14.95), (-12.65,14.95), (-10.35,14.95), (10.35,14.95), (12.65,14.95), (14.95,14.95), (17.25,14.95), (26.45,14.95), (-19.55,12.65), (-14.95,12.65), (-12.65,12.65), (-10.35,12.65), (10.35,12.65), (12.65,12.65), (14.95,12.65), (19.55,12.65), (26.45,12.65), (-26.45,10.35), (-21.85,10.35), (-12.65,10.35), (-10.35,10.35), (10.35,10.35), (12.65,10.35), (19.55,10.35), (21.85,10.35), (26.45,10.35), (28.75,10.35), (-31.05,8.05), (-28.75,8.05), (-26.45,8.05), (-21.85,8.05), (-19.55,8.05), (-12.65,8.05), (10.35,8.05), (12.65,8.05), (19.55,8.05), (21.85,8.05), (26.45,8.05), (28.75,8.05), (31.05,8.05), (-33.35,5.75), (-31.05,5.75), (-28.75,5.75), (-26.45,5.75), (-21.85,5.75), (-14.95,5.75), (-12.65,5.75), (12.65,5.75), (14.95,5.75), (17.25,5.75), (21.85,5.75), (26.45,5.75), (28.75,5.75), (31.05,5.75), (-31.05,3.45), (-28.75,3.45), (-26.45,3.45), (-19.55,3.45), (-17.25,3.45), (-14.95,3.45), (14.95,3.45), (17.25,3.45), (19.55,3.45), (26.45,3.45), (28.75,3.45), (31.05,3.45), (33.35,3.45), (-33.35,1.15), (-31.05,1.15), (-28.75,1.15), (-26.45,1.15), (26.45,1.15), (28.75,1.15), (31.05,1.15), (33.35,1.15), (-33.35,-1.15), (-31.05,-1.15), (-28.75,-1.15), (-26.45,-1.15), (26.45,-1.15), (28.75,-1.15), (31.05,-1.15), (33.35,-1.15), (-35.65,-3.45), (-33.35,-3.45), (-31.05,-3.45), (-28.75,-3.45), (-26.45,-3.45), (-24.15,-3.45), (24.15,-3.45), (26.45,-3.45), (28.75,-3.45), (31.05,-3.45), (33.35,-3.45), (-33.35,-5.75), (-31.05,-5.75), (-28.75,-5.75), (-26.45,-5.75), (-24.15,-5.75), (-21.85,-5.75), (-19.55,-5.75), (19.55,-5.75), (21.85,-5.75), (24.15,-5.75), (26.45,-5.75), (28.75,-5.75), (31.05,-5.75), (33.35,-5.75), (-33.35,-8.05), (-31.05,-8.05), (-28.75,-8.05), (-26.45,-8.05), (-24.15,-8.05), (-21.85,-8.05), (-19.55,-8.05), (-5.75,-8.05), (-3.45,-8.05), (3.45,-8.05), (5.75,-8.05), (19.55,-8.05), (21.85,-8.05), (24.15,-8.05), (26.45,-8.05), (28.75,-8.05), (31.05,-8.05), (33.35,-8.05), (-33.35,-10.35), (-31.05,-10.35), (-28.75,-10.35), (-26.45,-10.35), (-24.15,-10.35), (-21.85,-10.35), (-19.55,-10.35), (-5.75,-10.35), (-3.45,-10.35), (3.45,-10.35), (5.75,-10.35), (19.55,-10.35), (21.85,-10.35), (24.15,-10.35), (26.45,-10.35), (28.75,-10.35), (31.05,-10.35), (33.35,-10.35), (-31.05,-12.65), (-28.75,-12.65), (-26.45,-12.65), (-24.15,-12.65), (-21.85,-12.65), (-19.55,-12.65), (19.55,-12.65), (21.85,-12.65), (24.15,-12.65), (26.45,-12.65), (28.75,-12.65), (31.05,-12.65), (33.35,-12.65), (-33.35,-14.95), (-31.05,-14.95), (-28.75,-14.95), (-26.45,-14.95), (-24.15,-14.95), (-21.85,-14.95), (-19.55,-14.95), (-17.25,-14.95), (19.55,-14.95), (21.85,-14.95), (24.15,-14.95), (26.45,-14.95), (28.75,-14.95), (31.05,-14.95), (-28.75,-17.25), (-26.45,-17.25), (-24.15,-17.25), (-21.85,-17.25), (-19.55,-17.25), (-17.25,-17.25), (17.25,-17.25), (19.55,-17.25), (21.85,-17.25), (24.15,-17.25), (26.45,-17.25), (28.75,-17.25), (31.05,-17.25), (-28.75,-19.55), (-26.45,-19.55), (-24.15,-19.55), (-21.85,-19.55), (-19.55,-19.55), (-17.25,-19.55), (-14.95,-19.55), (14.95,-19.55), (17.25,-19.55), (19.55,-19.55), (21.85,-19.55), (24.15,-19.55), (26.45,-19.55), (28.75,-19.55), (-26.45,-21.85), (-24.15,-21.85), (-21.85,-21.85), (-19.55,-21.85), (-17.25,-21.85), (-14.95,-21.85), (-12.65,-21.85), (12.65,-21.85), (14.95,-21.85), (17.25,-21.85), (19.55,-21.85), (21.85,-21.85), (24.15,-21.85), (26.45,-21.85), (28.75,-21.85), (31.05,-21.85), (33.35,-21.85), (-31.05,-24.15), (-28.75,-24.15), (-26.45,-24.15), (-24.15,-24.15), (-21.85,-24.15), (-19.55,-24.15), (-17.25,-24.15), (-14.95,-24.15), (-12.65,-24.15), (-10.35,-24.15), (12.65,-24.15), (14.95,-24.15), (17.25,-24.15), (19.55,-24.15), (21.85,-24.15), (24.15,-24.15), (26.45,-24.15), (28.75,-24.15), (31.05,-24.15), (-28.75,-26.45), (-26.45,-26.45), (-24.15,-26.45), (-21.85,-26.45), (-19.55,-26.45), (-17.25,-26.45), (-14.95,-26.45), (-12.65,-26.45), (-10.35,-26.45), (-8.05,-26.45), (-5.75,-26.45), (-3.45,-26.45), (-1.15,-26.45), (1.15,-26.45), (3.45,-26.45), (5.75,-26.45), (8.05,-26.45), (10.35,-26.45), (12.65,-26.45), (14.95,-26.45), (17.25,-26.45), (19.55,-26.45), (21.85,-26.45), (24.15,-26.45), (26.45,-26.45), (28.75,-26.45), (-26.45,-28.75), (-24.15,-28.75), (-21.85,-28.75), (-19.55,-28.75), (-17.25,-28.75), (-14.95,-28.75), (-12.65,-28.75), (-10.35,-28.75), (-8.05,-28.75), (-5.75,-28.75), (-3.45,-28.75), (-1.15,-28.75), (1.15,-28.75), (3.45,-28.75), (5.75,-28.75), (8.05,-28.75), (10.35,-28.75), (12.65,-28.75), (14.95,-28.75), (17.25,-28.75), (19.55,-28.75), (21.85,-28.75), (24.15,-28.75), (26.45,-28.75), (-24.15,-31.05), (-21.85,-31.05), (-19.55,-31.05), (-17.25,-31.05), (-14.95,-31.05), (-12.65,-31.05), (-10.35,-31.05), (-8.05,-31.05), (-5.75,-31.05), (-3.45,-31.05), (-1.15,-31.05), (1.15,-31.05), (3.45,-31.05), (5.75,-31.05), (8.05,-31.05), (10.35,-31.05), (12.65,-31.05), (14.95,-31.05), (17.25,-31.05), (19.55,-31.05), (21.85,-31.05), (24.15,-31.05), (-21.85,-33.35), (-19.55,-33.35), (-17.25,-33.35), (-14.95,-33.35), (-12.65,-33.35), (-10.35,-33.35), (-8.05,-33.35), (-5.75,-33.35), (-3.45,-33.35), (-1.15,-33.35), (1.15,-33.35), (3.45,-33.35), (5.75,-33.35), (8.05,-33.35), (10.35,-33.35), (12.65,-33.35), (14.95,-33.35), (17.25,-33.35), (19.55,-33.35), (21.85,-33.35), (-17.25,-35.65), (-14.95,-35.65), (-12.65,-35.65), (-10.35,-35.65), (-8.05,-35.65), (-5.75,-35.65), (-3.45,-35.65), (-1.15,-35.65), (1.15,-35.65), (3.45,-35.65), (5.75,-35.65), (8.05,-35.65), (10.35,-35.65), (12.65,-35.65), (14.95,-35.65), (17.25,-35.65), (-10.35,-37.95), (-8.05,-37.95), (-5.75,-37.95), (-3.45,-37.95), (-1.15,-37.95), (1.15,-37.95), (3.45,-37.95), (5.75,-37.95), (8.05,-37.95), (10.35,-37.95)] tagrfp_points = [(24.15,28.75), (-14.95,26.45), (-12.65,26.45), (-10.35,26.45), (-8.05,26.45), (-5.75,26.45), (-3.45,26.45), (-1.15,26.45), (1.15,26.45), (3.45,26.45), (5.75,26.45), (8.05,26.45), (10.35,26.45), (12.65,26.45), (14.95,26.45), (17.25,26.45), (-24.15,24.15), (-21.85,24.15), (-19.55,24.15), (-17.25,24.15), (-14.95,24.15), (-8.05,24.15), (-5.75,24.15), (-3.45,24.15), (3.45,24.15), (5.75,24.15), (8.05,24.15), (14.95,24.15), (17.25,24.15), (19.55,24.15), (21.85,24.15), (-24.15,21.85), (-19.55,21.85), (-14.95,21.85), (-8.05,21.85), (-5.75,21.85), (-3.45,21.85), (1.15,21.85), (3.45,21.85), (5.75,21.85), (8.05,21.85), (10.35,21.85), (14.95,21.85), (17.25,21.85), (19.55,21.85), (24.15,21.85), (-28.75,19.55), (-26.45,19.55), (-24.15,19.55), (-21.85,19.55), (-19.55,19.55), (19.55,19.55), (21.85,19.55), (24.15,19.55), (26.45,19.55), (-26.45,17.25), (-24.15,17.25), (26.45,17.25), (28.75,17.25), (-28.75,14.95), (-26.45,14.95), (-24.15,14.95), (-28.75,12.65), (-17.25,12.65), (17.25,12.65), (-31.05,10.35), (-28.75,10.35), (-19.55,10.35), (-33.35,8.05), (-17.25,5.75), (-35.65,3.45), (35.65,3.45), (-33.35,-17.25)] mruby2_points = [(-17.25,26.45), (-12.65,24.15), (-10.35,24.15), (10.35,24.15), (12.65,24.15), (-17.25,21.85), (-12.65,21.85), (-10.35,21.85), (12.65,21.85), (-17.25,19.55), (17.25,19.55), (-33.35,17.25), (-28.75,17.25), (-21.85,17.25), (-19.55,17.25), (19.55,17.25), (21.85,17.25), (-21.85,14.95), (21.85,14.95), (-31.05,12.65), (-33.35,10.35), (33.35,8.05), (-35.65,5.75), (35.65,5.75), (-12.65,3.45), (-21.85,1.15), (-19.55,1.15), (-17.25,1.15), (-14.95,1.15), (19.55,1.15), (21.85,1.15), (-21.85,-1.15), (-35.65,-8.05), (-35.65,-10.35), (35.65,-10.35), (-35.65,-12.65), (33.35,-17.25), (-33.35,-19.55), (33.35,-19.55)] mkate2_points = [(-1.15,24.15), (1.15,24.15), (-1.15,21.85), (-5.75,19.55), (-3.45,19.55), (-1.15,19.55), (1.15,19.55), (3.45,19.55), (5.75,19.55), (-5.75,17.25), (-3.45,17.25), (-1.15,17.25), (1.15,17.25), (3.45,17.25), (5.75,17.25), (-5.75,14.95), (-3.45,14.95), (-1.15,14.95), (1.15,14.95), (3.45,14.95), (5.75,14.95), (-5.75,12.65), (5.75,12.65), (-8.05,-3.45), (-5.75,-3.45), (-3.45,-3.45), (-1.15,-3.45), (1.15,-3.45), (3.45,-3.45), (5.75,-3.45), (8.05,-3.45), (-8.05,-5.75), (-5.75,-5.75), (-3.45,-5.75), (-1.15,-5.75), (1.15,-5.75), (3.45,-5.75), (5.75,-5.75), (8.05,-5.75), (-8.05,-8.05), (-1.15,-8.05), (1.15,-8.05), (8.05,-8.05), (-8.05,-10.35), (-1.15,-10.35), (1.15,-10.35), (8.05,-10.35), (-8.05,-12.65), (-5.75,-12.65), (-3.45,-12.65), (-1.15,-12.65), (1.15,-12.65), (3.45,-12.65), (5.75,-12.65), (8.05,-12.65), (-5.75,-14.95), (-3.45,-14.95), (-1.15,-14.95), (1.15,-14.95), (3.45,-14.95), (5.75,-14.95), (-1.15,-17.25), (1.15,-17.25), (-1.15,-19.55), (1.15,-19.55), (-8.05,-21.85), (-5.75,-21.85), (-3.45,-21.85), (-1.15,-21.85), (1.15,-21.85), (3.45,-21.85), (5.75,-21.85), (8.05,-21.85)] mkate2_tf_points = [(-21.85,21.85), (21.85,21.85), (24.15,17.25), (24.15,14.95), (28.75,14.95), (-26.45,12.65), (28.75,12.65), (-10.35,8.05), (-19.55,5.75), (19.55,5.75), (33.35,5.75), (-33.35,3.45), (-21.85,3.45), (21.85,3.45), (-35.65,1.15), (17.25,1.15), (35.65,1.15), (-35.65,-1.15), (35.65,-1.15), (35.65,-3.45), (-35.65,-5.75), (35.65,-5.75), (35.65,-8.05), (-33.35,-12.65), (33.35,-14.95), (-31.05,-17.25), (-31.05,-19.55), (31.05,-19.55), (-33.35,-21.85), (-31.05,-21.85), (-28.75,-21.85)] mko2_points = [(-14.95,19.55), (-12.65,19.55), (-10.35,19.55), (-8.05,19.55), (8.05,19.55), (10.35,19.55), (12.65,19.55), (14.95,19.55), (-17.25,17.25), (-8.05,17.25), (8.05,17.25), (17.25,17.25), (-19.55,14.95), (-8.05,14.95), (8.05,14.95), (19.55,14.95), (-24.15,12.65), (-21.85,12.65), (-8.05,12.65), (8.05,12.65), (21.85,12.65), (24.15,12.65), (-24.15,10.35), (-8.05,10.35), (8.05,10.35), (24.15,10.35), (-24.15,8.05), (-8.05,8.05), (-5.75,8.05), (-3.45,8.05), (-1.15,8.05), (1.15,8.05), (3.45,8.05), (5.75,8.05), (8.05,8.05), (24.15,8.05), (-24.15,5.75), (-8.05,5.75), (-5.75,5.75), (-3.45,5.75), (-1.15,5.75), (1.15,5.75), (3.45,5.75), (5.75,5.75), (8.05,5.75), (24.15,5.75), (-24.15,3.45), (-10.35,3.45), (-8.05,3.45), (-5.75,3.45), (-3.45,3.45), (-1.15,3.45), (1.15,3.45), (3.45,3.45), (5.75,3.45), (8.05,3.45), (10.35,3.45), (12.65,3.45), (24.15,3.45), (-24.15,1.15), (-10.35,1.15), (-8.05,1.15), (-5.75,1.15), (-3.45,1.15), (-1.15,1.15), (1.15,1.15), (3.45,1.15), (5.75,1.15), (8.05,1.15), (10.35,1.15), (12.65,1.15), (24.15,1.15), (-24.15,-1.15), (-12.65,-1.15), (-10.35,-1.15), (-8.05,-1.15), (-5.75,-1.15), (-3.45,-1.15), (-1.15,-1.15), (1.15,-1.15), (3.45,-1.15), (5.75,-1.15), (8.05,-1.15), (10.35,-1.15), (12.65,-1.15), (14.95,-1.15), (24.15,-1.15), (-21.85,-3.45), (-19.55,-3.45), (-17.25,-3.45), (-14.95,-3.45), (-12.65,-3.45), (-10.35,-3.45), (10.35,-3.45), (12.65,-3.45), (14.95,-3.45), (17.25,-3.45), (19.55,-3.45), (21.85,-3.45), (-17.25,-5.75), (-14.95,-5.75), (-12.65,-5.75), (-10.35,-5.75), (10.35,-5.75), (12.65,-5.75), (14.95,-5.75), (17.25,-5.75), (-17.25,-8.05), (-14.95,-8.05), (-12.65,-8.05), (-10.35,-8.05), (10.35,-8.05), (12.65,-8.05), (14.95,-8.05), (17.25,-8.05), (-17.25,-10.35), (-14.95,-10.35), (-12.65,-10.35), (-10.35,-10.35), (10.35,-10.35), (12.65,-10.35), (14.95,-10.35), (17.25,-10.35), (-17.25,-12.65), (-14.95,-12.65), (-12.65,-12.65), (-10.35,-12.65), (10.35,-12.65), (12.65,-12.65), (14.95,-12.65), (17.25,-12.65), (-14.95,-14.95), (-12.65,-14.95), (-10.35,-14.95), (-8.05,-14.95), (8.05,-14.95), (10.35,-14.95), (12.65,-14.95), (14.95,-14.95), (17.25,-14.95), (-14.95,-17.25), (-12.65,-17.25), (-10.35,-17.25), (-8.05,-17.25), (-5.75,-17.25), (-3.45,-17.25), (3.45,-17.25), (5.75,-17.25), (8.05,-17.25), (10.35,-17.25), (12.65,-17.25), (14.95,-17.25), (-12.65,-19.55), (-10.35,-19.55), (-8.05,-19.55), (-5.75,-19.55), (-3.45,-19.55), (3.45,-19.55), (5.75,-19.55), (8.05,-19.55), (10.35,-19.55), (12.65,-19.55), (-10.35,-21.85), (10.35,-21.85), (-8.05,-24.15), (-5.75,-24.15), (-3.45,-24.15), (-1.15,-24.15), (1.15,-24.15), (3.45,-24.15), (5.75,-24.15), (8.05,-24.15), (10.35,-24.15)]

point_name_pairing = [(“egfp”, egfp_points),(“mlychee_tf”, mlychee_tf_points),(“tagrfp”, tagrfp_points),(“mruby2”, mruby2_points),(“mkate2”, mkate2_points),(“mkate2_tf”, mkate2_tf_points),(“mko2”, mko2_points)]

Robot deck setup constants

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

Place the PCR tubes in this order

well_colors = { ‘A1’: ‘sfGFP’, ‘A2’: ‘mRFP1’, ‘A3’: ‘mKO2’, ‘A4’: ‘Venus’, ‘A5’: ‘mKate2_TF’, ‘A6’: ‘Azurite’, ‘A7’: ‘mCerulean3’, ‘A8’: ‘mClover3’, ‘A9’: ‘mJuniper’, ‘A10’: ‘mTurquoise2’, ‘A11’: ‘mBanana’, ‘A12’: ‘mPlum’, ‘B1’: ‘Electra2’, ‘B2’: ‘mWasabi’, ‘B3’: ‘mScarlet_I’, ‘B4’: ‘mPapaya’, ‘B5’: ’eqFP578’, ‘B6’: ’tdTomato’, ‘B7’: ‘DsRed’, ‘B8’: ‘mKate2’, ‘B9’: ‘EGFP’, ‘B10’: ‘mRuby2’, ‘B11’: ‘TagBFP’, ‘B12’: ‘mChartreuse_TF’, ‘C1’: ‘mLychee_TF’, ‘C2’: ‘mTagBFP2’, ‘C3’: ‘mEGFP’, ‘C4’: ‘mNeonGreen’, ‘C5’: ‘mAzamiGreen’, ‘C6’: ‘mWatermelon’, ‘C7’: ‘avGFP’, ‘C8’: ‘mCitrine’, ‘C9’: ‘mVenus’, ‘C10’: ‘mCherry’, ‘C11’: ‘mHoneydew’, ‘C12’: ‘TagRFP’, ‘D1’: ‘mTFP1’, ‘D2’: ‘Ultramarine’, ‘D3’: ‘ZsGreen1’, ‘D4’: ‘mMiCy’, ‘D5’: ‘mStayGold2’, ‘D6’: ‘PA_GFP’ }

volume_used = { ’egfp’: 0, ‘mlychee_tf’: 0, ’tagrfp’: 0, ‘mruby2’: 0, ‘mkate2’: 0, ‘mkate2_tf’: 0, ‘mko2’: 0 }

def update_volume_remaining(current_color, quantity_to_aspirate): rows = string.ascii_uppercase for well, color in list(well_colors.items()): if color == current_color: if (volume_used[current_color] + quantity_to_aspirate) > 250: # Move to next well horizontally by advancing row letter, keeping column number row = well[0] col = well[1:]

            # Find next row letter
            next_row = rows[rows.index(row) + 1]
            next_well = f"{next_row}{col}"
            
            del well_colors[well]
            well_colors[next_well] = current_color
            volume_used[current_color] = quantity_to_aspirate
        else:
            volume_used[current_color] += quantity_to_aspirate
        break

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

# 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])

# PCR Plate
temperature_plate = protocol.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul', 6)

# Agar Plate
agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')
agar_plate.set_offset(x=0.00, y=0.00, z=Z_VALUE_AGAR)

# Get the top-center of the plate, make sure the plate was calibrated before running this
center_location = agar_plate['A1'].top()

pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

# Helper function (dispensing)
def dispense_and_jog(pipette, volume, location):
    assert(isinstance(volume, (int, float)))
    # Go above the location
    above_location = location.move(types.Point(z=location.point.z + 2))
    pipette.move_to(above_location)
    # Go downwards and dispense
    pipette.dispense(volume, location)
    # Go upwards to avoid smearing
    pipette.move_to(above_location)

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

# Print pattern by iterating over lists
for i, (current_color, point_list) in enumerate(point_name_pairing):
    # Skip the rest of the loop if the list is empty
    if not point_list:
        continue

    # Get the tip for this run, set the bacteria color, and the aspirate bacteria of choice
    pipette_20ul.pick_up_tip()
    max_aspirate = int(18 // POINT_SIZE) * POINT_SIZE
    quantity_to_aspirate = min(len(point_list)*POINT_SIZE, max_aspirate)
    update_volume_remaining(current_color, quantity_to_aspirate)
    pipette_20ul.aspirate(quantity_to_aspirate, location_of_color(current_color))

    # Iterate over the current points list and dispense them, refilling along the way
    for i in range(len(point_list)):
        x, y = point_list[i]
        adjusted_location = center_location.move(types.Point(x, y))

        dispense_and_jog(pipette_20ul, POINT_SIZE, adjusted_location)
        
        if pipette_20ul.current_volume == 0 and len(point_list[i+1:]) > 0:
            quantity_to_aspirate = min(len(point_list[i:])*POINT_SIZE, max_aspirate)
            update_volume_remaining(current_color, quantity_to_aspirate)
            pipette_20ul.aspirate(quantity_to_aspirate, location_of_color(current_color))

    # Drop tip between each color
    pipette_20ul.drop_tip()

Part 3: Post Lab Questions

1. Automation Plan for Final Project The integration of automation tools, specifically the Opentrons, aims to minimize human intervention while maximizing precision in complex biological workflows. Below is the proposed strategy for utilizing automation in the final project.

A. Automation in Reaction Synthesis (Opentrons) Utilizing robotic systems to control contamination, increase accuracy, and improve reproducibility in large-scale transformation processes.

Procedures to Automate:

PCR & rt-PCR: Precise master mix preparation and DNA template distribution.

Bacterial Culture: Automated inoculations and serial dilutions.

Molecular Transformation: Systematic development of plasmid constructs.

Hardware Requirements: Custom 3D printed holders for non-standard tubes or specific labware geometries.

B. Automation in Marker Detection Integration of software and specialized hardware to standardize results and eliminate manual bias.

Software Integration: Utilizing ImageJ or Matlab for high-resolution image processing and automated gel electrophoresis analysis.

Quantification: Fluorescence, absorbance, and luminescence assays (e.g., mimicking the workflow of the Varioskan™ ALF Multimode Microplate Reader).

C. Automation in Data Analysis Leveraging computational tools to handle large-scale experimental statistics and expression data.

Tools: RStudio, Python, IBM SPSS Statistics, and Stata.

Applications:

Analyzing gene/protein expression trends.

Biostatistical comparison of experimental results.

2. Literature Review: Automation in Biological Applications Case Study 1: DNA Assembly (AssemblyTron) Paper: AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots (2023)

Summary: This study introduces AssemblyTron, a Python-based package designed to automate the DNA assembly process. It streamlines the construction of plasmids and DNA fragments via homology or Golden Gate assembly.

Impact: Reduces human intervention to a minimum, ensuring high-fidelity assembly.

Link: Read Paper

Case Study 2: SARS-CoV-2 Diagnostic Strategy Paper: Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots

Summary: This research demonstrates a novel RT-qPCR method for saliva samples using open-source robots for the pipetting process.

Impact: Provides a cost-effective, scalable alternative to conventional manual methods while drastically reducing human experimental error.

Link: Read Paper

Case Study 3: Culture Media Inoculation Paper: Automation of inoculation in culture media for the microbiology laboratory

Summary: This article details the blueprint for an automated system dedicated to the inoculation of culture media.

Impact: Covers the entire production chain—from initial inoculation to disposal—significantly reducing contamination risks and mishandling.

Link: Read Paper

Additional Resource Review: Automated high-throughput DNA synthesis and assembly

Link: Read Review

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project