Week 1 HW: Principles and Practices

This page includes Class Assigment and Week 2 Lecture Preparation Questions

Class Assignment

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.

For HTGAA 2026, I’d like to propose the design and development of a synthetic biology based microbial system for the improvement of agricultural productivity in saline soils of the Bolivian Altiplano. This is because oil salinization is continuing to progress in the high-altitude areas of Bolivia as a consequence of climate change, water shortage and historical land use (Andrade, 2025). According to the Food and Agriculture Organization (n.d.), already a considerable fraction of irrigated and arid agricultural lands worldwide face the challenge of soil salinity. Scientific studies have shown that soil salinity significantly reduces crop yields, alters soil biological functions, and directly threatens food security, particularly in smallholder farming systems (Farooq et al., 2021). In the same way, the majority of smallholder farmers in the Altiplano rely on marginal soils, often where conventional fertilizers cannot be used effectively or are economically unaffordable and are a direct threat to local food security and livelihoods from salinization. This is why my proposed project aims to investigate the conceptual design for soil microorganisms that can sense such high salinity and improve soil structure and plant stress tolerance. However, beyond its technical feasibility, this application raises relevant ethical, environmental and governance issues surrounding environmental release and biosafety and also equitable access to biotechnology. Finally, as a Bolivian, I see this work as an opportunity to link cutting edge biological engineering with locally anchored solutions that address real challenges faced by vulnerable agricultural communities in my country.

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.

Main Goal: Ensuring Environmental Safety and Biosecurity This goal focuses on preventing ecological harm and unintended consequences associated with the environmental use of engineered microorganisms.

• Sub-goal 1: Preventing Uncontrolled Spread

-> Design biological containment mechanisms to limit survival outside target environments.

-> Require environmental risk assessments prior to any field deployment.

• Sub-goal 2: Reducing Ecological Uncertainty

-> Promote long-term monitoring of soil and microbial ecosystem impacts.

-> Establish protocols for detecting and responding to unintended ecological effects.

Main Goal: Promoting Equity and Responsible Use This goal ensures that the benefits of the technology reach vulnerable communities without reinforcing existing inequalities.

• Sub-goal 1: Supporting Smallholder Farmers

-> Ensure that the technology is affordable and adapted to local agricultural contexts.

-> Encourage community involvement in deployment decisions.

• Sub-goal 2: Preventing Technological Exploitation

-> Avoid extractive research practices in developing regions.

-> Promote benefit-sharing and local capacity building.

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

A) Biosafety by design through genetic containment. Purpose: Current agricultural biotechnology often relies on external monitoring after deployment. This action proposes embedding biosafety mechanisms directly into the engineered organisms.

Design:

• Implemented by academic researchers and biotech developers.
• Reviewed by institutional biosafety committees and environmental regulators.

Assumptions: Genetic containment systems function reliably in complex soil environments.

Risks of Failure & “Success”:

• Failure: Evolutionary escape from containment mechanisms
• Unintended Success: Reduced emphasis on ecological monitoring due to overconfidence in technical controls.

B) Regulatory frameworks for environmental synthetic biology. Purpose: Environmental release regulations are often unclear or inconsistent. This action proposes clearer regulatory pathways specific to environmental synthetic biology.

Design: National environmental and agricultural agencies conduct standardized risk assessments.

Assumptions: Regulators have sufficient technical expertise.

Risks of Failure & “Success”:

• Failure: Overregulation slows innovation
• Unintended Success: Rapid approval without sufficient local adaptation

C) Community centered deployment and oversight. Purpose: Agricultural technologies should align with the needs and values of affected communities.

Design:

• Collaboration among researchers, NGOs, and local farming communities.
• Participatory decision making processes.

Assumptions: Community participation is meaningful and informed.

Risks of Failure & “Success”:

• Failure: Delays due to conflicting priorities.
• Unintended Success: Token participation without real influence.

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. 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:

Based on the comparative scoring of the governance options, the approach that I would prioritize is a combination of biosafety by design and community centered governance. This is because embedding safety mechanisms directly into engineered soil microorganisms is essential to prevent unintended ecological harm and to address biosecurity concerns at the earliest stage of development. This option performs strongly in preventing incidents and maintaining laboratory and environmental safety, making it a foundational requirement for any responsible application of environmental synthetic biology. At the same time, community centered governance is critical for ensuring that this technology is ethically deployed in the Bolivian Altiplano and engaging local farming communities helps align the technology with real agricultural needs, promotes trust and reduces the risk of inequitable or extractive use.

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.

A key ethical concern that stood out to me was the increasing use of artificial intelligence in synthetic biology because AI tools can greatly accelerate the design of engineered microorganisms, such as those proposed in my project to improve agricultural productivity in saline soils of the Bolivian Altiplano. However, a new ethical issue for me was the possibility that decisions driven by AI models may lack transparency or embed biases, potentially leading to unintended ecological consequences when organisms are applied in open environments. In consequence, to address these issues, I would suggest appropriate governance actions; for example, transparency in the use of AI for biological design, rigorous validation and risk assessment prior to environmental application. In addition, governance frameworks should encourage participatory approaches that involve local communities and ensure that resulting technologies are accessible, safe and aligned with local agricultural needs.

Assignment (Week 2 Lecture Prep)

Homework Questions from Professor Jacobson:

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome? How does biology deal with that discrepancy?

DNA polymerase copies DNA with high accuracy as the raw error rate of DNA polymerase is about 1 mistake per 10⁵ nucleotides copied. However, most DNA polymerases also have a proofreading function which corrects many of these mistakes, improving accuracy to about 1 error per 10⁷ - 10⁸ nucleotides and after replication, additional DNA repair systems fix remaining errors, bringing the final error rate to roughly 1 mistake per 10⁹ - 10¹⁰ nucleotides. On the other hand, the human genome is about 3 × 10⁹ base pairs long which means that without repair, thousands of errors would occur when a cell divides. For the last question, biology deals with this discrepancy through three layers of control which are polymerase proofreading, mismatch repair and other DNA repair pathways, keeping mutation rates low enough for genome stability while still allowing evolution.

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?

Proteins are encoded using codons which are groups of three DNA nucleotides and there are 64 possible codons but only 20 amino acids plus stop signals. It is for this reason that most amino acids are encoded by multiple codons being this called degeneracy of the genetic code. On the other side, for an average human protein of about 400 amino acids, the number of possible DNA sequences that could encode the same protein is more than 10¹⁹ possible sequences. However, in practice, most of these sequences do not work well because some codons are translated more efficiently, certain sequences affect mRNA stability and others create unwanted secondary structures meanwhile some interfere with translation speed and protein folding. Moreover, regulatory elements, splicing signals, and GC content also limit which DNA sequences can successfully produce a functional protein in real cells.

Homework Questions from Dr. LeProust:

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

The most widely used nowadays is solid - phase phosphoramidite chemical synthesis and in this method DNA is built one nucleotide at a time on a solid support (controlled - pore glass). Also, each synthesis cycle adds one base through chemical reactions (deprotection, coupling, capping, oxidation) making this process fast and reliable for short DNA sequences being this the reason why it dominates both research and commercial oligo production.

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

Because each synthesis step is not 100% efficient. As oligos get longer, small inefficiencies compound leading to incorrect sequences. For example, after 200 cycles the fraction of full - length, correct molecules drops sharply. In addition, longer oligos accumulate chemical side products, have higher error rates and are harder to purify.

3. Why can’t you make a 2000bp gene via direct oligo synthesis? Because a 2000 bp gene would require 2000 consecutive chemical synthesis cycles that would result in a low yield of correct full - length DNA due to errors and the final product would be dominated by short fragments and mutated sequences, making purification not practical. Instead, long genes are made by assembling shorter, high - quality oligos through Gibson assembly or Golden Gate which improves accuracy and yield.

Homework Question from George Church:

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 that all animals have are histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arginine (HyperPhysics, n.d.). On the other hand my view of “lysine contingency” now makes me think that as all animals require lysine from their environment, synthetic biology could turn this constraint into a design principle in this area by engineering organisms that depend on externally supplied lysine and scientists would be able to control growth, improve biosafety and limit ecological spread. This would be very interesting for applications in agriculture, in my opinion.

References.

Andrade, D. (2025). Characterization, prediction, and remediation of salt-affected soils in the High Valley of Cochabamba - Bolivia (Doctoral thesis, Université de Liège - Gembloux Agro-Bio Tech). ORBi-University of Liège. https://orbi.uliege.be/handle/2268/325556

Farooq, M., et al. (2021). Climate change and salinity effects on crops and plant–microbe interactions. Frontiers in Sustainable Food Systems, 5, 618092. https://www.frontiersin.org/articles/10.3389/fsufs.2021.618092/full

Food and Agriculture Organization of the United Nations. (n.d.). Soil salinity. FAO Global Soil Partnership. https://www.fao.org/global-soil-partnership/areas-of-work/soil-salinity/en/

HyperPhysics. (n.d.). Essential Amino Acids. HSC.edu.kw. http://www.hsc.edu.kw/student/materials/Physics/website/hyperphysics%20modified/hbase/organic/essam.html

Week 2 HW: DNA Read, Write, & Edit

HOMEWORK 2

Part 1: Benchling & In-silico Gel Art

See this week’s lab protocol “Gel Art: Restriction Digests and Gel Electrophoresis” for details. Overview:

• Make a free account at benchling.com
• Import the Lambda DNA.
• Simulate Restriction Enzyme Digestion with the following Enzymes:

1. EcoRI
2. HindIII
3. BamHI
4. KpnI
5. EcoRV
6. SacI
7. 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!

HOMEWORK RESULTS :)

1ST ATTEMPT

For my first attempt, I tried to form the phrase “Hi!”. It didn’t turn out as perfect as I imagined, but with practice, I hope to create more creative drawings.

2ND ATTEMPT

For my second attempt I tried to draw my own name in capital letters, “IAN”.

3RD ATTEMPT

For my third attempt I tried to draw the silhouette of an animal’s head.

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.

The protein I chose is …

Q68KI4 · NHX1_ARATH

Function: Acts in low affinity electroneutral exchange of protons for cations such as Na+ or K+ across membranes. Can also exchange Li+ and Cs+ with a lower affinity. Involved in vacuolar ion compartmentalization necessary for cell volume regulation and cytoplasmic Na+ detoxification. Required during leaves expansion, probably to stimulate epidermal cell expansion. Confers competence to grow in high salinity conditions.

FASTA sequence

sp|Q68KI4|NHX1_ARATH Sodium/hydrogen exchanger 1 OS=Arabidopsis thaliana OX=3702 GN=NHX1 PE=1 SV=2 MLDSLVSKLPSLSTSDHASVVALNLFVALLCACIVLGHLLEENRWMNESITALLIGLGTG VTILLISKGKSSHLLVFSEDLFFIYLLPPIIFNAGFQVKKKQFFRNFVTIMLFGAVGTII SCTIISLGVTQFFKKLDIGTFDLGDYLAIGAIFAATDSVCTLQVLNQDETPLLYSLVFGE GVVNDATSVVVFNAIQSFDLTHLNHEAAFHLLGNFLYLFLLSTLLGAATGLISAYVIKKL YFGRHSTDREVALMMLMAYLSYMLAELFDLSGILTVFFCGIVMSHYTWHNVTESSRITTK HTFATLSFLAETFIFLYVGMDALDIDKWRSVSDTPGTSIAVSSILMGLVMVGRAAFVFPL SFLSNLAKKNQSEKINFNMQVVIWWSGLMRGAVSMALAYNKFTRAGHTDVRGNAIMITST ITVCLFSTVVFGMLTKPLISYLLPHQNATTSMLSDDNTPKSIHIPLLDQDSFIEPSGNHN VPRPDSIRGFLTRPTRTVHYYWRQFDDSFMRPVFGGRGFVPFVPGSPTERNPPDLSKA

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.

[Example: Get to the original sequence of phage MS2 L-protein from its genome phage MS2 genome - Nucleotide - NCBI]

Reverse Translation:

reverse translation of sp|Q68KI4|NHX1_ARATH Sodium/hydrogen exchanger 1 OS=Arabidopsis thaliana OX=3702 GN=NHX1 PE=1 SV=2 to a 1614 base sequence of most likely codons. atgctggatagcctggtgagcaaactgccgagcctgagcaccagcgatcatgcgagcgtggtggcgctgaacctgtttgtggcgctgctgtgcgcgtgcattgtgctgggccatctgctggaagaaaaccgctggatgaacgaaagcattaccgcgctgctgattggcctgggcaccggcgtgaccattctgctgattagcaaaggcaaaagcagccatctgctggtgtttagcgaagatctgttttttatttatctgctgccgccgattatttttaacgcgggctttcaggtgaaaaaaaaaacagttttttcgcaactttgtgaccattatgctgtttggcgcggtgggcaccattattagctgcaccattattagcctgggcgtgacccagttttttaaaaaactggatattggcacctttgatctgggcgattatctggcgattggcgcgatttttgcggcgaccgatagcgtgtgcaccctgcaggtgctgaaccaggatgaaaccccgctgctgtatagcctggtgtttggcgaaggcgtggtgaacgatgcgaccagcgtggtggtgtttaacgcgattcagagctttgatctgacccatctgaaccatgaagcggcgtttcatctgctgggcaactttctgtatctgtttctgctgagcaccctgctgggcgcggcgaccggcctgattagcgcgtatgtgattaaaaaactgtattttggccgccatagcaccgatcgcgaagtggcgctgatgatgctgatggcgtatctgagctatatgctggcggaactgtttgatctgagcggcattctgaccgtgtttttttgcggcattgtgatgagccattatacctggcataacgtgaccgaaagcagccgcattaccaccaaacatacctttgcgaccctgagctttctggcggaaacctttatttttctgtatgtgggcatggatgcgctggatattgataaatggcgcagcgtgagcgataccccgggcaccagcattgcggtgagcagcattctgatgggcctggtgatggtgggccgcgcggcgtttgtgtttccgctgagctttctgagcaacctggcgaaaaaaaaccagagcgaaaaaattaactttaacatgcaggtggtgatttggtggagcggcctgatgcgcggcgcggtgagcatggcgctggcgtataacaaatttacccgcgcgggccataccgatgtgcgcggcaacgcgattatgattaccagcaccattaccgtgtgcctgtttagcaccgtggtgtttggcatgctgaccaaaccgctgattagctatctgctgccgcatcagaacgcgaccaccagcatgctgagcgatgataacaccccgaaaagcattcatattccgctgctggatcaggatagctttattgaaccgagcggcaaccataacgtgccgcgcccggatagcattcgcggctttctgacccgcccgacccgcaccgtgcattattattggcgccagtttgatgatagctttatgcgcccggtgtttggcggccgcggctttgtgccgtttgtgccgggcagcccgaccgaacgcaacccgccggatctgagcaaagcg

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?

[Example from Codon Optimization Tool | Twist Bioscience while avoiding Type IIs enzyme recognition sites BsaI, BsmBI, and BbsI]

Codon Optimized sequence

NHX1_optimized_IDT ATGTTAGATTCTTTAGTTAGCAAATTGCCCTCACTCTCAACCTCTGACCACGCCAGCGTGGTTGCGCTGAACCTGTTTGTGGCGCTGCTGTGTGCCTGTATTGTGCTGGGCCACCTGCTGGAAGAAAACCGCTGGATGAATGAATCCATCACTGCGCTGCTGATCGGCCTGGGTACTGGTGTCACCATCCTGCTGATCAGTAAAGGCAAAGCTCCACCTGCTGGTGTTCTCTGAAGATCTGTTCTTTATCTATCTGCTGCCGCCGATCATCTTCAACGCCGGTTTCCAGGTGAAAAAGAAACAGTTCTTCCGTAATTCGTCACCATCATGCTGTTTGGTGCGGTAGGTACCATTATCAGCTGTACCATTATCAGCCTGGGTGTGACTCAGTTCTTCAAAAAACTGGATATCGGTACCTTTGACCTGGGTGATTATCTTGCGATTGGTGCGATCTTTGCTGCAACCGACAGTGTGTGCACCCTGCAGGTGCTGAACCAGGATGAAACCCCGCTGCTGTACAGCCTGGTGTTCGGTGAAGGTGTGGTGAACGATGCGACCTCGGTGGTGGTTTTAACGCCATTCAGAGCTTTGACCTGACCCATCTGAACCATGAAGCGGCGTTCCACCTGCTCGGCAACTTCCTGTACCTGTTCCTGCTGTCCACCCTGCTGGGTGCGGCGACCGGTCTGATCTCTGCCTATGTGATCAAGAAGCTGTATTTGGTCGTCACAGCACCGACCGCGAAGTTGCACTGATGATGCTGATGGCGTACCTGAGCTACATGCTGGCAGAGCTGTTTGACCTCAGTGGTATCCTGACCGTGTTCTTCTGCGGTATTGTCATGAGCCACTACACCTGGCATAACGTGACTGAAGCAGCCGTATCACCACCAAACACACCTTTGCCACCCTGTCGTTCTTGGCTGAAACCTTTATCTTCCTGTATGTCGGTATGGATGCGCTGGACATCGATAAGTGGCGCTCGGTAAGCGACACACCGGGTACCTCTATTGCGGTTAGCTCGATTCTGATGGGCCTGGTGATGGTAGGTCGTGCGGCGTTCGTGTTCCCGCTGTCGTTCTTGAGCAACCTGGCGGAGAAGAACCAGTCTGAGAAAATCAACTTCAACATGCAGGTGGTGATCTGGTGGTCTGGGCTGATGCGTGGTGCAGTCTCTATGGCCCTGGCCTACAACAAGTTTACCCGTGCAGGTCACACTGATGTACGTGGTAATGCGATTATGATCACCTCCACCATCACCGTGTGCCTGTTCAGCACCGTGGTGTTTGGCATGCTGACCAAACCGCTGATCAGCTACCTGCTGCCGCATCAGAATGCCACCACCAGCATGCTGTCTGATGACAACACGCCGAAATCTATTCACATTCCGCTGCTGGATCAGGACAGCTTTATTGAGCCGTCTGGTAACCACAATGTTCCACGTCCGGACAGCATTCGCGGTTTCCTGACCCGCCCGACCCGCACCGTGCACTACTATTGGCGTCAGTTTGATGACTCCTTCATGCGCCCGGTGTTTGGTGGTCGCGGCTTTGTGCCGTTTGTTCCGGGCTCCCCAACTGAGCGTAACCCGCCGGATCTGAGCAAAGCA

My answer:

Codon optimization is necessary because, although multiple codons can encode the same amino acid, each organism preferentially uses certain codons over others. For example, If a gene from one organism is expressed in a different host without optimization, rare codons may reduce translation efficiency, slow ribosome movement, decrease protein yield, or cause premature termination. The NHX1 coding sequence was optimized according to the codon usage preference of Escherichia coli, which was selected because it is one of the most widely used systems for recombinant protein expression due to its rapid growth, well - characterized genetics and availability of expression vectors and laboratory tools.

On the other hand, the codon optimization was performed using the IDT Codon Optimization Tool (Integrated DNA Technologies). During optimization, the tool adjusted synonymous codons to match E. coli codon bias while maintaining the original amino acid sequence. Additionally, to facilitate downstream cloning strategies, recognition sites for Type IIS restriction enzymes BsaI, BsmBI, and BbsI were avoided during the optimization process which ensures compatibility with Golden Gate assembly and prevents unwanted internal digestion of the gene sequence.

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.

My answer:

Cell-dependent protein expression

This option would clone the optimized NHX1 gene into an expression vector (plasmid) containing:

• A strong promoter.
• A ribosome binding site (RBS).
• A selectable marker (antibiotic resistance gene)
• A transcription terminator

The recombinant plasmid is then introduced into a host (Escherichia coli in this case), through transformation. Once inside the cell, the DNA sequence is transcribed, where RNA polymerase recognizes the promoter and synthesizes messenger RNA (mRNA) complementary to the coding strand of the DNA and translated where ribosomes bind to the mRNA and read the codons in triplets. Transfer RNAs (tRNAs) bring the corresponding amino acids, which are linked together through peptide bonds to form the NHX1 protein.

Part 4: Prepare a Twist DNA Synthesis Order

4.1. Create a Twist account and a Benchling account

4.2. Build Your DNA Insert Sequence

For example, let’s make a sequence that will make E. coli glow fluorescent green under UV light by constitutively (always) expressing sfGFP (a green fluorescent protein): In Benchling, select New DNA/RNA sequence Give your insert sequence a name and select DNA with a Linear topology (this is a linear sequence that will be inserted into a circular backbone vector of our choosing).

The image above shows the Codon Optimized sequence of Q68KI4 · NHX1_ARATH.

Go through each piece of the given DNA sequences highlighted below (Promoter, RBS, Start Codon, Coding Sequence, His Tag, Stop Codon, Terminator) and paste the sequences into the Benchling file one after the other (replacing the coding sequence with your codon optimized DNA sequence of interest!). Each time you add a new piece of the sequence, make sure to annotate by right clicking over the sequence and creating an annotation that describes what each piece (e.g., Promoter, RBS, etc.) is.

Promoter (e.g. BBa_J23106): TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGC

RBS (e.g. BBa_B0034 with spacers for optimal expression): CATTAAAGAGGAGAAAGGTACC

Start Codon: ATG

Coding Sequence (your codon optimized DNA for a protein of interest, sfGFP for example): AGCAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCCGTGGAGAGGGTGAAGGTGATGCTACAAACGGAAAACTCACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCGTGGCCAACACTTGTCACTACTCTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCACATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGACCTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAGGGTATTGATTTTAAAGAAGATGGAAACATTCTTGGACACAAACTCGAGTACAACTTTAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACGTTGAAGATGGTTCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGTCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAA

7x His Tag (Let’s add a 7×His tag at the C-terminus of the protein to enable protein purification from E. coli): CATCACCATCACCATCATCAC

Stop Codon: TAA

Terminator (e.g. BBa_B0015): CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

Once you’ve completed this, click on Linear Map to preview the entire sequence. If you intend to have a TA review a sequence in the future, this is a good way to verify that all sections are annotated!

https://benchling.com/ian-teran-35/f_/91Ap236lfD-htgaa-2026/

Downloaded FASTA sequence of the construct:

AtNHX1_Ecoli_expression_construct TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGCCATTAAAGAGGAGAAAGGTACCATGTTAGATTCTTTAGTTAGCAAATTGCCCTCACTCTCAACCTCTGACCACGCCAGCGTGGTTGCGCTGAACCTGTTTGTGGCGCTGCTGTGTGCCTGTATTGTGCTGGGCCACCTGCTGGAAGAAAACCGCTGGATGAATGAATCCATCACTGCGCTGCTGATCGGCCTGGGTACTGGTGTCACCATCCTGCTGATCAGTAAAGGCAAAGCTCCACCTGCTGGTGTTCTCTGAAGATCTGTTCTTTATCTATCTGCTGCCGCCGATCATCTTCAACGCCGGTTTCCAGGTGAAAAAGAAACAGTTCTTCCGTAATTCGTCACCATCATGCTGTTTGGTGCGGTAGGTACCATTATCAGCTGTACCATTATCAGCCTGGGTGTGACTCAGTTCTTCAAAAAACTGGATATCGGTACCTTTGACCTGGGTGATTATCTTGCGATTGGTGCGATCTTTGCTGCAACCGACAGTGTGTGCACCCTGCAGGTGCTGAACCAGGATGAAACCCCGCTGCTGTACAGCCTGGTGTTCGGTGAAGGTGTGGTGAACGATGCGACCTCGGTGGTGGTTTTAACGCCATTCAGAGCTTTGACCTGACCCATCTGAACCATGAAGCGGCGTTCCACCTGCTCGGCAACTTCCTGTACCTGTTCCTGCTGTCCACCCTGCTGGGTGCGGCGACCGGTCTGATCTCTGCCTATGTGATCAAGAAGCTGTATTTGGTCGTCACAGCACCGACCGCGAAGTTGCACTGATGATGCTGATGGCGTACCTGAGCTACATGCTGGCAGAGCTGTTTGACCTCAGTGGTATCCTGACCGTGTTCTTCTGCGGTATTGTCATGAGCCACTACACCTGGCATAACGTGACTGAAGCAGCCGTATCACCACCAAACACACCTTTGCCACCCTGTCGTTCTTGGCTGAAACCTTTATCTTCCTGTATGTCGGTATGGATGCGCTGGACATCGATAAGTGGCGCTCGGTAAGCGACACACCGGGTACCTCTATTGCGGTTAGCTCGATTCTGATGGGCCTGGTGATGGTAGGTCGTGCGGCGTTCGTGTTCCCGCTGTCGTTCTTGAGCAACCTGGCGGAGAAGAACCAGTCTGAGAAAATCAACTTCAACATGCAGGTGGTGATCTGGTGGTCTGGGCTGATGCGTGGTGCAGTCTCTATGGCCCTGGCCTACAACAAGTTTACCCGTGCAGGTCACACTGATGTACGTGGTAATGCGATTATGATCACCTCCACCATCACCGTGTGCCTGTTCAGCACCGTGGTGTTTGGCATGCTGACCAAACCGCTGATCAGCTACCTGCTGCCGCATCAGAATGCCACCACCAGCATGCTGTCTGATGACAACACGCCGAAATCTATTCACATTCCGCTGCTGGATCAGGACAGCTTTATTGAGCCGTCTGGTAACCACAATGTTCCACGTCCGGACAGCATTCGCGGTTTCCTGACCCGCCCGACCCGCACCGTGCACTACTATTGGCGTCAGTTTGATGACTCCTTCATGCGCCCGGTGTTTGGTGGTCGCGGCTTTGTGCCGTTTGTTCCGGGCTCCCCAACTGAGCGTAACCCGCCGGATCTGAGCAAAGCACATCACCATCACCATCATCACTAACCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

4.3. On Twist, Select The “Genes” Option

4.4. Select “Clonal Genes” option

For this demonstration, we’ll choose Clonal Genes. You’ll select clonal genes or gene fragments depending on your final project. Historically, HTGAA projects using clonal genes (circular DNA) have reached experimental results 1-2 weeks quicker because they can be transformed directly into E. coli without additional assembly. Gene fragments (linear DNA) offer greater design flexibility but typically require an assembly or cloning step prior to transformation. An advantage is If designed with the appropriate exonuclease protection, gene fragments can be used directly in cell-free expression.

4.5. Import your sequence

You just took an amino acid sequence of interest and converted it into DNA, codon optimized it, and built an expression cassette around it! Choose the Nucleotide Sequence option and Upload Sequence File to upload your FASTA file.

4.6. Choose Your Vector

Since we’re ordering a clonal gene, you will need to refer to Twist’s Vector Catalog to choose your circular backbone. You can think of this as taking your linear expression cassette for your protein of interest, and completing the rest of the circle!

The backbone confers many special properties like antibiotic resistance, an origin of replication, and more. Discuss with your node to decide on appropriate antibiotic options. At MIT/Harvard, you can use Ampicillin, Chloramphenicol, or Kanamycin resistance.

Twist vectors do not contain restriction sites near the insert fragment, so make sure to flank your design with cut sites if you are intending to extract this DNA insert fragment later.

For this demonstration, choose a Twist cloning vectors like pTwist Amp High Copy. Click into your sequence and select download construct (GenBank) to get the full plasmid sequence:

Go back to your Benchling account. Inside of a folder, click the import DNA/RNA sequence button and upload the GenBank file you just downloaded.

WOW! :)

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 would sequence the cry4Ba gene from Bacillus thuringiensis isolates in the field, the promoter and regulatory regions controlling cry4Ba expression and comparable cry4 family homologs from different strains because I would like to understand the genetic diversity of cry4Ba which would be useful to explore new methods to improve efficacy against mosquito larvae, reveal natural sequence variation influencing toxicity and assist in environmental monitoring of Bt toxin dissemination.

(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:

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

The technology I would use is Illumina short-read sequencing which is a second generation sequencing method. It provides high accuracy, cost effectiveness and is well suited to bacterial genes.

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

• DNA extraction
• Fragmentation (to ~300 bp)
• Adapter ligation
• PCR enrichment
• Library quantification & pooling

The input is Genomic DNA from Bacillus thuringiensis cultures.

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

• Sequencing-by-synthesis.
• Each base is read by fluorescently labeled nucleotides incorporated one at a time.
• Signals are captured and used for base calling.

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

FASTQ files of read sequences and paired reads that can be aligned to reference genomes.

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 would design and synthesize a codon-optimized cry4Ba gene for high toxin expression in a chosen bacterial host, variant versions with enhanced insecticidal activity and chimeric constructs combining parts of different Cry proteins to improve biological control of mosquitoes, increase production yield in recombinant strains and make toxin variants tailored to resistant insect populations.

cry4Ba gene DNA sequence:

ATGATGAATTCTGGTTATCCTTTAGCTAATGATTTACAAGGTTCTATGAAAAATACTAATTATAAAGATTGGTTAGCTATGTGTGAAAATAATCAACAATATGGTGTTAATCCTGCTGCTATTAATTCTTCTTCTGTTTCTACTGCTTTAAAAGTTGCTGGTGCTATTTTAAAATTTGTTAATCCTCCTGCTGGTACTGTTTTAACTGTTTTATCTGCTGTTTTACCTATTTTATGGCCTACTAATACTCCTACTCCTGAACGTGTTTGGAATGATTTTATGACTAATACTGGTAATTTAATTGATCAAACTGTTACTGCTTATGTTCGTACTGATGCTAATGCTAAAATGACTGTTGTTAAAGATTATTTAGATCAATATACTACTAAATTTAATACTTGGAAACGTGAACCTAATAATCAATCTTATCGTACTGCTGTTATTACTCAATTTAATTTAACTTCTGCTAAATTACGTGAAACTGCTGTTTATTTTTCTAATTTAGTTGGTTATGAATTATTATTATTACCTATTTATGCTCAAGTTGCTAATTTTAATTTATTATTAATTCGTGATGGTTTAATTAATGCTCAAGAATGGTCTTTAGCTCGTTCTGCTGGTGATCAATTATATAATACTATGGTTCAATATACTAAAGAATATATTGCTCATTCTATTACTTGGTATAATAAAGGTTTAGATGTTTTACGTAATAAATCTAATGGTCAATGGATTACTTTTAATGATTATAAACGTGAAATGACTATTCAAGTTTTAGATATTTTAGCTTTATTTGCTTCTTATGATCCTCGTCGTTATCCTGCTGATAAAATTGATAATACTAAATTATCTAAAACTGAATTTACTCGTGAAATTTATACTGCTTTAGTTGAATCTCCTTCTTCTAAATCTATTGCTGCTTTAGAAGCTGCTTTAACTCGTGATGTTCATTTATTTACTTGGTTAAAACGTGTTGATTTTTGGACTAATACTATTTATCAAGATTTACGTTTTTTATCTGCTAATAAAATTGGTTTTTCTTATACTAATTCTTCTGCTATGCAAGAATCTGGTATTTATGGTTCTTCTGGTTTTGGTTCTAATTTAACTCATCAAATTCAATTAAATTCTAATGTTTATAAAACTTCTATTACTGATACTTCTTCTCCTTCTAATCGTGTTACTAAAATGGATTTTTATAAAATTGATGGTACTTTAGCTTCTTATAATTCTAATATTACTCCTACTCCTGAAGGTTTACGTACTACTTTTTTTGGTTTTTCTACTAATGAAAATACTCCTAATCAACCTACTGTTAATGATTATACTCATATTTTATCTTATATTAAAACTGATGTTATTGATTATAATTCTAATCGTGTTTCTTTTGCTTGGACTCATAAAATTGTTGATCCTAATAATCAAATTTATACTGATGCTATTACTCAAGTTCCTGCTGTTAAATCTAATTTTTTAAATGCTACTGCTAAAGTTATTAAAGGTCCTGGTCATACTGGTGGTGATTTAGTTGCTTTAACTTCTAATGGTACTTTATCTGGTCGTATGGAAATTCAATGTAAAACTTCTATTTTTAATGATCCTACTCGTTCTTATGGTTTACGTATTCGTTATGCTGCTAATTCTCCTATTGTTTTAAATGTTTCTTATGTTTTACAAGGTGTTTCTCGTGGTACTACTATTTCTACTGAATCTACTTTTTCTCGTCCTAATAATATTATTCCTACTGATTTAAAATATGAAGAATTTCGTTATAAAGATCCTTTTGATGCTATTGTTCCTATGCGTTTATCTTCTAATCAATTAATTACTATTGCTATTCAACCTTTAAATATGACTTCTAATAATCAAGTTATTATTGATCGTATTGAAATTATTCCTATTACTCAATCTGTTTTAGATGAAACTGAAAATCAAAATTTAGAATCTGAACGTGAAGTTGTTAATGCTTTATTTACTAATGATGCTAAAGATGCTTTAAATATTGGTACTACTGATTATGATATTGATCAAGCTGCTAATTTAGTTGAATGTATTTCTGAAGAATTATATCCTAAAGAAAAAATGTTATTATTAGATGAAGTTAAAAATGCTAAACAATTATCTCAATCTCGTAATGTTTTACAAAATGGTGATTTTGAATCTGCTACTTTAGGTTGGACTACTTCTGATAATATTACTATTCAAGAAGATGATCCTATTTTTAAAGGTCATTATTTACATATGTCTGGTGCTCGTGATATTGATGGTACTATTTTTCCTACTTATATTTTTCAAAAAATTGATGAATCTAAATTAAAACCTTATACTCGTTATTTAGTTCGTGGTTTTGTTGGTTCTTCTAAAGATGTTGAATTAGTTGTTTCTCGTTATGGTGAAGAAATTGATGCTATTATGAATGTTCCTGCTGATTTAAATTATTTATATCCTTCTACTTTTGATTGTGAAGGTTCTAATCGTTGTGAAACTTCTGCTGTTCCTGCTAATATTGGTAATACTTCTGATATGTTATATTCTTGTCAATATGATACTGGTAAAAAACATGTTGTTTGTCAAGATTCTCATCAATTTTCTTTTACTATTGATACTGGTGCTTTAGATACTAATGAAAATATTGGTGTTTGGGTTATGTTTAAAATTTCTTCTCCTGATGGTTATGCTTCTTTAGATAATTTAGAAGTTATTGAAGAAGGTCCTATTGATGGTGAAGCTTTATCTCGTGTTAAACATATGGAAAAAAAATGGAATGATCAAATGGAAGCTAAACGTTCTGAAACTCAACAAGCTTATGATGTTGCTAAACAAGCTATTGATGCTTTATTTACTAATGTTCAAGATGAAGCTTTACAATTTGATACTACTTTAGCTCAAATTCAATATGCTGAATATTTAGTTCAATCTATTCCTTATGTTTATAATGATTGGTTATCTGATGTTCCTGGTATGAATTATGATATTTATGTTGAATTAGATGCTCGTGTTGCTCAAGCTCGTTATTTATATGATACTCGTAATATTATTAAAAATGGTGATTTTACTCAAGGTGTTATGGGTTGGCATGTTACTGGTAATGCTGATGTTCAACAAATTGATGGTGTTTCTGTTTTAGTTTTATCTAATTGGTCTGCTGGTGTTTCTCAAAATGTTCATTTACAACATAATCATGGTTATGTTTTACGTGTTATTGCTAAAAAAGAAGGTCCTGGTAATGGTTATGTTACTTTAATGGATTGTGAAGAAAATCAAGAAAAATTAACTTTTACTTCTTGTGAAGAAGGTTATATTACTAAAACTGTTGATGTTTTTCCTGATACTGATCGTGTTCGTATTGAAATTGGTGAAACTGAAGGTTCTTTTTATATTGAATCTATTGAATTAATTTGTATGAATGAATAA

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

I would use high throughput chemical DNA synthesis combined with assembly methods such as Gibson Assembly because chemical oligonucleotide synthesis (phosphoramidite chemistry) is the standard technology to produce short DNA fragments with controlled sequence and high purity and for this reason, they can be assembled into the full length cry4Ba gene using Gibson Assembly which joins overlapping oligonucleotides in a single reaction. I would choose this combination because it enables accurate synthesis of long genes, allows codon optimization for different expression hosts and supports easy modular design.

Also answer the following questions:

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

• Oligo synthesis
• Purification
• Gene assembly
• Cloning into an expression vector

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

• Cost increases with length.
• Errors may occur during oligo synthesis.
• Requires verification.

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 would edit the cry4Ba coding sequence to enhance toxicity or stability, regulatory elements to improve expression in non-Bt hosts and domains to broaden target specificity. The goal of this DNA Edit would be to develop improved insecticides or novel delivery systems.

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

I would use CRISPR/Cas9 because it allows precise modification of DNA by using a guide RNA (gRNA) that directs the Cas9 nuclease to a specific sequence within the cry4Ba gene and it is highly specific, relatively easy to design, efficient in bacteria and also scalable for generating multiple toxin variants. On the other hand, If I wanted to introduce small point mutations to improve toxin activity or stability without creating double-strand breaks I would use CRISPR base editors enabling single nucleotide changes with greater precision and lower risk of unwanted insertions or deletions.

Also answer the following questions:

1. How does your technology of choice edit DNA? What are the essential steps?

CRISPR/Cas9 edits DNA by creating a targeted double strand break at a specific sequence defined by a designed guide RNA (gRNA) that would be complementary to the cry4Ba locus as it is computationally designed to match the desired target site and cloned or synthesized. Then, the Cas9 nuclease and gRNA are delivered into Bacillus cells via plasmid transformation. Once inside the cell, the gRNA directs Cas9 to the target sequence, where Cas9 introduces a precise cut in the DNA and the cell’s natural DNA repair mechanisms then repair the break either through non-homologous end joining or homologous recombination if a donor DNA template containing desired modifications is provided. Finally, edited colonies are screened and verified by PCR and sequencing to confirm the intended modification.

2. 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?

• Design guide RNA targeting cry4Ba.
• Cas9 delivery vector or ribonucleoprotein.
• Editing template with desired mutations.
• Bacterial host cells.

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

• Off - target activity (needs careful design).
• Editing efficiency depends on repair pathways.
• Delivery methods vary in success.

Week 3 HW: Lab Automation

Assignment: Python Script for Opentrons Artwork

Link: https://colab.research.google.com/drive/1G9DLa7Y6og9m0Ik8HrF0KU1YG555WzoK?usp=sharing

MY CODE:

import math

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

GREEN SECTION (Body + Flagella)

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

pipette_20ul.pick_up_tip() center = center_location

Oval body:

a = 16 b = 8 points = 40

for i in range(points):

  if i % 8 == 0:
      pipette_20ul.aspirate(8, location_of_color('Green'))

  angle = 2 * math.pi * i / points
  x = a * math.cos(angle)
  y = b * math.sin(angle)

  loc = center.move(types.Point(x=x, y=y, z=0))
  dispense_and_detach(pipette_20ul, 1, loc)

Flagella:

flagella_points = 6

for i in range(flagella_points):

  pipette_20ul.aspirate(6, location_of_color('Green'))

  angle = 2 * math.pi * i / flagella_points
  start_x = (a + 1) * math.cos(angle)
  start_y = (b + 1) * math.sin(angle)

  for t in range(5):
      fx = start_x + t * 2 * math.cos(angle)
      fy = start_y + t * 2 * math.sin(angle)
      loc = center.move(types.Point(x=fx, y=fy, z=0))
      dispense_and_detach(pipette_20ul, 1, loc)

pipette_20ul.drop_tip()

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

RED SECTION (Eyes + Smile)

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

pipette_20ul.pick_up_tip()

Eyes:

pipette_20ul.aspirate(4, location_of_color(‘Red’))

left_eye = center.move(types.Point(x=-5, y=2, z=0))

right_eye = center.move(types.Point(x=5, y=2, z=0))

dispense_and_detach(pipette_20ul, 2, left_eye)

dispense_and_detach(pipette_20ul, 2, right_eye)

Smile:

smile_points = 15

pipette_20ul.aspirate(15, location_of_color(‘Red’))

for i in range(smile_points):

  angle = math.pi * i / smile_points
  
  
  x = 6 * math.cos(angle)
  y = -3 * math.sin(angle) - 2
  loc = center.move(types.Point(x=x, y=y, z=0))
  
  dispense_and_detach(pipette_20ul, 1, loc)

pipette_20ul.drop_tip()

Don’t forget to end with a drop_tip()

RESULT :) :

AI tools (ChatGPT and Gemini) assisted in suggesting mathematical approaches for generating an oval body and radial flagella. I reviewed, modified and finalized the code to ensure correct simulation behavior and compliance with lab constraints (volume limits).

Post-Lab Questions:

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:

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

Paper link: https://www.sciencedirect.com/science/article/pii/S2472630325000263

This paper describes how an Opentrons - 2 liquid handling robot can be used to automate and scale up protein crystallization experiments which is a foundational step in structural biology that is traditionally manual intensive. The robot was programmed via Python scripts to prepare 24 well sitting drop crystallization trials with precise reagent mixing and drop deposition. By comparing results against standard manual setup, the study showed that automation:

• Reduce hands-on labor and variability, improving reproducibility.
• Produce consistent crystal growth for both model proteins (hen egg white lysozyme) and a periplasmic protein from Campylobacter jejuni.
• Scale preparation in a way that could benefit labs doing structural studies or materials research requiring uniform crystal batches.

I find this application novel because protein crystallization is a critical but laborious step in X - ray crystallography and related structural methods. Most labs still perform it manually or with high-cost automation. This work shows that a relatively low - cost, open-programmable robot like Opentrons - 2 can reliably handle complex setup steps, lowering the barrier to high-throughput crystallization workflows and enabling new scale and reproducibility in structural biology applications

2. Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details. While your description/project idea doesn’t need to be set in stone, we would like to see core details of what you would automate. This is due at the start of lecture and does not need to be tested on the Opentrons yet.

What I Would Automate?

A) Automated DNA Assembly & Construct Library Design

Using a liquid handling robot such as the Opentrons OT-2, I would automate Golden Gate or Gibson assembly reactions to generate a small library of salt-inducible constructs:

• Normalize DNA part concentrations.
• Set up combinatorial assembly reactions.
• Transform into competent cells.
• Plate onto selective media.

Python:

for promoter in promoter_list:

for gene in response_genes:

    assemble_mix = {
    
        "promoter": promoter,
        "gene": gene,
        "backbone": vector
    }
    
    pipette.transfer(2, promoter, assembly_well)
    
    pipette.transfer(2, gene, assembly_well)
    
    pipette.transfer(1, backbone, assembly_well)
    
    pipette.mix(5, 10, assembly_well)

B) Automated Salinity Gradient Screening

To test salt responsiveness the Opentrons OT-2 liquid handling robot would:

• Prepare a 96-well plate with increasing NaCl concentrations (0–400 mM).
• Inoculate engineered strains.

If I would use a cloud lab platform such as Ginkgo Bioworks’s Ginkgo Nebula (conceptually), the workflow would include:

-Automated liquid handling for salt gradients. -Plate sealing and incubation. -Automated plate reader measurements. -Data export for downstream analysis.

C) Data Processing & Optimization

I would automate analysis using Python:

import pandas as pd

import matplotlib.pyplot as plt

data = pd.read_csv(“plate_reader_output.csv”)

grouped = data.groupby(“NaCl_concentration”).mean()

plt.plot(grouped.index, grouped[“fluorescence”])

plt.xlabel(“NaCl (mM)”)

plt.ylabel(“Normalized Fluorescence”)

plt.show()

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project