Week 1 HW: Principles and Practices

Engineered Biosensors For the Detection of Illegal Mining Pollutants

Week One’s Principles and Practice class taught us the foundations of ethics, safety, and governance using biotechnology. While pondering ideas for the bioengineered tool or application, I was inspired by the battle against the ongoing menace of small-scale illegal mining in Ghana propularly known as “Galamsey”.

Illegal mining is the extraction of minerals, metals, or other resources without proper authorization, permits, or compliance with national laws or regulations. It leads to the destruction of forests, leading to the loss of biodiversity, land degradation, and water pollution of rivers and groundwater with pollutants such as mercury, cyanide, and arsenic. Water pollution from galamsey activities is causing chronic diseases as pollutants seep into the water supply undetected.

I wish to explore the development of a microbial testing kit that uses genetically engineered non-pathogenic microbes to detect metal pollutants such as mercury, cyanide, and arsenic associated with small-scale mining activities in Ghana. The bioengineered microbe should be housed in a sealed, single-use microfluidic cartridge that will generate a visible signal when pollutant concentrations exceed defined thresholds. This approach will be a low-cost, rapid, and field-deployable environmental monitoring tool that can support public health by preventing the use of contaminated water supply and aid remediation efforts by facilitating the tracking of pollutants without the direct release of bioengineered organisms in the environment.

Escherichia coli will serve as an ideal engineered biosensor for detecting mining pollutants because it can be genetically engineered to couple potent specific sensing elements with standardized reporter outputs. It has native regulatory systems responsive to mercury, cyanide, and arsenic that can be integrated with plug-and-play genetic circuits that convert toxin recognition into a visible or measurable signal, such as fluorescence or luminescence. Engineered E. coli biosensors have been successfully demonstrated for mercury using mer-regulated promoters, for arsenic using ars operon regulators, and for cyanide through redox- and respiration-linked sensing systems, highlighting their sensitivity, specificity, and applicability for environmental monitoring in contaminated water systems. Making it a practical platform for environmental monitoring in mining-impacted regions.

Governance & Policy Goals for Ethical Usage

I chose these policy goals due to the project being a contained diagnostic synthetic biology tool, not a system meant for environmental release. As such, the primary ethical risks center on contaiment, missue and social impact.

Policy Goals

1. Biological Containment and Preventing Harm.

• Prevent environmental release
• Prevent horizontal gene transfer
• Ensure post-use inactivation

2. Responsible Use and Misuse Prevention

• Restrict access to live biological material
• limit modification and replication
• Ensure appropriate interpretation of results

3. Environmental and Social Protection

• Avoid stigmatization or punitive misuse of data
• Support remediation and public health responses
• Protect vulnerable communities

4. Accessibility and Constructive Innovation

• Maintain affordability
• Avoid impeding legitimate research
• Encourage local adoption and trust

Governance Actions

Option 1. Build-In Dual Contatiment

Purpose

Currently, biosensors are often regulated based on organism release risk. This option shift goverance upsteam by embedding safety directly into design.

Design

• Physical containment (sealed cartridges, microfluidics)
• Genetic safeguards(auxtrophy, kill switches, chromosomal integration)
• Automation chemical inactivation after use.

Actors

• Academic researchers to aid in design.
• Biotechnology companies to facilitate manufacturing.
• Biosafety regulators such as the Environmental Protection Agency (EPA) and the National Biosafety Authority (NBA) for approval standards.
• Funders: biosafety enforcement through grants and investments.

Assumptions

• Containment systems remain reliable across conditions
• Kill switches remain evolutionarily stable.

Risks of Failure & Success

• Failure: Manufacturing defects or improper disposal
• Success risk: over-reliance on technical fixes leading to reduced oversight

Option 2. Device-Level Regulatory Certification

Purpose

More governance from organism-based oversight to diagnostic-device style regulation, similar to water quality strips or pregnancy kits.

Design

• Certification based on performance, containment, disposal, and shelf life.
• Independent validation studies
• Periodic recertification.

Actors

• National environmental agencies: defining acceptable detection thresholds
• Biosafety authorities: monitor post approval compliance and certify containment, inactivation, and disposal protocols.
• standards organizations: develop testing, labelling, and performance standards.
• Independent academic validators: conduct third-party performance and safety evaluations to provide credibility and transparency.

Assumptions

• Regulators have the capacity to evaluate synthetic biology devices.
• Certification increases public trust.

Risks of Failure & Success

• Failure: slow approval processes.
• Success risk: compliance cost excludes small innovators.

Option 3: Controlled Distribution and Stewardship

Purpose

Prevents misuse while ensuring ethical use.

Design

• Distribution through approved institutions such as the EPA, NGOs and Universities.
• Basic user training.
• Standardize results reporting templates.
• No access to cell or DNA.

Actors

• Local environmental agencies: distribute kits to approved users, aggregate and interpret monitoring data.
• NGOs and community organizations: act as community intermediaries, train users, and support ethical use.
• Universities and extension services: provide technical training and oversight, update protocols as science evolves, and support data quality and analysis.
• Local governments: coordinate response actions, ensure data is used for public health, not punishment, and set rules on who can deploy kits.

Assumptions

• Training reduces misuse
• Institutions act in the community’s interest.

Risk of Failure and Success

• Failure: informal redistribution
• Success risk: limited access in remote areas

Governance Scoring

Scores

• 1 = Most Effective
• 2 = Moderatly Effective
• 3 = Minimally Effective

Prioritization and Recommendation

I would prioritize a combined strategy of Option 1 (Dual containment) as a non-negotiable baseline, Option 2 (Device-level certification) for clarity and trust, and Option 3 (Controlled distribution) selectively in high-risk or sensitive contexts. This layered approach balances technical safety, regulatory clarity, and social responsibility. The primary trade-offs are increased development cost and reduced flexibility, but this is justified by a substantial reduction in ecological, ethical, and reputational risk.

Assignment Week 2 Lecture Prep

Homework Questions from Professor Jacobson

Question 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?

Question 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?

Answers

Question 1. The error rate of DNA polymerase is approximately 1 mistake for every 10⁶ added during DNA replication. The intrinsic 3’ to 5’ exonucleolytic proofreading activity of DNA polymerase removes the mismatch bases and lowers the replication error rate to about 10⁸ nucleotides. When this is combined with the post-replication mismatch repair mechanisms, the overall error rate is reduced to better than 1 in 10⁹ nucleotides. The human genome consists of approximately 3.1 to 3.2 billion base pairs (3×10^10) due to the combined accuracy of DNA polymerase, 3’ to 5’ exonucleolytic proofreading activity, and post-replication repair. The error rate compared to the human genome is less than one mistake per genome per cell division cycle. Biology deals with the discrepancy through a multilayered correction system that consists of polymerase accuracy, 3’ to 5’ exonucleolytic proofreading activity, post-replicational mismatch, and redundancy in DNA sequences, which prevent the massive number of errors that would occur otherwise.

Question 2. The genetic code consists of 4 nucleotide bases that code for 20 amino acids. mRNA reads nucleotides in triplets called codons, resulting in 64 possible codon combinations. The average human protein is composed of approximately 300 to 500 amino acids, and most amino acids are encoded by two to six different codons. There is a huge number of possible DNA sequences for any given protein approximatly X⁴⁵⁰ combinations, where X is the average number of codons per amino acid. In practice, however not all codon combinations are equally effective code for expression due to codon usage bias. Most cells do not have equal amounts of tRNAs for every codon and prefer optimal codons, which enhance translation efficiency and protein production. Some other reasons are

The use of suboptimal codons slows tranlastion leading to protein misfolding and

Homework Questions from Dr. LeProus

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

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

Question 3. Why can’t you make a 2000bp gene via direct oligo synthesis?

Answers

Question 1. The most commonly used method for oligo synthesis currently is solid-phase phosphoramidite chemistry. It was developed by Caruthurs in 1981 and has become the industry standard because it allows for easy automation, rapid, and cost-effective production of custom oligonucleotides of 150-200 nucleotides in length.

Question 2. It is difficult to make oligonucleotides longer than 200 nucleotides via direct chemical synthesis due to the cumulative effect of inefficiencies such as depurination, loss of yield, and accumulation of truncated sequences in each coupling step. By the 200 nucleotide, the fraction of full-length correct oligonucleotides becomes very low while truncated sequences and error-containing sequences increase, making further synthesis and purification increasingly difficult.

Question 3. You cannot make a 2000bp gene via direct oligo synthesis because it is not feasible due to the cumulative effect of increasing low yields and errors in phosphoramidite chemistry as the chain length of nucleotide increases. Even at 200 nucleotide purification is difficult, much less at 2000 nucleosides, where the high number of truncated sequences and low yields would make the purification process impractical and the error rate unacceptably high.

Homework Questions from George Church

Choose ONE of the following three questions to answer; and please cite AI prompts or paper citations used, if any.

Question 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”?

Question 2. [Given slides #2 & 4 (AA:NA and NA:NA codes)] What code would you suggest for AA:AA interactions?

Question 3. [(Advanced students)] Given the one paragraph abstracts for these real 2026 grant programs sketch a response to one of them or devise one of your own:

https://arpa-h.gov/explore-funding/programs/boss

https://www.darpa.mil/research/programs/smart-rbc

https://www.darpa.mil/research/programs/go

Answers

Question 1.

An amino acid is an organic molecule that consists of a basic amino group (-NH2), an acidic carboxyl group (-COOH), and an organic R group that is unique to each amino acid. They are organic compounds that serve as the fundamental building blocks of proteins, which are essential for repairing tissue, building muscle, and driving nearly all cellular functions.

Essential amino acids are amino acids that cannot be synthesized from scratch by organisms fast enough in sufficient quantities to supply their demands and must therefore be obtained from their diets. Essential amino acids are crucial for protein synthesis, tissue repair, and immune function. The 10 essential amino acids are Arginine, Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, and Valine. Nine of the essential amino acids are essential in humans, with the exception of Arginine, which is generally only essential in infants and many non-human species, particularly in strict carnivores such as felines, reptiles, avian, and some fish.

The lysine Contingency was a genetically engineered fail-safe performed by Dr. Henry Wu in Jurassic Park. The fail-safe was meant to knock out the ability of dinosaurs to produce the essential amino acid Lysine, forcing them to rely on synthetic supplements from the park’s staff. To ensure that, in the event of a dinosaur breakout, it would not survive long enough to damage global ecosystems. Based on my understanding of essential amino acids, using Lysine as a bioengineered fail-safe was not the right choice. Lysine is an integral part of the metabolic process; it is needed for collagen formation, calcium formation, and energy production, and might seem to be a good target for a failsafe mechanism. However, all known animals lack the ability to synthesize lysine in adequate amounts but derive it from their food sources, primarily plants. Thus, Dr. Wu and the other scientists at InGen (International Genetics Technologies) essentially broke a feature in the dinosaur genome that was already broken in nature, assuming dinosaurs could actually produce lysine in adequate amounts in the first place. In nature, herbivores obtain lysine from feeding on plants, and carnivores obtain it by feeding on other animals. The Lysine contingency essentially forced the dinosaurs into the food web; as such, any dinosaur that escaped the park could survive by just consuming their normal diet in the natural environment, which ironically is lysine-rich in nature.

Reference

Bechor, O., Smulski, D.R., Van Dyk, T.K., LaRossa, R.A. and Belkin, S., 2002. Recombinant microorganisms as environmental biosensors: pollutants detection by Escherichia coli bearing fabA′:: lux fusions. Journal of Biotechnology, 94(1), pp.125-132.

Beese, L.S., Derbyshire, V. and Steitz, T.A., 1993. Structure of DNA polymerase I Klenow fragment bound to duplex DNA. Science, 260(5106), pp.352-355.

Benserhir, Y., Salaün, A.C., Geneste, F., Pichon, L. and Jolivet-Gougeon, A., 2022. Recent Developments for the Detection of Escherichia Coli Biosensors Based on Nano-Objects—A Review. IEEE Sensors Journal, 22(10), pp.9177-9188.

Bilal, M. and Iqbal, H.M., 2019. Microbial-derived biosensors for monitoring environmental contaminants: Recent advances and future outlook. Process Safety and Environmental Protection, 124, pp.8-17.

Dieudonné, A., Prévéral, S. and Pignol, D., 2020. A sensitive magnetic arsenite-specific biosensor hosted in magnetotactic bacteria. Applied and Environmental Microbiology, 86(14), pp.e00803-20.

Cai, S., Shen, Y., Zou, Y., Sun, P., Wei, W., Zhao, J. and Zhang, C., 2018. Engineering highly sensitive whole-cell mercury biosensors based on positive feedback loops from quorum-sensing systems. Analyst, 143(3), pp.630-634.

Hou, Y. and Wu, G., 2018. Nutritionally essential amino acids. Advances in Nutrition, 9(6), pp.849-851.

Jurassic-Pedia (no date) Lysine Contingency (S/F). Available at: https://www.jurassic-pedia.com/lysine-contingency-sf/ (Accessed: 7 February 2026

Reddy, Michael K.. “amino acid”. Encyclopedia Britannica, 23 Jan. 2026, https://www.britannica.com/science/amino-acid. Accessed 8 February 2026.

Week 2 HW: DNA - READ, WRITE & EDIT

Homework

Part 0: Basics of Gel Electrophoresis

I have watched all the lecture slides and reciatation videos.

Part 1: Benchling & In-silico Gel Art

I created a benchling account and imported the Lambda DNA

Restriction Enzyme Digest Simulations

EcoRI Restriction Enzyme Digest Simulation.

HindIII Restriction Enzyme Digest Simlution.

BamHI Restriction Enzyme Digest Simulation.

KpnI Restriction Enzyme Digest Simulation.

EcoRV Restriction Enzyme Digest Simulation.

SacI Restriction Enzyme Digest Simulation.

SalI Restriction Enzyme Digest Simulation.

EcoRI, HindIII, BamHI, KpnI, EcoRV, SacI, SalI Simultanious Restriction Enzyme Digest.

I tried to create a design in Benchling, after many trials and errors, I managed to make a pattern by using double and triple digests of restriction enzymes.

I think it looks like a Y.

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 the recitation (NCBI, UniProt, Google), obtain the protein sequence for the protein you chose.

Answer

I picked the BMP1 protein (Bone morphogenetic protein 1). It is a secreted metalloprotease encoded by the BMP1 gene in humans. It belongs to the astacin M12A family of proteases and plays a central role in extracellular matrix assembly by cleaving precursor proteins into the mature functional forms. Growing up, I never had a bone fracture or dislocation, but my brother had a fracture in his left hand, which made me curious about the proteins and genes that drive bone formation. (https://www.uniprot.org/uniprotkb/P13497/entry)

The protein sequence for BMP1 protein on Uniprot is an isoform that has been chosen as the canonical sequence. The sequence is as follows: MPGVARLPLLLGLLLLPRPGRPLDLADYTYDLAEEDDSEPLNYKDPCKAAAFLGDIALDEEDLRAFQVQQAVDLRRHTARKSSIKAAVPGNTSTPSCQSTNGQPQRGACGRWRGRSRSRRAATSRPERVWPDGVIPFVIGGNFTGSQRAVFRQAMRHWEKHTCVTFLERTDEDSYIVFTYRPCGCCSYVGRRGGGPQAISIGKNCDKFGIVVHELGHVVGFWHEHTRPDRDRHVSIVRENIQPGQEYNFLKMEPQEVESLGETYDFDSIMHYARNTFSRGIFLDTIVPKYEVNGVKPPIGQRTRLSKGDIAQARKLYKCPACGETLQDSTGNFSSPEYPNGYSAHMHCVWRISVTPGEKIILNFTSLDLYRSRLCWYDYVEVRDGFWRKAPLRGRFCGSKLPEPIVSTDSRLWVEFRSSSNWVGKGFFAVYEAICGGDVKKDYGHIQSPNYPDDYRPSKVCIWRIQVSEGFHVGLTFQSFEIERHDSCAYDYLEVRDGHSESSTLIGRYCGYEKPDDIKSTSSRLWLKFVSDGSINKAGFAVNFFKEVDECSRPNRGGCEQRCLNTLGSYKCSCDPGYELAPDKRRCEAACGGFLTKLNGSITSPGWPKEYPPNKNCIWQLVAPTQYRISLQFDFFETEGNDVCKYDFVEVRSGLTADSKLHGKFCGSEKPEVITSQYNNMRVEFKSDNTVSKKGFKAHFFSDKDECSKDNGGCQQDCVNTFGSYECQCRSGFVLHDNKHDCKEAGCDHKVTSTSGTITSPNWPDKYPSKKECTWAISSTPGHRVKLTFMEMDIESQPECAYDHLEVFDGRDAKAPVLGRFCGSKKPEPVLATGSRMFLRFYSDNSVQRKGFQASHATECGGQVRADVKTKDLYSHAQFGDNNYPGGVDCEWVIVAEEGYGVELVFQTFEVEEETDCGYDYMELFDGYDSTAPRLGRYCGSGPPEEVYSAGDSVLVKFHSDDTITKKGFHLRYTSTKFQDTLHSRK

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.

Answer

I used https://www.bioinformatics.org/sms2/rev_trans.html to reverse transcribe the protein to its nucleotide sequence.
The nucleotide sequence for the Bone morphogenetic protein 1 is as follows:

atgccgggcgtggcgcgcctgccgctgctgctgggcctgctgctgctgccgcgcccgggc cgcccgctggatctggcggattatacctatgatctggcggaagaagatgatagcgaaccg ctgaactataaagatccgtgcaaagcggcggcgtttctgggcgatattgcgctggatgaa gaagatctgcgcgcgtttcaggtgcagcaggcggtggatctgcgccgccataccgcgcgc aaaagcagcattaaagcggcggtgccgggcaacaccagcaccccgagctgccagagcacc aacggccagccgcagcgcggcgcgtgcggccgctggcgcggccgcagccgcagccgccgc gcggcgaccagccgcccggaacgcgtgtggccggatggcgtgattccgtttgtgattggc ggcaactttaccggcagccagcgcgcggtgtttcgccaggcgatgcgccattgggaaaaa catacctgcgtgacctttctggaacgcaccgatgaagatagctatattgtgtttacctat cgcccgtgcggctgctgcagctatgtgggccgccgcggcggcggcccgcaggcgattagc attggcaaaaactgcgataaatttggcattgtggtgcatgaactgggccatgtggtgggc ttttggcatgaacatacccgcccggatcgcgatcgccatgtgagcattgtgcgcgaaaac attcagccgggccaggaatataactttctgaaaatggaaccgcaggaagtggaaagcctg ggcgaaacctatgattttgatagcattatgcattatgcgcgcaacacctttagccgcggc atttttctggataccattgtgccgaaatatgaagtgaacggcgtgaaaccgccgattggc cagcgcacccgcctgagcaaaggcgatattgcgcaggcgcgcaaactgtataaatgcccg gcgtgcggcgaaaccctgcaggatagcaccggcaactttagcagcccggaatatccgaac ggctatagcgcgcatatgcattgcgtgtggcgcattagcgtgaccccgggcgaaaaaatt attctgaactttaccagcctggatctgtatcgcagccgcctgtgctggtatgattatgtg gaagtgcgcgatggcttttggcgcaaagcgccgctgcgcggccgcttttgcggcagcaaa ctgccggaaccgattgtgagcaccgatagccgcctgtgggtggaatttcgcagcagcagc aactgggtgggcaaaggcttttttgcggtgtatgaagcgatttgcggcggcgatgtgaaa aaagattatggccatattcagagcccgaactatccggatgattatcgcccgagcaaagtg tgcatttggcgcattcaggtgagcgaaggctttcatgtgggcctgacctttcagagcttt gaaattgaacgccatgatagctgcgcgtatgattatctggaagtgcgcgatggccatagc gaaagcagcaccctgattggccgctattgcggctatgaaaaaccggatgatattaaaagc accagcagccgcctgtggctgaaatttgtgagcgatggcagcattaacaaagcgggcttt gcggtgaacttttttaaagaagtggatgaatgcagccgcccgaaccgcggcggctgcgaa cagcgctgcctgaacaccctgggcagctataaatgcagctgcgatccgggctatgaactg gcgccggataaacgccgctgcgaagcggcgtgcggcggctttctgaccaaactgaacggc agcattaccagcccgggctggccgaaagaatatccgccgaacaaaaactgcatttggcag ctggtggcgccgacccagtatcgcattagcctgcagtttgatttttttgaaaccgaaggc aacgatgtgtgcaaatatgattttgtggaagtgcgcagcggcctgaccgcggatagcaaa ctgcatggcaaattttgcggcagcgaaaaaccggaagtgattaccagccagtataacaac atgcgcgtggaatttaaaagcgataacaccgtgagcaaaaaaggctttaaagcgcatttt tttagcgataaagatgaatgcagcaaagataacggcggctgccagcaggattgcgtgaac acctttggcagctatgaatgccagtgccgcagcggctttgtgctgcatgataacaaacat gattgcaaagaagcgggctgcgatcataaagtgaccagcaccagcggcaccattaccagc ccgaactggccggataaatatccgagcaaaaaagaatgcacctgggcgattagcagcacc ccgggccatcgcgtgaaactgacctttatggaaatggatattgaaagccagccggaatgc gcgtatgatcatctggaagtgtttgatggccgcgatgcgaaagcgccggtgctgggccgc ttttgcggcagcaaaaaaccggaaccggtgctggcgaccggcagccgcatgtttctgcgc ttttatagcgataacagcgtgcagcgcaaaggctttcaggcgagccatgcgaccgaatgc ggcggccaggtgcgcgcggatgtgaaaaccaaagatctgtatagccatgcgcagtttggc gataacaactatccgggcggcgtggattgcgaatgggtgattgtggcggaagaaggctat ggcgtggaactggtgtttcagacctttgaagtggaagaagaaaccgattgcggctatgat tatatggaactgtttgatggctatgatagcaccgcgccgcgcctgggccgctattgcggc agcggcccgccggaagaagtgtatagcgcgggcgatagcgtgctggtgaaatttcatagc gatgataccattaccaaaaaaggctttcatctgcgctataccagcaccaaatttcaggat accctgcatagccgcaaa

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]

Answer

Codons need to be optimized for use due to the codon usage bias in the heterologous host organisms. The codon usage bias is due to variations in tRNA abundance in different organisms, which directly impacts translation speed and accuracy. When a gene from one organism is expressed in another, such as a human gene in bacteria, the mismatch in codon preference can cause ribosomes to stall at rare codons, leading to reduced protein yield, truncated proteins, or misfolding. Thus, codons are optimized to ensure the efficient expression of proteins in heterologous host organisms.

I optimized the Bone morphogenetic protein 1 for insertion into an E.coli plasmid using the VectorBuilder DNA Optimization tool (https://en.vectorbuilder.com/tool/codon-optimization.html).

The optimized Bone morphogenetic protein 1 nucleotide sequence for insertion into an Escherichia coli str. K-12 substr. MG1655 is as follows:

ATGCCGGGTGTTGCGCGCCTGCCGCTGCTGCTGGGCCTGCTGCTGCTGCCGCGTCCGGGCCGCCCGCTGGATCTGGCGGACTATACCTATGATCTGGCCGAAGAAGATGATAGCGAACCGCTGAACTATAAAGATCCGTGCAAAGCCGCGGCGTTTCTGGGCGATATTGCGCTGGATGAAGAAGATCTGCGCGCGTTCCAGGTGCAGCAGGCCGTGGATCTGCGCCGCCATACCGCGCGTAAAAGCAGCATTAAAGCGGCGGTCCCGGGCAACACCTCGACCCCGAGCTGCCAGAGCACCAATGGCCAGCCGCAGCGCGGTGCCTGCGGCCGCTGGCGCGGCCGCTCACGTAGCCGTCGTGCGGCCACCAGCCGCCCGGAACGTGTGTGGCCGGATGGCGTCATCCCGTTCGTGATTGGCGGCAATTTCACCGGCAGCCAGCGTGCCGTATTTCGCCAGGCGATGCGCCATTGGGAAAAACATACATGCGTGACCTTCCTGGAACGCACCGATGAAGATAGCTACATTGTGTTTACCTATCGCCCGTGCGGCTGCTGCAGCTATGTGGGCCGCCGTGGCGGCGGCCCGCAGGCGATTAGCATTGGCAAAAATTGCGACAAATTTGGTATTGTGGTGCATGAACTGGGCCATGTGGTGGGCTTTTGGCATGAACATACCCGCCCGGATCGTGATCGCCATGTTAGCATTGTGCGCGAAAACATTCAGCCGGGCCAGGAATATAATTTTCTGAAAATGGAGCCGCAGGAAGTGGAAAGCCTGGGCGAAACCTATGATTTCGATAGCATTATGCACTATGCGCGTAACACCTTCAGCCGCGGCATTTTCCTGGATACCATTGTACCGAAATACGAAGTCAATGGCGTAAAACCGCCGATTGGCCAGCGCACCCGCCTGAGCAAAGGAGATATTGCGCAGGCGCGTAAACTGTATAAATGCCCGGCGTGCGGCGAAACCCTGCAAGATAGCACCGGTAACTTCAGCAGCCCGGAATATCCGAATGGATATAGCGCCCATATGCACTGCGTGTGGCGCATTTCAGTTACCCCGGGCGAAAAAATTATTCTGAACTTTACCTCGCTGGATCTGTATCGCAGCCGCCTGTGCTGGTACGATTACGTTGAAGTGCGTGATGGCTTTTGGCGCAAAGCGCCGCTGCGCGGCCGCTTCTGTGGCAGCAAACTGCCGGAACCGATTGTCAGCACAGATAGCCGTCTGTGGGTGGAATTTCGCAGCTCAAGCAATTGGGTGGGCAAAGGCTTTTTCGCGGTATATGAAGCCATTTGCGGCGGTGATGTGAAAAAAGATTACGGCCACATTCAGAGCCCGAACTATCCGGATGATTACCGCCCGAGCAAAGTATGCATCTGGCGCATTCAGGTGAGCGAAGGTTTTCACGTGGGCCTGACCTTTCAGTCATTTGAAATTGAGCGCCATGATAGCTGTGCGTACGATTATCTGGAAGTGCGTGATGGTCATAGCGAAAGCTCAACCCTGATTGGCCGCTACTGCGGCTACGAAAAACCGGATGATATTAAAAGCACCAGCAGCCGTCTGTGGCTGAAATTTGTGAGCGATGGCAGCATTAACAAAGCGGGCTTCGCGGTTAATTTCTTCAAAGAAGTGGATGAATGCTCGCGCCCGAATCGCGGCGGCTGCGAACAGCGCTGTCTGAATACCCTGGGCAGCTATAAATGCAGCTGCGATCCGGGTTATGAACTGGCGCCGGATAAACGTCGCTGTGAAGCGGCGTGCGGCGGTTTTCTGACCAAACTGAATGGTAGCATTACGAGCCCGGGTTGGCCGAAAGAATATCCGCCGAACAAAAATTGCATCTGGCAGCTGGTGGCGCCGACCCAGTACCGCATTAGCCTGCAGTTTGATTTCTTTGAAACCGAAGGCAATGACGTCTGTAAATATGACTTCGTGGAAGTCCGCAGCGGCCTGACCGCGGATAGTAAACTGCACGGCAAATTCTGCGGCAGCGAAAAACCGGAAGTGATCACCAGCCAGTACAATAACATGCGCGTGGAATTCAAAAGCGACAACACCGTGAGCAAAAAAGGCTTTAAAGCACATTTTTTTAGCGATAAAGATGAATGTAGTAAAGATAACGGTGGCTGTCAGCAGGATTGCGTTAACACCTTTGGCAGCTACGAATGCCAGTGCCGCAGCGGTTTTGTGCTGCACGATAACAAACATGATTGTAAAGAAGCGGGTTGCGATCATAAAGTGACCAGCACCTCAGGCACCATTACCAGCCCGAACTGGCCGGATAAATATCCGAGCAAAAAAGAATGCACCTGGGCGATTAGCAGCACCCCGGGCCATCGTGTGAAACTGACCTTTATGGAAATGGATATTGAAAGCCAGCCGGAATGTGCGTACGACCATCTGGAAGTGTTTGATGGTCGCGATGCCAAAGCGCCGGTTCTGGGCCGTTTTTGCGGCAGCAAAAAACCGGAACCGGTCCTGGCGACGGGCAGCCGCATGTTCCTGCGGTTCTACAGCGATAACAGCGTGCAGCGTAAAGGTTTTCAGGCCAGCCATGCGACCGAATGCGGTGGCCAGGTGCGTGCCGATGTGAAAACCAAAGATCTGTACAGCCATGCGCAGTTTGGCGATAACAACTATCCGGGCGGCGTGGATTGCGAATGGGTGATCGTGGCGGAAGAAGGCTATGGCGTGGAACTTGTGTTTCAGACCTTTGAAGTGGAAGAAGAAACCGATTGTGGTTACGACTACATGGAACTGTTCGATGGCTATGACAGCACCGCCCCGCGCCTGGGCCGTTATTGCGGCAGCGGCCCGCCGGAAGAAGTGTACTCCGCCGGCGATAGCGTTCTGGTGAAGTTTCATAGCGATGATACCATTACCAAGAAAGGCTTTCACCTGCGCTATACCAGCACCAAATTTCAGGATACCCTGCACAGCCGCAAA

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.

Answer

In a cell-dependent system, the Bone morphogenetic protein 1 can be produced using recombinant plasmid cloning technology. This would work by inserting the DNA sequence coding for the BMP1 protein into a plasmid such as E.coli. The DNA sequence should be optimised for the chosen plasmid. The plasmid should have a promoter, start and stop codons, regulator sequences, and a terminator. The plasmid is then introduced into bacteria via transformation. Inside the bacteria, RNA polymerase will bind to the promoter and transcribe the DNA coding region in RNA. Which then binds to the ribosome and tRNA reads the codons and assembles amino acids and peptide chains fold into the BMP1 protein.

Part 4: Prepare a Twist DNA Synthesis Order

4.1 Create a twist account I created a twist account

I chose to build an insert sequence for the luciferase. The luciferase gene encodes an enzyme that catalyzes a bioluminescent reaction, producing light in the presence of its substrate (luciferin), ATP, and oxygen. It is widely used as a reporter gene to study gene expression.

I first started the build by using the T7 promoter and added the Shine-Dalgarno sequence as the ribosome binding site.

I added a start codon

I imported the luciferase gene from NCBI and tried to copy out the coding sequence for luciferase. I used Benchling to optimize the coding sequence for insertion into E.coli plasmid.

I then inserted the optimized luciferase coding sequence into the build, added a 6x his tag,a stop codon, and a T7 terminator

The structure of the insert was as follows:

• T7 Promoter
• RBS
• Start Codon
• Luciferase Coding sequence
• 6x His Tag
• Stop Codon
• T7 Terminator

Benchling link: https://benchling.com/s/seq-udkiPsJ6nw8LkBmPBwlf?m=slm-X0Qj5SONejIma2zXxUQ8

I uploaded the sequence into Twist and chose the pTwist Amp High Copy vector. I then downloaded the construct as GenBack and imported it into Benchling.

Benchling link: https://benchling.com/s/seq-kstnI8Jy48TISQPIJUoh?m=slm-d5lB8uOEDoqk2WyY47fS

Part 5: DNA Read/Write/Edit

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

Answer

I would like to sequence the whole genome and transcriptome of rice varieties grown in Ghana. I would focus on genes involved in nitrogen use efficiency, drought tolerance, and yield stability. Coming from an agricultural biotechnology background, sequencing rice DNA for nitrogen use efficiency will improve food security by reducing fertilizer dependency while maintaining yield levels. Additionally, nitrogen metabolism is tightly linked to drought stress and carbon metabolism, thus sequencing can reveal alleles that enhance resilience under variable rainfall.

(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?

Answer

I would use a hybrid sequencing strategy combining third-generation PacBio HiFi sequencing (developed by Pacific Biosciences) and second-generation Illumina sequencing (developed by Illumina) because each technology addresses different challenges of plant genomics. The genome of Oryza sativa contains many repetitive elements and duplicated gene families, which are difficult to assemble accurately using short reads alone. PacBio HiFi produces long, highly accurate reads that can span repetitive regions and resolve structural variants such as insertions, deletions, and gene duplications. This is especially important when studying nitrogen use efficiency genes, which may exist in multiple similar copies or be influenced by regulatory structural variation. Long-read sequencing therefore enables the creation of a high-quality de novo assembly of locally adapted rice varieties, ensuring that important region-specific alleles are not missed.

However, PacBio alone is not sufficient for large-scale comparative or expression studies. Illumina sequencing provides extremely high depth at a lower cost per base, making it ideal for population-level SNP discovery, genome polishing, and RNA sequencing. Since nitrogen use efficiency is strongly influenced by gene regulation, Illumina RNA-seq would allow precise quantification of gene expression under different nitrogen treatments. Combining long-read structural resolution with high-depth short-read accuracy ensures reliable variant detection, strong transcriptomic analysis, and cost efficiency. Together, this hybrid approach provides the comprehensive genomic insight needed to improve nitrogen use efficiency, enhance sustainable fertilizer management, and support precision breeding strategies in rice.

For PacBio HiFi sequencing, the input would be high-molecular-weight genomic DNA (15-25 kb fragments).

Library Preparation:

• Extract intact genomic DNA.
• Size selection.
• Ligate hairpin adapters to create circular SMRTbell templates.
• Polymerase binding (no amplification required).

Sequencing & Base Calling:

• DNA polymerase is immobilized in Zero-Mode Waveguides (ZMWs).
• Fluorescently labeled nucleotides are incorporated.
• Each base emits a distinct fluorescence signal.
• Circular consensus sequencing (multiple passes) improves accuracy to >99.9% (Wenger et al., 2019).

Output:

• Long high-fidelity reads (10–25 kb)
• FASTQ files with quality scores
• Ideal for resolving repetitive plant genome regions
• Long-read sequencing is especially important because plant genomes are repeat-rich and structurally complex (Michael & VanBuren, 2020).

For Illumina Sequencing, the input would be fragmented DNA of 300-500 bp or cDNA for RNA sequencing.

Library Preparation:

• DNA fragmentation
• End repair and adapter ligation
• PCR amplification
• Cluster generation via bridge amplification

Sequencing & Base Calling: -Sequencing-by-synthesis

• Reversible terminator nucleotides incorporated one at a time
• Fluorescent imaging determines base identity
• High per-base accuracy (>99%)

Output: Millions to billions of short reads (100–150 bp), which are ideal for gene expression quantification and polishing assemblies

5.2 DNA Write

I would design and synthesize a synthetic nitrogen-sensing genetic circuit that could be introduced into rice to improve nitrogen uptake and fertilizer responsiveness.

Instead of simply overexpressing a transporter gene (which can cause metabolic imbalance), I would engineer a smart, feedback-controlled genetic circuit that activates nitrogen uptake genes only under low-nitrogen conditions. The genetic circuit would consist of:

• A low-nitrogen inducible promoter.
• A synthetic transcriptional activator module.
• A nitrogen transporter gene.
• A fluorescent reporter for monitoring.
• A terminator sequence.

The core gene would be NRT2.1 (high-affinity nitrate transporter, which is involved in nitrate uptake under nitrogen-limited conditions.

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

Answer

To synthesize the nitrogen-responsive genetic circuit containing the NRT2.1 module, I would use phosphoramidite-based solid-phase DNA synthesis combined with enzymatic assembly methods such as Gibson Assembly.

In this approach, short DNA oligonucleotides are chemically synthesized base-by-base through iterative cycles of deprotection, nucleotide coupling, capping, and oxidation. Because individual oligos are typically limited to ~200 bp, overlapping fragments would then be assembled into the full-length construct using enzymatic assembly in a single reaction using Gibson assembly.

This method is precise, scalable, and well-suited for modular plant genetic circuit design. However, limitations include length constraints requiring multi-fragment assembly, potential synthesis errors that accumulate with longer sequences, and challenges with high-GC or repetitive regions. Therefore, the final construct would require sequence verification to ensure accuracy before plant transformation.

5.3 DNA Edit

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

Beyond plants, I would be very interested in editing the genome of Aedes aegypti, the mosquito species that transmits diseases such as dengue, Zika, and yellow fever. The goal would be to reduce the transmission of these viruses through gene drives or other targeted genome editing strategies. Specifically, I would target genes involved in fertility or pathogen susceptibility, such as those encoding reproductive proteins or viral receptor proteins in the mosquito midgut. For example, disrupting a key fertility gene could reduce mosquito population density, while modifying viral receptor genes could make mosquitoes resistant to virus infection, breaking the disease transmission cycle.

My rationale for editing Aedes aegypti is both public health and environmental impact. Vector-borne diseases affect millions worldwide, especially in tropical regions, and current control methods (insecticides, habitat elimination) are often insufficient, costly, or ecologically harmful. Gene editing offers a precise, sustainable solution that can complement traditional control strategies.

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

For this, I would use CRISPR-Cas9-based gene drives, which allow a targeted gene to be copied preferentially to offspring, ensuring rapid spread of the desired trait through wild populations. CRISPR-Cas9, which is currently the most precise and widely used genome editing technology for insects and other organisms. CRISPR-Cas9 enables targeted modifications of DNA by creating double-strand breaks at specific genomic locations, which are then repaired by the cell’s own repair machinery, allowing for insertions, deletions, or gene replacement. This technology is ideal for engineering traits such as reduced fertility or virus resistance in mosquitoes.

However, careful containment, ecological risk assessment, and ethical considerations would be critical because of the potential for irreversible effects in wild populations.

How CRISPR-Cas9 Edits DNA

• Targeting – A single-guide RNA is designed to complement a specific DNA sequence in the mosquito genome, adjacent to a protospacer adjacent motif.
• Cutting – Cas9 endonuclease binds the sgRNA and introduces a double-strand break at the targeted site.
• Repair – The mosquito cell repairs the break via:
• Non-Homologous End Joining → introduces small insertions/deletions (indels), which can disrupt gene function.
• Homology-Directed Repair → if a DNA template is provided, precise sequence changes can be introduced, e.g., inserting a virus-resistance allele.

Preparation and Inputs

Before editing, careful design and preparation are required:

Design steps

• Identify the target genes critical for fertility or viral susceptibility.
• Design sgRNAs that minimize off-target effects using computational tools.
• Design donor DNA templates if precise sequence insertion is needed.

Inputs

• sgRNA – synthesized guide RNA targeting the mosquito gene.
• Cas9 protein or Cas9-expressing plasmid/mRNA.
• Donor DNA template.
• Embryos or cultured mosquito cells – Aedes aegypti embryos are typically microinjected with these components.

The delivery will be a microinjection of CRISPR components into fertilized mosquito eggs, which is standard for germline editing, ensuring heritable changes.

CRISPR-Cas9 has some limitations, which are editing efficiency can be low because not all injected embryos survive, and only a fraction carries the intended mutation.

The precision of the edits is also variable: when DNA breaks are repaired through non-homologous end joining, unpredictable insertions or deletions can occur, and homology-directed repair is often inefficient in embryos. Off-target effects are another concern, as the guide RNA may bind unintended genomic sites, causing unwanted mutations.

Additionally, while CRISPR-based gene drives can spread edited traits through populations, they require careful ecological risk assessment to avoid unintended consequences, and scaling up edits for population-level interventions demands extensive breeding and monitoring. Despite these challenges, CRISPR remains the most practical and validated method for achieving heritable and targeted genetic modifications in mosquitoes.

References

Michael, T.P. & VanBuren, R., 2020. Building near-complete plant genomes. Current Opinion in Plant Biology, 54, pp.26–33.

Thomsen, H.C. et al., 2014. Glutamine synthetase: role in nitrogen metabolism and crop productivity. Frontiers in Plant Science, 5, p.465. The sequence of sequencers: The history of sequencing DNA https://pmc.ncbi.nlm.nih.gov/articles/PMC4727787/

Oxford Nanopore Technologies – Official Documentation https://nanoporetech.com](https://nanoporetech.com/

Wang, W. et al., 2018. Genetic variation in ARE1 mediates grain yield by modulating nitrogen utilization in rice. Nature Communications, 9, p.735.

Wenger, A.M. et al., 2019. Accurate circular consensus long-read sequencing improves variant detection and genome assembly. Nature Biotechnology, 37, pp.1155–1162.

Xu, G. et al., 2012. Plant nitrogen assimilation and use efficiency. Annual Review of Plant Biology, 63, pp.153–182.

I also used Chat-gtp to guide me in the steps, and design procedures used in read , write and edit DNA questions.

Week 3 HW: Lab Automation

Python Script for Opentrons Artwork

This has been the most interesting and somewhat challenging assignment so far. I chose make an artistic design based on the adrinkra symbols. The adinkra symbols are a set of visual symbols from Ghana, created by the Akan people to represent philosophical concepts, historical events, and social proverbs.

I picked the Nsaa symbol, which is a type of woven cloth renowned for its quality. It is the symbol of excellence, genuineness, and authenticity.

I used the GUI at opentrons-art.rcdonovan.com.to generate an artistic design for the Nsaa symbol.

https://opentrons-art.rcdonovan.com/?id=i96nm69hbxsi9ck

I then used the design coordinates from the Opentrons Automation Art Interface to write the code in Google Colab.

Writing the code to ensure that the colors were dispensed at the correct coordinates and there was no cross-contamination of pipette tips was very tricky. I wrote my first attempt and used the Gemini 2.5 flash in Google Colab to optimize and debug errors in the code. When writing the code, I noticed that the color wells available were red, green, and orange, so I used green and red for the design.

link to Google Colab:

https://colab.research.google.com/drive/1jWiojebcoIdJ1J4sFfEVNB5z-NEdmHvg#scrollTo=pczDLwsq64mk&line=1&uniqifier=1

Visualization of the code

This table is the documentation of how I used Google Genimi 2.5 flash in Google Colab to help debug my code.

I also created other designs using the GUI :

https://opentrons-art.rcdonovan.com/?id=20719mxv010c1y8

https://opentrons-art.rcdonovan.com/?id=k2p8012s471hljv

Post-Lab Questions

Question 1: Revolutionizing sample preparation: a novel autonomous microfluidic platform for serial dilution

The paper I chose is titled “Revolutionizing sample preparation: a novel autonomous microfluidic platform for serial dilution” by Dries Vloemans et al. The paper presented a novel, standalone, and fully automated microfluidic platform for the stepwise preparation of serial dilutions without the need for any active elements.

Dilution is a standard fluid operation that is widely employed in the sample preparation of many biochemical assays. It serves multiple essential functions, such as sample mixing with certain reagents at specific dilution ratios, reducing sample matrix effects, and bringing target analytes within the linear assay detection range, among many others.

Traditionally, dilution relies either on manual pipetting, which is labor-intensive and prone to human error, or automated laboratory liquid handling systems, which are bulky, expensive, and unsuitable for point-of-care use. The goal of the authors was to develop a passive, self-contained microfluidic platform that could execute serial dilution in a controlled, programmable, and reproducible manner.

The key findings of the paper include demonstrating that the proposed automated microfluidic platform can perform precise and reproducible serial dilutions without pumps or active control systems. The hydrophobic burst valves reliably metered out defined liquid volumes, enabling accurate dilution ratios such as 2X, 5X, and 10X. It also showed that effective mixing could be achieved through the incorporation of sequential expansion chambers. Which were geometrically optimized to promote passive mixing as fluids pass through them, eliminating the need for mechanical agitation. Additionally, it demonstrated the platform’s compatibility with relevant biological fluids like blood and the integration of a capillary-driven SIMPLE pumping mechanism to allow the device to operate in a fully self-powered manner and complete dilution sequences within short time frames after user activation.

Fig. 1 a) Conceptual design of the dilution module illustrating the 3 microfluidic units that are used for plug metering, merging and mixing, and the positions of the different valving elements (single-coated (sc) and double-coated (dc) HBVs, and hydrophobic barrier (HB)). b) Configuration and working principle of the different valves with their respective theoretical burst pressure profiles. The sc HBV contains a hydrophobic coating at the bottom channel wall, while the dc HBV is treated hydrophobically at both the top and bottom walls, resulting in varying burst pressures. The HB comprises a hydrophobic-treated filter paper, which allows air passage but forms a physical barrier for the liquid, hence, inducing a very high burst pressure. c) Conceptual exploded view of the integrated microfluidic device for autonomous multistep serial dilution, illustrating the top ‘dilution’ and bottom ‘pumping’ layer. The top dilution layer comprises 3 serially coupled dilution modules (5× DF), connected with a connection hole to the bottom pumping layer, holding the prefilled working liquid and wedge-shaped filter paper (Whatman grade 598) of the SIMPLE pump unit.

Fig. 2 a) Snapshots of the different liquid manipulations within a dilution module (DF = 5×) illustrating the working principle. (i and ii) The coordinated burst action of HBVs with different burst strengths is used to first isolate a precisely metered sample liquid (2 μL, blue), after which the excess is removed to the storage channel. (iii and iv) The metered sample liquid is next merged with a prefilled diluent (8 μL, yellow), after which (v and vi) the combined plug is sent through a sequence of expansion chambers in which it is mixed into a homogeneous solution. b) Detailed schematics of plug merging, and working principle of the microfluidic air bridge (top). Illustration of failed downstream plug manipulation when no blocking channel is used due to air intake via the microfluidic air bridge (bottom). c) Close-up of the expansion chambers, illustrating the three ongoing principles that are used within the mixing process: increase of diffusion interface, parabolic flow profile, and lateral plug distribution. Dashed and full arrows indicate air and liquid flow, respectively.

Question 2: What I intend to do with automation tools for my final project

Using my first idea, which involves developing a biosensor kit for the detection of illegal mining pollutants. The automation tools used would be a combination of Python-based liquid handling, 3D-printed assay holders, and could-based design tool like Google Nebula.

Here is a rough idea of the automation tools I might end up using:

• Using Opentrons OT-2 to dispense growth media and mix microbial cultures with chemical regents.
• Using PLateLoc to seal the plate.
• Using XPeel to remove the seal after incubation.
• Measuring fluorescence and color intensity using PHERAstar plate reader.
• Using Ginkgo Nebula to design synthetic genetic circuits for microbial biosensors and simulate sensor response behaviors.

Final Project Ideas

I have submitted my final project ideas in the slide deck that was provided for committed listeners

Reference

https://opentrons-art.rcdonovan.com/?id=i96nm69hbxsi9ck

https://colab.research.google.com/drive/1jWiojebcoIdJ1J4sFfEVNB5z-NEdmHvg#scrollTo=pczDLwsq64mk&line=1&uniqifier=1

Vloemans, D., Pieters, A., Dal Dosso, F. and Lammertyn, J., 2024. Revolutionizing sample preparation: a novel autonomous microfluidic platform for serial dilution. Lab on a Chip, 24(10), pp.2791-2801.

Week 4 HW: Protein Design - I

Part A. Conceptual Questions

Question 1. How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)

Answer

A dalton is a unit of mass used to express the mass of atoms, molecules, and other subatomic particles.

The percentage of protein is

Question 2. Why do humans eat beef but do not become a cow, eat fish but do not become fish?

Answer

This is due to digestion breaking down food such as meat and fish into basic molecules like amino acids, fatty acids, and sugars. The DNA present in Food is therefore broken down and rendered nonfunctional; as such, it is not directly incorporated into our genetic structure.

Question 3. Why are there only 20 natural amino acids?

Answer

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project