Week 1 HW: Principles and Practices

I am interested in developing an engineered probiotic system designed to release the lactase enzyme on demand in the human gut for individuals with lactose intolerance. This project is entirely in silico, combining concepts from synthetic biology, microbiome modeling, and systems biology without any wet-lab implementation.

The system would simulate a probiotic chassis such as Lactobacillus or Bifidobacterium, equipped with virtual genetic circuits inspired by lactose metabolism. These circuits would model regulatory control of lactase expression based on local lactose concentration, using logic-gate–like behavior and feedback mechanisms. Enzyme production would increase when lactose is present and decrease once lactose is depleted, allowing adaptive and resource-efficient regulation.

Because this project represents an early, in-silico design phase, its governance goals focus on the responsible framing, communication, and interpretation of computational results rather than regulation of a finalized biological product.

From my perspective, scenario-based risk modeling can be prioritized over the other governance options, because all three approaches address public health and safety either directly or indirectly. Scenario-based analysis explicitly explores what could go wrong if an in-silico model were physically implemented, making it the most direct mechanism for anticipating risks to gut microbiome balance or unintended metabolic effects. However, maintaining scientific integrity also plays a critical indirect role in protecting public health: by avoiding overclaiming the safety or effectiveness of a purely conceptual model, the transition from simulation to real-world application becomes more cautious, accurate, and oriented toward appropriate experimental validation, thereby reducing the likelihood of harmful misinterpretations. Similarly, ensuring ethical transparency through clear and accurate documentation of modeling assumptions, parameters, and limitations improves how the model is interpreted and reused by others, helping prevent incorrect applications that could ultimately pose health risks.

Sources:

• Emerging biotechnologies: Technology, choice and the public good. (n.d.). Nuffield Council on Bioethics. Retrieved February 9, 2026, from https://www.nuffieldbioethics.org/publication/emerging-biotechnologies-technology-choice-and-the-public-good/
• Gingold-Belfer, R., Levy, S., Layfer, O., Pakanaev, L., Niv, Y., Dickman, R., & Perets, T. T. (2020). Use of a Novel Probiotic Formulation to Alleviate Lactose Intolerance Symptoms-a Pilot Study. Probiotics and Antimicrobial Proteins, 12(1), 112–118. https://doi.org/10.1007/s12602-018-9507-7
• iGEM Responsibility. (n.d.). Retrieved February 9, 2026, from https://responsibility.igem.org/
• Khalil, A. S., & Collins, J. J. (2010). Synthetic biology: Applications come of age. Nature Reviews Genetics, 11(5), 367–379. https://doi.org/10.1038/nrg2775
• Lactose Intolerance—NIDDK. (2024, January 30). National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/digestive-diseases/lactose-intolerance

Assignment (Week 2 Lecture Prep):

Homework Questions from Professor Jacobson:

1. Error rate and genome context • From the slide N°= 8 , DNA polymerase has an error rate of ~1 in 10⁶ bases. • With the human genome of ~3 × 10⁹ bp, this would result in ~3,000 errors per replication without repair. • Biology reduces this discrepancy with proofreading activity of DNA polymerase (3′→5′ exonuclease) and post-replication mismatch repair like MutS, NER, BER…, which collectively reduce the final error rate to ~1 in 10⁹–10¹⁰.
2. Human protein: ~1036 bp (~345 amino acids), With ~3 codons per amino acid on average, the number of possible DNA sequences for an average human protein is ~3³⁴⁵ (~10¹⁶⁴ possible sequences). Not all sequences work in practice because of Mutations: Insertions, deletions, transitions, and transversions that can introduce frameshifts or premature stop codons, making the protein non-functional. Also, there are some mechanism of regulations that make some Sequences creating unwanted secondary structures in mRNA, affect splicing, or introduce cryptic signals that disrupt translation.

Homework Questions from Dr. LeProust:

1. Most commonly used method for oligo synthesis Today, almost all synthetic DNA is made using phosphoramidite solid-phase synthesis. This method adds one nucleotide at a time on a solid support and is reliable, efficient, and easy to automate, which is why it became the standard for modern DNA synthesizers. https://biolabmix.ru/en/info/detail/oligonucleotide-synthesis/#:~:text=The%20most%20common%20approach%20to,for%20example%2C%20by%20attaching%20fluorophores.

2. Why it’s hard to make oligos longer than ~200 nt Each step in chemical DNA synthesis is very efficient but not perfect, so small errors happen every time a base is added. As the oligo gets longer, these errors pile up, and beyond about 200 nucleotides it becomes very difficult to get a clean, full-length sequence. https://pubs.rsc.org/en/content/articlepdf/2025/sc/d4sc06958g

3. Why you can’t directly synthesize a 2000 bp gene Making a 2000-base gene in one piece would accumulate too many chemical errors and damaged bases to be useful. Instead, companies synthesize short oligos and then assemble them enzymatically, followed by cloning and sequence checking to make sure the gene is correct. https://www.pnas.org/doi/10.1073/pnas.2237126100#:~:text=The%20broader%20implications%20of%20the,without%20multiple%20repair/selection%20steps.

Homework Question from George Church:

All animals require the same 10 essential amino acids because they cannot synthesize them and must obtain them from their diet. These are: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arginine (arginine is essential for all animals and conditionally essential in adult humans). The “lysine contingency” refers to the idea that lysine is often the limiting essential amino acid in plant-based diets, especially those dominated by cereals like wheat, rice, or maize. Since animals cannot make lysine, their growth and health are directly constrained by how much lysine is available in their food. So knowing that all animals share the same essential amino acid requirements makes lysine’s importance stand out even more. It shows that lysine is not just nutritionally important but evolutionarily critical.

https://www.kemin.com/ap/en/blog/animal/amino-acids-for-animal-health#:~:text=Essential%20amino%20acids:%20These%20are,essential)%2C%20leucine%20and%20lysine

Week 2 HW: DNA Read, Write, and Edit

• In this part, I imported The complete 48,502 bp linear genome of bacteriophage lambda from NCBI GenBank into Benchling. This sequence corresponds to the Lambda DNA sold by NEB (N3011) and will be used for in-silico restriction digestion.

• Then simulated restriction enzyme digestion using EcoRI, HindIII, BamHI, KpnI, EcoRV, SacI, and SalI. By running in-silico gel electrophoresis . The resulting virtual gel shows discrete bands corresponding to these fragments, which demostrates how sequence information maps to physical separation in gel electrophoresis.

• To create a pattern in the style of Paul Vanouse’s work, I experimented with different combinations of restriction enzymes to control the gel band patterns. By adjusting the number and length of the resulting DNA fragments, I explored how these parameters influence the final visual outcome. Through this process, I ultimately obtained a gel pattern resembling a butterfly shape.

• This helped me understand how restriction digests and gels work before doing any real lab experiment. I treated this as both a technical exercise and a creative exploration, inspired by DNA gel art concepts.

• For the DNA design challenge, I chose a protein related to my project interest in engineered probiotics and conditional enzyme release in the gut.The enzyme β-galactosidase is well-characterized and commonly expressed in Escherichia coli, making it an ideal candidate for computational DNA design and expression modeling.
• I first searched online database UniProt to obtain the amino acid sequence of the protein.

• the amino acid equence was as follow:

>sp|P00722|BGAL_ECOLI Beta-galactosidase OS=Escherichia coli (strain K12) OX=83333 GN=lacZ PE=1 SV=2
MTMITDSLAVVLQRRDWENPGVTQLNRLAAHPPFASWRNSEEARTDRPSQQLRSLNGEWR
FAWFPAPEAVPESWLECDLPEADTVVVPSNWQMHGYDAPIYTNVTYPITVNPPFVPTENP
TGCYSLTFNVDESWLQEGQTRIIFDGVNSAFHLWCNGRWVGYGQDSRLPSEFDLSAFLRA
GENRLAVMVLRWSDGSYLEDQDMWRMSGIFRDVSLLHKPTTQISDFHVATRFNDDFSRAV
LEAEVQMCGELRDYLRVTVSLWQGETQVASGTAPFGGEIIDERGGYADRVTLRLNVENPK
LWSAEIPNLYRAVVELHTADGTLIEAEACDVGFREVRIENGLLLLNGKPLLIRGVNRHEH
HPLHGQVMDEQTMVQDILLMKQNNFNAVRCSHYPNHPLWYTLCDRYGLYVVDEANIETHG
MVPMNRLTDDPRWLPAMSERVTRMVQRDRNHPSVIIWSLGNESGHGANHDALYRWIKSVD
PSRPVQYEGGGADTTATDIICPMYARVDEDQPFPAVPKWSIKKWLSLPGETRPLILCEYA
HAMGNSLGGFAKYWQAFRQYPRLQGGFVWDWVDQSLIKYDENGNPWSAYGGDFGDTPNDR
QFCMNGLVFADRTPHPALTEAKHQQQFFQFRLSGQTIEVTSEYLFRHSDNELLHWMVALD
GKPLASGEVPLDVAPQGKQLIELPELPQPESAGQLWLTVRVVQPNATAWSEAGHISAWQQ
WRLAENLSVTLPAASHAIPHLTTSEMDFCIELGNKRWQFNRQSGFLSQMWIGDKKQLLTP
LRDQFTRAPLDNDIGVSEATRIDPNAWVERWKAAGHYQAEAALLQCTADTLADAVLITTA
HAWQHQGKTLFISRKTYRIDGSGQMAITVDVEVASDTPHPARIGLNCQLAQVAERVNWLG
LGPQENYPDRLTAACFDRWDLPLSDMYTPYVFPSENGLRCGTRELNYGPHQWRGDFQFNI
SRYSQQQLMETSHRHLLHAEEGTWLNIDGFHMGIGGDDSWSPSVSAEFQLSAGRYHYQLV
WCQK

• After selecting the protein, I converted the amino acid sequence of β-galactosidase (1024 residues) into the corresponding DNA sequence using the Sequence Manipulation Suite Reverse Translate tool. Because the genetic code is degenerate, multiple codons can encode the same amino acid. The resulting 3072 bp DNA sequence represents one valid nucleotide sequence capable of encoding the β-galactosidase protein.

• the resulted DNA sequence was as follow:

>reverse translation of sp|P00722|BGAL_ECOLI Beta-galactosidase OS=Escherichia coli (strain K12) OX=83333 GN=lacZ PE=1 SV=2 to a 3072 base sequence of most likely codons.
atgaccatgattaccgatagcctggcggtggtgctgcagcgccgcgattgggaaaacccg
ggcgtgacccagctgaaccgcctggcggcgcatccgccgtttgcgagctggcgcaacagc
gaagaagcgcgcaccgatcgcccgagccagcagctgcgcagcctgaacggcgaatggcgc
tttgcgtggtttccggcgccggaagcggtgccggaaagctggctggaatgcgatctgccg
gaagcggataccgtggtggtgccgagcaactggcagatgcatggctatgatgcgccgatt
tataccaacgtgacctatccgattaccgtgaacccgccgtttgtgccgaccgaaaacccg
accggctgctatagcctgacctttaacgtggatgaaagctggctgcaggaaggccagacc
cgcattatttttgatggcgtgaacagcgcgtttcatctgtggtgcaacggccgctgggtg
ggctatggccaggatagccgcctgccgagcgaatttgatctgagcgcgtttctgcgcgcg
ggcgaaaaccgcctggcggtgatggtgctgcgctggagcgatggcagctatctggaagat
caggatatgtggcgcatgagcggcatttttcgcgatgtgagcctgctgcataaaccgacc
acccagattagcgattttcatgtggcgacccgctttaacgatgattttagccgcgcggtg
ctggaagcggaagtgcagatgtgcggcgaactgcgcgattatctgcgcgtgaccgtgagc
ctgtggcagggcgaaacccaggtggcgagcggcaccgcgccgtttggcggcgaaattatt
gatgaacgcggcggctatgcggatcgcgtgaccctgcgcctgaacgtggaaaacccgaaa
ctgtggagcgcggaaattccgaacctgtatcgcgcggtggtggaactgcataccgcggat
ggcaccctgattgaagcggaagcgtgcgatgtgggctttcgcgaagtgcgcattgaaaac
ggcctgctgctgctgaacggcaaaccgctgctgattcgcggcgtgaaccgccatgaacat
catccgctgcatggccaggtgatggatgaacagaccatggtgcaggatattctgctgatg
aaacagaacaactttaacgcggtgcgctgcagccattatccgaaccatccgctgtggtat
accctgtgcgatcgctatggcctgtatgtggtggatgaagcgaacattgaaacccatggc
atggtgccgatgaaccgcctgaccgatgatccgcgctggctgccggcgatgagcgaacgc
gtgacccgcatggtgcagcgcgatcgcaaccatccgagcgtgattatttggagcctgggc
aacgaaagcggccatggcgcgaaccatgatgcgctgtatcgctggattaaaagcgtggat
ccgagccgcccggtgcagtatgaaggcggcggcgcggataccaccgcgaccgatattatt
tgcccgatgtatgcgcgcgtggatgaagatcagccgtttccggcggtgccgaaatggagc
attaaaaaatggctgagcctgccgggcgaaacccgcccgctgattctgtgcgaatatgcg
catgcgatgggcaacagcctgggcggctttgcgaaatattggcaggcgtttcgccagtat
ccgcgcctgcagggcggctttgtgtgggattgggtggatcagagcctgattaaatatgat
gaaaacggcaacccgtggagcgcgtatggcggcgattttggcgataccccgaacgatcgc
cagttttgcatgaacggcctggtgtttgcggatcgcaccccgcatccggcgctgaccgaa
gcgaaacatcagcagcagttttttcagtttcgcctgagcggccagaccattgaagtgacc
agcgaatatctgtttcgccatagcgataacgaactgctgcattggatggtggcgctggat
ggcaaaccgctggcgagcggcgaagtgccgctggatgtggcgccgcagggcaaacagctg
attgaactgccggaactgccgcagccggaaagcgcgggccagctgtggctgaccgtgcgc
gtggtgcagccgaacgcgaccgcgtggagcgaagcgggccatattagcgcgtggcagcag
tggcgcctggcggaaaacctgagcgtgaccctgccggcggcgagccatgcgattccgcat
ctgaccaccagcgaaatggatttttgcattgaactgggcaacaaacgctggcagtttaac
cgccagagcggctttctgagccagatgtggattggcgataaaaaacagctgctgaccccg
ctgcgcgatcagtttacccgcgcgccgctggataacgatattggcgtgagcgaagcgacc
cgcattgatccgaacgcgtgggtggaacgctggaaagcggcgggccattatcaggcggaa
gcggcgctgctgcagtgcaccgcggataccctggcggatgcggtgctgattaccaccgcg
catgcgtggcagcatcagggcaaaaccctgtttattagccgcaaaacctatcgcattgat
ggcagcggccagatggcgattaccgtggatgtggaagtggcgagcgataccccgcatccg
gcgcgcattggcctgaactgccagctggcgcaggtggcggaacgcgtgaactggctgggc
ctgggcccgcaggaaaactatccggatcgcctgaccgcggcgtgctttgatcgctgggat
ctgccgctgagcgatatgtataccccgtatgtgtttccgagcgaaaacggcctgcgctgc
ggcacccgcgaactgaactatggcccgcatcagtggcgcggcgattttcagtttaacatt
agccgctatagccagcagcagctgatggaaaccagccatcgccatctgctgcatgcggaa
gaaggcacctggctgaacattgatggctttcatatgggcattggcggcgatgatagctgg
agcccgagcgtgagcgcggaatttcagctgagcgcgggccgctatcattatcagctggtg
tggtgccagaaa

• After reverse translation, I verified the identity of the resulting nucleotide sequence by performing a BLASTn search against the reference lacZ gene from Escherichia coli K-12. The alignment showed 100% query coverage with an E-value of 0.0, confirming a highly significant match. The percent identity was ~84%, which is expected because reverse translation produces a synonymous DNA sequence that differs at the codon level while still encoding the same β-galactosidase protein. This result confirmed that the reverse-translated sequence correctly corresponds to the lacZ gene.

Next, I performed codon optimization of the sequence originates from E. coli K-12 to improve expression efficiency in a Lactobacillus probiotic strain (delbrueckii subsp. Bulgaricus), as this organism is the intended chassis for conditional lactase expression in the human gut, to ensure efficient translation in the final probiotic host organism. Codon optimization was performed using a host-specific algorithm using the Vector Builder codon orimisation tool that adjusts synonymous codon usage to match the preferred codons of L. delbrueckii while preserving the original amino acid sequence.

• the resulted optimised sequence is as following:

Host organism: Lactobacillus delbruekii susbsp. Bulgaricus ATCC 11842 = JCM 1002 Original Sequence: GC=59.80%, CAI=0.72 Optimized Sequence: GC=60.16%, CAI=0.89

Improved DNA[1]: GC=60.16%, CAI=0.89
ATGACTATGATCACCGACAGCCTGGCAGTTGTTTTGCAACGGCGGGACTGGGAAAACCCGGGCGTCACTCAGTTGAACCGGCTGGCCGCCCACCCACCATTTGCCAGCTGGCGCAACTCCGAAGAAGCCCGGACCGACCGGCCGAGCCAGCAACTGAGAAGCTTGAACGGCGAATGGCGTTTCGCCTGGTTTCCGGCCCCGGAAGCCGTCCCAGAAAGCTGGTTGGAATGCGACCTCCCGGAAGCCGATACCGTCGTGGTGCCGAGCAACTGGCAAATGCACGGCTATGACGCCCCCATCTACACCAATGTTACCTACCCAATTACCGTCAACCCGCCATTTGTCCCGACCGAAAACCCGACTGGTTGCTATAGCTTGACCTTCAACGTTGACGAAAGCTGGCTGCAAGAAGGCCAGACCCGCATTATTTTTGACGGCGTTAACAGCGCCTTCCACTTGTGGTGCAACGGCCGCTGGGTCGGCTACGGCCAGGACAGCCGCTTGCCATCCGAATTTGACCTGAGTGCTTTCTTGCGGGCCGGCGAAAACCGTCTGGCCGTCATGGTCCTGCGCTGGAGCGACGGCAGCTACCTGGAAGACCAAGACATGTGGCGGATGTCCGGCATTTTCCGGGACGTCAGCCTGCTGCACAAGCCGACCACCCAGATTTCCGACTTTCACGTTGCAACCCGGTTCAACGACGACTTCTCTCGGGCTGTGCTGGAAGCTGAAGTCCAGATGTGCGGCGAATTGCGGGACTACCTGCGGGTTACTGTTTCATTGTGGCAGGGCGAAACCCAGGTTGCCTCAGGCACCGCCCCGTTTGGCGGTGAAATTATCGACGAACGCGGCGGGTACGCCGACCGGGTTACCTTGAGACTGAACGTGGAAAACCCGAAGTTGTGGAGCGCCGAAATCCCAAATCTGTACCGCGCCGTCGTCGAATTGCACACCGCTGACGGCACCCTGATCGAAGCCGAAGCCTGCGACGTTGGCTTCCGGGAAGTCCGCATCGAAAACGGCTTGCTGCTCCTGAACGGCAAGCCACTGCTGATCCGGGGCGTTAACCGGCACGAACACCACCCATTGCACGGCCAAGTCATGGACGAACAGACTATGGTCCAGGACATCCTGCTGATGAAGCAGAACAACTTCAACGCTGTTCGTTGCTCACACTATCCAAACCATCCACTGTGGTACACTCTGTGCGACCGGTACGGCCTGTACGTTGTGGACGAAGCCAACATCGAAACTCACGGCATGGTTCCGATGAACCGGCTGACCGACGACCCGAGATGGCTGCCAGCCATGAGCGAACGGGTTACTCGCATGGTTCAACGCGACCGGAACCACCCATCCGTTATTATCTGGAGCCTGGGGAACGAAAGCGGCCACGGCGCCAATCACGACGCTCTGTACCGGTGGATCAAGTCCGTCGACCCATCCCGCCCTGTTCAGTACGAAGGCGGCGGCGCCGATACGACCGCCACCGACATCATCTGCCCAATGTACGCCCGGGTTGATGAAGACCAGCCGTTTCCGGCTGTCCCAAAGTGGAGCATCAAGAAGTGGCTGAGCCTGCCAGGCGAAACTCGGCCGCTGATCCTGTGCGAATACGCCCACGCCATGGGCAACTCCCTGGGCGGCTTTGCCAAGTACTGGCAGGCTTTTCGCCAGTATCCACGGTTGCAGGGCGGCTTTGTTTGGGACTGGGTCGACCAAAGCCTGATCAAGTACGACGAAAACGGCAACCCGTGGAGCGCCTACGGCGGCGACTTTGGCGACACCCCGAACGACCGCCAGTTTTGCATGAACGGTCTGGTTTTCGCTGACCGGACGCCACACCCGGCCCTGACCGAAGCCAAGCACCAGCAGCAGTTCTTCCAGTTCCGGCTGTCAGGCCAGACCATCGAAGTGACTAGCGAATACCTGTTTCGCCACTCCGACAACGAATTGTTGCACTGGATGGTCGCCCTGGACGGCAAGCCACTGGCCAGCGGCGAAGTTCCGCTGGACGTTGCCCCACAGGGCAAGCAGCTGATCGAATTGCCGGAACTGCCGCAGCCGGAAAGCGCCGGCCAACTGTGGCTGACTGTTCGGGTCGTTCAGCCGAACGCCACTGCCTGGTCTGAAGCCGGGCACATCTCAGCCTGGCAGCAGTGGCGCCTGGCCGAAAACTTGAGCGTTACGCTGCCGGCCGCCAGCCACGCCATCCCACACCTGACTACTAGCGAAATGGACTTTTGCATCGAATTGGGCAACAAGCGGTGGCAATTCAACCGGCAGAGCGGCTTTCTGAGCCAGATGTGGATCGGCGACAAGAAGCAGTTGCTGACCCCACTGCGGGATCAGTTCACCCGGGCCCCGCTGGACAACGACATCGGCGTCAGCGAAGCCACTCGGATCGACCCAAACGCCTGGGTCGAACGCTGGAAGGCCGCCGGCCACTACCAGGCCGAAGCCGCTCTGCTGCAATGTACCGCTGATACGCTGGCTGACGCCGTCTTGATTACTACCGCTCACGCCTGGCAACACCAGGGCAAGACTTTGTTTATCAGCCGGAAGACCTACCGGATTGACGGCAGCGGTCAGATGGCCATCACAGTCGATGTCGAAGTTGCCAGCGACACCCCGCACCCGGCACGGATCGGCCTGAACTGCCAGCTGGCCCAGGTTGCCGAACGGGTTAACTGGCTGGGCCTGGGCCCTCAGGAAAACTACCCAGACCGTTTGACGGCTGCCTGCTTTGACCGGTGGGACTTACCGTTGAGCGATATGTACACTCCATACGTCTTTCCGTCCGAAAACGGCCTGCGGTGCGGCACCAGAGAACTGAACTATGGCCCGCACCAGTGGCGCGGTGACTTTCAATTCAACATCAGCCGGTACTCCCAGCAGCAGTTGATGGAAACCAGCCACCGCCACCTGCTGCACGCCGAAGAAGGGACGTGGTTGAACATCGACGGCTTTCACATGGGCATCGGCGGCGACGACTCATGGAGCCCGAGCGTTAGCGCTGAATTCCAGTTGAGCGCCGGCCGGTACCACTACCAGTTGGTTTGGTGCCAGAAG

• To produce the protein from this DNA sequence, I would use a cell-dependent expression system based on bacterial transformation and expression. In this approach, This gene is then placed into an expression cassette with the necessary regulatory elements so it can be used by a biological system.
• To produce the protein, I would use a cell-dependent expression system through bacterial cloning. The designed DNA sequence is inserted into a plasmid and introduced into a bacterial host by transformation. Inside the cell, the gene is transcribed into mRNA under the control of the selected promoter. The mRNA is then translated by ribosomes, which read the codons starting at the start codon and assemble the corresponding amino acids into the lactase protein. This approach follows the natural flow of genetic information (DNA to RNA to protein) and allows controlled production of the enzyme in living cells.

• A lactose-inducible promoter was selected to enable conditional expression of lactase in response to lactose availability in the gut. The PlacA promoter region was extracted from the Lactococcus lactis lac operon upstream of the native ribosome binding site, with preserving lactose-responsive regulation.

AATCGTCGTTTTTTGTTCATATGAAGACTTTCTTTCATAAAGTAATTTTTTTCCAAAGATAATTCTCTTT
TAATTGTATCATAAAAGATAATATTTTCAAGGTAAAACAAACAATTTCAAACAAAAACAAACGTTAGATG
ATGAAATAAGAACAGAGGATTGACGTATATTAGCTTAGGTCAGATTTTGTATAAGACGAAAATAAAGTAG
GACCTCTTAATCAGTAAGTTATAGAAAGTAAAAGACTTTTGTAATACCTGAATAGATATTTCACGTCCAT
TTTGTGATGGATTAAATGAACAAAAATGAACAATAATTTAACGGTGTTATCTATTTTTTAAAAAAACAAA
TAAAAAAAAACAAAAAATTAACAAAAATAGTTGCGTTTTGTTTGAATGTTTGATATCATATAAACAAAGA
AATGATGAAAACGTTATCTTGAACATTTTGCAAAATATTTTCTACTTCTACGTAGCATTTCTTTTTAAAA
TTTAGGAGGTAGTCCAA

• For the RBS, I chose to keep the native Lactococcus lactis ribosome binding site (RBS) derived from the lacA operon which is the region immediately upstream of the coding sequence (CDS) and preserved its original spacer length to ensure efficient translation initiation in the probiotic host. Maintaining native RBS spacing is critical in Gram-positive bacteria, as ribosome binding and translation efficiency are highly sensitive to the distance between the Shine–Dalgarno sequence and the start codon.

• the RBS sequence is as follow:

AGGAGGTAGTCCAA

• I selected the transcription terminator from the tpi gene of Lactococcus lactis, a highly expressed native housekeeping gene, to ensure efficient and reliable transcription termination in the probiotic host. While two related annotations are present in GenBank for this region, both correspond to the same rho-independent transcription terminator. Therefore, I chose the complete annotated terminator region (positions 958–988), which includes both the inverted repeat and the downstream poly-T tract, to ensure proper formation of the termination hairpin and robust termination of transcription.

• A transcription terminator was included downstream of the lactase coding sequence to ensure proper termination of transcription. This prevents transcriptional read-through into adjacent sequences and improves the stability and predictability of gene expression, independent of promoter regulation.

• ATG used as start codon and AAG as stop codon

• From the selected elements, I built a linear expression cassette in Benchling containing a lactose-regulated promoter, native LAB ribosome binding site, codon-optimized lacZ, and a native transcription terminator. I exported this sequence as a FASTA file. Cassette_link_to_Benchling

• When I first uploaded my expression cassette FASTA file to Twist Bioscience, I encountered an initial error related to the FASTA header name. The header exceeded the maximum allowed length (32 characters), which caused the sequence to be rejected. I fixed this issue by shortening the header name and re-uploading the file. After this correction, the sequence was accepted for further analysis.

However, after re-uploading the corrected file, additional synthesis warnings appeared. These warnings were related to large GC content variation, repetitive regions, and overall sequence complexity. These issues are mainly due to the codon-optimized lacZ gene and the presence of multiple regulatory elements such as the ribosome binding site and transcription terminator. Twist flagged these features as potential manufacturability risks. Unfortunately, I was not able to resolve these additional issues at this stage. Fixing them would have required re-optimizing the enzyme sequence, possibly changing the host organism for codon optimization, and redesigning the regulatory architecture of the cassette. Due to time constraints and because this assignment focuses on learning the design and ordering workflow rather than producing a synthesis-ready construct, I chose not to redesign the sequence further.

For this exercise, I proceeded by selecting a Twist clonal vector (pTwist Amp High Copy) to complete the plasmid design. Although the insert sequence still contained manufacturability warnings. However, In a real DNA synthesis order, additional sequence optimization would be required to reduce GC content extremes and repetitive regions to meet synthesis constraints.

DNA read:

• I would want to sequence DNA used for digital data storage. In my knowledge, this technology enables the storage of digital information such as text, images, or files by encoding them into DNA sequences instead of being stored on hard drives. DNA is extremely stable and can store a huge amount of information in a very small space, which makes it interesting for long-term data storage. Reading this DNA by sequencing is necessary to retrieve the stored information and check that the data has not been damaged or changed over time.
• For this porpose, I would use Illumina sequencing because it is very accurate and well suited for reading short DNA fragments, which is how DNA data storage is usually organized. this strategy can be performed following 4 crusial steps: Image_adress

1. Generation This method is a second-generation sequencing technology. It sequences millions of short DNA fragments in parallel, which makes it fast and reliable, but it cannot read very long DNA molecules in one piece.

2. Input and preparation The input is DNA that contains the encoded digital data. To prepare it: The DNA is fragmented into short pieces, Adapters are added to both ends of the fragments, The fragments are amplified using PCR, The prepared DNA is loaded onto a flow cell

3. How the technology reads DNA (base calling) Each DNA fragment is copied one base at a time using fluorescently labeled nucleotides. A camera records the color added at each step, and the machine translates these signals into DNA letters (A, T, C, G).

4. Output The output is a large number of short DNA sequence reads saved as digital files. These reads are then assembled and decoded to recover the original stored data.

DNA write:

• I am particularly interested in the genes in human genomic DNA related to pharmacogenomics and pharmacogenetics. These fields study how genetic variation affects how people respond to drugs. So, I would want to synthesize genes encoding drug-metabolizing enzymes, like human cytochrome P450 enzymes. Since, these genes are central to pharmacogenetics as variations in them strongly influence how drugs are processed in the body. Synthesizing these genes allows them to be studied, expressed, and tested in controlled systems.

• So in order to synthetizing them , I would use chemical DNA synthesis combined with gene assembly, which is the standard approach used by commercial DNA synthesis companies.

1. Essential steps

• DNA synthesis starts with the digital design of the DNA sequence. This is followed by the chemical synthesis of short oligonucleotides, which are then assembled into full-length genes (for example, using Gibson Assembly). The synthesized genes are cloned into plasmids and finally sequence-verified to confirm their accuracy before use.

• This DNA synthesis method is easy to use and works well for many projects.However, it can sometimes make mistakes during the process. Parts of DNA that have lots of G and C letters or repeated sequences are harder to make. Very long DNA pieces also need to be built from many shorter fragments, which can be tricky and may cause errors.

DNA Edit:

• I would want to edit DNA in human cell lines used for drug testing, focusing on genes that affect how drugs work. Changing these genes helps researchers see how different genetic variants influence drug effects and side effects, which is useful in pharmacogenomics.

• The modification can be realised by CRISPR for editing because it allows precise and programmable changes to DNA. this stratigy works by using a guide RNA to find a specific DNA sequence. The Cas enzyme then makes a cut or nick, and the cell repairs it, introducing the change we want.

• To use CRISPR, you need to design guide RNAs, prepare the CRISPR components (DNA, RNA, or protein), deliver them into cells, and then check which cells were correctly edited.

• However, there are some limitations, like different editing efficiencies depending on cell type, and ethical or regulatory concerns when working with human cells.

Sources:

• Ahmad, E., Mahapatra, V., M, V. V., & Nagaraja, V. (2022). Intrinsic and Rho-dependent termination cooperate for efficient transcription termination at 3’ untranslated regions (p. 2022.07.21.500918). bioRxiv. https://doi.org/10.1101/2022.07.21.500918
• Amin, A. A., Olama, Z. A., & Ali, S. M. (2023). Characterization of an isolated lactase enzyme produced by Bacillus licheniformis ALSZ2 as a potential pharmaceutical supplement for lactose intolerance. Frontiers in Microbiology, 14, 1180463. https://doi.org/10.3389/fmicb.2023.1180463 Bioinformatic Tools | VectorBuilder. (n.d.). Retrieved February 17, 2026, from https://en.vectorbuilder.com/tool/overview.html
• Coenen, T. M. M., Bertens, A. M. C., de Hoog, S. C. M., & Verspeek-Rip, C. M. (2000). Safety evaluation of a lactase enzyme preparation derived from Kluyveromyces lactis. Food and Chemical Toxicology, 38(8), 671–677. https://doi.org/10.1016/S0278-6915(00)00053-3
• De Jesus, L. C. L., Aburjaile, F. F., Sousa, T. D. J., Felice, A. G., Soares, S. D. C., Alcantara, L. C. J., & Azevedo, V. A. D. C. (2022). Genomic Characterization of Lactobacillus delbrueckii Strains with Probiotics Properties. Frontiers in Bioinformatics, 2, 912795. https://doi.org/10.3389/fbinf.2022.912795
• de Vrese, M., Stegelmann, A., Richter, B., Fenselau, S., Laue, C., & Schrezenmeir, J. (2001). Probiotics—Compensation for lactase insufficiency123. The American Journal of Clinical Nutrition, 73(2), 421s–429s. https://doi.org/10.1093/ajcn/73.2.421s
• How can I find the promoter sequence of a gene on NCBI? (n.d.). ResearchGate. Retrieved February 17, 2026, from https://www.researchgate.net/post/How_can_I_find_the_promoter_sequence_of_a_gene_on_NCBI
• Lactase—An overview | ScienceDirect Topics. (n.d.). Retrieved February 17, 2026, from https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/lactase
• Reverse Translate. (n.d.). Retrieved February 17, 2026, from https://www.bioinformatics.org/sms2/rev_trans.html
• Saqib, S., Akram, A., Halim, S. A., & Tassaduq, R. (2017). Sources of β-galactosidase and its applications in food industry. 3 Biotech, 7(1), 79. https://doi.org/10.1007/s13205-017-0645-5

Week 3 HW: Lab Automation

I created two different agar art designs using two Arabic calligraphy styles. For the first design, I used a simple calligraphy style and created it directly using Python scripting in a Google Colab notebook. For the second design, I used the Opentrons Automation Art interface to design the calligraphy and obtain the coordinates.

I used the Google Gemini AI tool in Colab to understand the logic of the example Opentrons scripts provided in the lab. It helped me understand how coordinates, loops, and pipetting commands work. I also used Gemini AI to help identify and correct mistakes in my Python script, such as indentation errors. I reviewed the suggestions and edited the final code myself.

1. Featured Article: Automated Assembly of Programmable RNA-Based Sensors

The research aimed to solve the challenge of rapidly designing and building large libraries of RNA sensors that can “sense” specific viral RNA signatures. These sensors are crucial for diagnostic applications and understanding RNA-protein interactions. The authors focused on the biological validation of these sensors in both in vivo (bacteria) and cell-free systems.

They used the following lab automation:

• Hardware: Hamilton Microlab STAR liquid-handling workstation.
• Software: Custom Python scripts integrated with the liquid handler’s control software to manage complex plate layouts and reaction conditions.

The researchers used the automated system as a tool to facilitate:

• High-Throughput Plasmid Assembly: The authors needed to construct 144 unique plasmids encoding different riboregulator designs. Doing this manually would be prone to pipetting errors and extremely time-consuming.
• Library Preparation: Automation was used to prepare DNA libraries and reaction mixes for cell-free protein synthesis assays, ensuring consistent reagent volumes across hundreds of samples.
• Normalization and Dilution: The Hamilton system handled the precise normalization of DNA concentrations across plates, which is critical for accurate comparative screening of sensor performance.

The study successfully identified several high-performing RNA sensors capable of detecting viral targets. The use of automation allowed the team to scale their construction phase by nearly 10-fold compared to manual workflows, enabling them to test a much wider range of biological designs than previously possible. For understanding the content of this artical and which type of Lab automation the authors used in their research , i used the AI tool “SCISPACE”.

2. Final project Lab Automation:

My final project focuses on developing an in silico model of a lactose-responsive probiotic that produces lactase only when lactose is present. The physical implementation of this model would allow laboratory automation to verify its predicted results through experimental tests. A liquid-handling robot such as Opentrons could be used to prepare a multi-well plate containing a gradient of lactose concentrations. The robot would then inoculate each well with the engineered probiotic strain, and perform timed sampling to measure lactase activity or reporter output. The automated workflow enables scientists to perform systematic and repeatable tests on lactose responses of the genetic circuit. This helps them match their experimental results with their computer-based model. The project currently exists as a computational project which will use automation as a future extension of the project which does not require automation for its current research activities.

1st Idea: In-Silico Model of an Engineered Probiotic Producing Lactase in Response to Lactose

• Problematic:

Many people cannot digest lactose because they lack enough lactase in their intestine. A possible solution is to use engineered probiotics that produce lactase only when lactose is present. Before building such probiotics in the laboratory, it is important to understand how the genetic system would behave. So, without computational modeling, designing these systems requires trial-and-error experiments that are slow and expensive. There is a need for a simple computational model that can predict how a lactose-responsive genetic circuit would control lactase production over time. Image_ref

• Objectives:

The project is based on a lactose-responsive genetic cassette, who’s dynamic behavior is modeled as a genetic circuit in silico. The objectives of this project are:

1. –> To build an in-silico model of a lactose-responsive genetic circuit.
2. –> To simulate how lactose stimulate lactase production.
3. –> To study how changing key parameters affects lactose degradation.
4. –> To explore system behavior completely in silico.

• Project Description:

The project develops a purely computational model of an engineered probiotic strain. The model is based on a lactose-responsive genetic cassette, whose dynamic behavior is represented in silico as a genetic circuit: – a lacA promoter, operator, and native RBS from Lactococcus lactis for lactose sensing and regulation, – the lacZ gene from Escherichia coli K-12, encoding β-galactosidase (lactase).

I used an AI tool (ChatGPT) to guide me about the repression mechanism I should use, and its response was as follows: To ensure realistic behavior in the model, the lacA promoter includes a native operator, normally repressed by a LacR-like protein in Lactococcus lactis. In the simulation, a repression term is included to prevent unnecessary accumulation of lacZ (lactase) when lactose is absent. The model simulates how the presence of lactose activates the promoter, leading to lactase production, and how this enzyme then degrades lactose over time. No DNA construction or wet-lab experiments are performed. All behavior is represented mathematically and simulated using a computer.

• Steps to Achieve the Project:

1. Define simplified biological assumptions (single strain, constant environment).
2. Represent lactose as the input signal.
3. Model promoter activation based on lactose concentration.
4. Model lactase production and degradation over time.
5. Model lactose degradation by lactase.
6. Run simulations to observe system behavior.
7. Change parameters to study different scenarios.

• Limitations:

1. The model does not include other gut microbes.
2. The gut environment is assumed constant.
3. Results are predictive, not experimentally validated.

2nd Idea:Engineering an E. coli Reporter Strain to Monitor Protein Aging During Heterologous Expression Using a Fluorescent Timer Protein

• Problematic:

Escherichia coli BL21(DE3) is one of the most widely used hosts for heterologous protein expression in research and biotechnology. Although protein expression levels can be easily measured, there are very limited tools to determine how long the expressed protein molecules have persisted inside the cell. During prolonged induction, proteins may accumulate, age, misfold, or lose functionality, even when expression appears successful. Most current methods detect protein quality only after purification, making optimization of expression conditions slow and inefficient. So, there is a need for a genetically encoded reporter system that can estimate protein aging in living cells during expression. Image_ref

• Objectives:

This project is based on a fluorescent timer protein–based reporter system integrated into a heterologous protein expression strain. The objectives are:

1. –> To engineer a reporter strain capable of estimating protein age in vivo.
2. –> To use a fluorescent timer protein to distinguish newly synthesized and older proteins.
3. –> To monitor protein aging during prolonged heterologous expression.
4. –> To provide a practical tool for optimizing protein expression conditions.

• Project Description:

The project focuses on the genetic engineering of a protein expression strain of E. coli BL21(DE3). The reporter system is based on a genetic fusion between:

• a protein of interest (POI) expressed under the T7 promoter, and
• a fluorescent timer protein whose emission spectrum changes over time after synthesis. The genetic construct consists of:
• a T7 promoter and ribosome binding site,
• the gene encoding the protein of interest,
• a flexible linker sequence,
• the fluorescent timer protein gene,
• a transcriptional terminator. Image_ref

After induction, newly synthesized POI–timer fusion proteins initially emit one fluorescent signal. As time progresses, the timer protein matures and shifts to a second fluorescent signal. The ratio of the two fluorescence signals provides an estimate of the age distribution of the expressed protein population.

I used AI tool (ChatGPT) version to refine questions related to the necessary genetic elements required for T7-based heterologous expression in Escherichia coli BL21(DE3) and to determine the appropriate placement of a fluorescent timer gene for monitoring the age of the expressed protein.

• Steps to Achieve the Project:

1. Select a heterologous protein suitable for expression in E. coli.
2. Design a genetic fusion between the protein of interest and a fluorescent timer protein.
3. Clone the fusion construct under a T7 promoter into an expression plasmid.
4. Transform the plasmid into E. coli BL21(DE3).
5. Induce protein expression using IPTG.
6. Monitor fluorescence signals over time using appropriate excitation/emission settings.
7. Calculate fluorescence signal ratios to estimate protein aging.
8. Compare protein aging under different induction times and expression conditions.

• Limitations:

1. Fusion of the timer protein may affect protein folding or function.
2. Protein damage mechanisms are not directly measured.

3rd Idea:Engineering Houseplants for Atmospheric Carbon Monoxide (CO) Capture

• Problematic:

Carbon monoxide (CO) is a toxic gas produced by cars, heaters, and incomplete combustion. It is dangerous for humans, especially in indoor environments. Current solutions such as CO detectors can detect the gas but cannot remove it. Some bacteria naturally use CO as an energy source and convert it into carbon dioxide (CO₂). However, common houseplants cannot metabolize CO. If plants could be engineered to convert CO into CO₂, they could act as natural biological air filters. Image_ref

• Objectives:

The objectives of this project are:

1. –> To engineer a houseplant capable of converting carbon monoxide into carbon dioxide.
2. –> To use microbial genes that naturally perform CO oxidation.
3. –> To ensure the system works safely in oxygen-rich (indoor) environments.
4. –> To allow the produced CO₂ to be reused by the plant’s normal photosynthesis.
5. –> To design a genetically stable and safe indoor plant system.

• Project Description:

This project engineers a plant to express a bacterial enzyme called carbon monoxide dehydrogenase (CODH). This enzyme converts carbon monoxide (CO) into carbon dioxide (CO₂). The CO₂ produced by this reaction is not wasted. Instead, it enters the plant’s natural photosynthetic pathway (Calvin cycle), where it can be fixed into sugars. The plant therefore detoxifies CO while continuing its normal metabolism. The system is designed to work only when CO is present, to avoid unnecessary energy use.

• Genetic Elements for construct design:

1. CO Oxidation Enzymes

The core of the system is the carbon monoxide dehydrogenase (CODH) enzyme, which is responsible for converting carbon monoxide (CO) into carbon dioxide (CO₂). This enzyme is composed of three subunits encoded by the genes coxL, coxM, and coxS. The coxL gene encodes the large catalytic subunit, coxM encodes a subunit involved in electron transfer, and coxS encodes a structural subunit that stabilizes the enzyme complex. These genes originate from Oligotropha carboxidovorans, a bacterium that can oxidize CO in the presence of oxygen, making it suitable for expression in plant cells.

2. Promoter (Gene Expression Control)

To drive the expression of the CODH genes in plant cells, the CaMV 35S promoter is used. This promoter originates from the Cauliflower mosaic virus and is one of the most widely used promoters in plant biotechnology. It enables strong and constitutive gene expression across many plant tissues and is well characterized, making it a reliable choice for this project.

3. Subcellular Targeting Signal

A chloroplast transit peptide is included to ensure that the CODH proteins are transported into the chloroplast after synthesis. This targeting signal is derived from the small subunit of the plant enzyme Rubisco, which naturally localizes to the chloroplast. By directing the CODH enzymes to the chloroplast, the CO₂ produced from CO oxidation is generated close to the photosynthetic machinery, allowing it to be efficiently reused by the plant during photosynthesis.

5. Transcription Terminator

The NOS terminator is used to ensure proper termination of transcription and stable gene expression. This terminator originates from Agrobacterium tumefaciens and is commonly used in plant genetic constructs. Its function is to signal the end of transcription, improving mRNA stability and ensuring reliable expression of the introduced genes.

• Steps to Achieve the Project:

1. Select CO-oxidation genes from aerobic bacteria.
2. Adapt bacterial gene sequences for plant expression (codon optimization).
3. Design a plant expression construct containing:

• Plant promoter
• CODH genes
• Chloroplast targeting signal
• Transcription terminator Image_ref

4. Introduce the construct into the plant genome.
5. Confirm expression of CODH proteins in plant cells.
6. Evaluate CO removal and plant health in controlled conditions.
7. Assess whether produced CO₂ supports normal photosynthesis.

• Limitations:

1. Plant genetic engineering is slow and complex.
2. CO uptake by plants may be limited.
3. CO metabolism efficiency may be low.

Sources:

• Bidwell, R., & Bebee, G. (2011). Carbon monoxide fixation by plants. Canadian Journal of Botany, 52, 1841–1847. https://doi.org/10.1139/b74-236 Buckley, S. (2024, September 12). Bioengineered Plants Offer Superior Indoor Air Purification. Sustainable Brands. https://sustainablebrands.com/read/bioengineered-plants-indoor-air-purification
• DesignWorksGarage. (n.d.). NCHH. NCHH. Retrieved February 24, 2026, from https://nchh.org/information-and-evidence/learn-about-healthy-housing/health-hazards-prevention-and-solutions/carbon-monoxide/
• Hartl, F. U., Bracher, A., & Hayer-Hartl, M. (2011). Molecular chaperones in protein folding and proteostasis. Nature, 475(7356), 324–332. https://doi.org/10.1038/nature10317
• Heiss, S., Hörmann, A., Tauer, C., Sonnleitner, M., Egger, E., Grabherr, R., & Heinl, S. (2016). Evaluation of novel inducible promoter/repressor systems for recombinant protein expression in Lactobacillus plantarum. Microbial Cell Factories, 15(1), 50. https://doi.org/10.1186/s12934-016-0448-0
• Orina, F., Amukoye, E., Bowyer, C., Chakaya, J., Das, D., Devereux, G., Dobson, R., Dragosits, U., Gray, C., Kiplimo, R., Lesosky, M., Loh, M., Meme, H., Mortimer, K., Ndombi, A., Pearson, C., Price, H., Twigg, M., West, S., & Semple, S. (2024). Household carbon monoxide (CO) concentrations in a large African city: An unquantified public health burden? Environmental Pollution, 351, 124054. https://doi.org/10.1016/j.envpol.2024.124054
• Promoter and terminator considerations for gene integration into the genome of E.coli? (n.d.).ResearchGate. Retrieved February 24, 2026, from https://www.researchgate.net/post/Promoter_and_terminator_considerations_for_gene_integration_into_the_genome_of_Ecoli
• Robson, J. M., Arevalos, N. R., & Green, A. A. (2025). Automated Assembly of Programmable RNA-Based Sensors. bioRxiv, 2025.08.12.669972. https://doi.org/10.1101/2025.08.12.669972

week 04 hw: protein design-part-I

1. Amino Acid Count in 500g Meat: Meat is roughly 20% protein by mass. (Human Nutrition - Protein, Vitamins, Minerals | Britannica, n.d.)
   • 500g meat x 0.20 = 100g protein.
   • Using an average mass of 100 Daltons (Da) per amino acid: 100g / 100 Daltons (or g/mol) = 1 moles of amino acids
   • 1x 6.022 x 10^{23 = 6.022 x 10}23 molecules /1 mole.
2. Why we don’t become cows: When we eat protein, our digestive system breaks it down into individual amino acids. Our body then uses its own DNA information to reassemble those amino acids into human proteins. The information which is coded by the sequence of AA is destroyed, but the building blocks or AA are reused.
3. Why only 20 amino acids: In nature, the use of 20 amino acids is often explained as a “frozen accident” that originated in the early RNA World. This set worked well very early in Earth’s history and then became fixed. These 20 amino acids were good enough to build strong and functional proteins. Even though many other amino acids exist, this small group provides enough variety to perform many functions while remaining simple, stable, and efficient for cells to use. (Doig, 2017)
4. Non-natural amino acids: Yes, scientists can make non-natural (unnatural) amino acids. They do this using chemical methods and special genetic tools that allow new amino acids to be added to proteins. These new amino acids can give proteins new properties that natural amino acids do not have. (Young & Schultz, 2010) For example, A new amino acid could be made by taking a normal amino acid, like alanine, and adding a fluorine atom to its side chain. This fluorinated amino acid would make proteins more stable and less likely to break down, which is useful for drug design. (Adhikari et al., n.d.)
5. Pre-life origins of amino acids: According to Gutiérrez-Preciado, Romero, and Peimbert (2010) Before enzymes and living organisms existed, amino acids were probably formed naturally on early Earth. Energy from lightning, UV light, and volcanic heat helped simple gases react to make amino acids. Some amino acids were also brought to Earth by meteorites and comets. Together, these processes created a “primordial soup” of basic organic molecules. (Amino Acids, Evolution | Learn Science at Scitable, n.d.)
6. D-amino acid α-helix: In nature, L-amino acids form right-handed helices. If you used only D-amino acids, the stereochemistry would be mirrored, resulting in a left-handed $\alpha$-helix. (Zotti et al., n.d.)
7. Additional helices: Yes, additional helical structures besides the standard α-helix can be found in proteins. Studies show that other types of helices occur in many proteins, but they are often overlooked or mistaken for small distortions in α-helices. These helices are especially common in membrane proteins and are found in a significant number of known protein structures.(Vieira-Pires & Morais-Cabral, 2010)
8. Why right-handed helices: because this shape is the most stable for the natural building blocks of life. L-amino acids and D-sugars fit together best in a right-handed twist, which allows strong hydrogen bonds and reduces crowding between atoms. Left-handed helices are usually less stable or hard to form. (Right-Handed Alpha-Helix - an Overview | ScienceDirect Topics, n.d.)
9. β -sheet aggregation: β-sheets tend to aggregate because their edges have exposed hydrogen-bonding groups that easily stick to other β-strands. The main driving forces are hydrogen bonding between strands and the hydrophobic effect, which together make the stacked β-sheet structure very stable and allow fibrils to form.(Gsponer & Vendruscolo, 2006)

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project