Homework
Weekly homework submissions:
PRE-LECTURE ABOUT CLASS 2 SLIDES ABOUT PROFESSOR JACOBSON -Question 1 When biological polymerase copies DNA, it makes about 1 mistake per million base pairs (1:10^6).Since the human genome has around 3.2 billion base pairs, that error rate would mean every time one of my cells divides, it would introduce over 3,000 mistakes if there weren’t any correction mechanisms. There’s a 3’-5’ exonuclease that catches and removes errors during DNA synthesis, and then the MutS repair system acts as a backup to fix any mismatches that slipped through afterward. Together, these mechanisms keep my genetic information stable across cell divisions.
Week 1 HW: Principles and Practices
- Biological engineering application: The development of various therapies in which they employ stem cells in Peru, to treat neurodegenerative diseases and chronic diseases. Along with this the appropriate regulations for this type of therapy. -> Why I chose this application: Because in Peru there is an increase in the development of various clinical therapies for diseases that have been very expensive to access, so the emergence of stem cell applications are recent. This is also due to a problem, this is more than anything focused on the little regulation on this type of treatment, which limits the creation of more trained centers, generating a delay in the access of new therapies and research.
Homework 3: Python Script for Opentrons Artwork Overview The task is to create a Python script that runs on an Opentrons OT-2 liquid handling robot and draws an artistic design on a 96-well plate using dye transfer. The design chosen for this project is a mercury droplet, representing the core element of the MeR (Mercury Bioremediation) project. Adapted to reflect a droplet / letter “M” for Mercurio.
Week 4 HW: Protein Design Part 1
Protein Design Part 1 Conceptual Questions Q1: 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) First, we need to calculate the number of moles and multiply by Avogadro’s number (NA=6.022×1023 mol−1). An amino acid has an average mass of ~100 Daltons (Da), which is roughly equivalent to 100 g/mol. Meat is mostly protein (~20% of its weight is protein). → 500 g of meat contains approximately 100 g of protein. Since 1 mole of amino acids weighs ~100 g, there are ~1 mole of amino acids in 100 g of protein. → 1 mole is equivalent to 6.022 × 1023 molecules (Avogadro’s number). So you consume approximately 6 × 1023 amino acids in 500 g of meat.
Week 6 HW: GENETIC CIRCUITS PART I: ASSEMBLY TECHNOLOGIES
- What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose? The Phusion Master Mix is basically a ready-to-use mix that makes setting up PCR way easier since everything is already in it. The main component is the Phusion High-Fidelity DNA Polymerase, which is the enzyme that actually copies the DNA. What makes it special is that it catches and fixes mistakes as it goes, giving you really accurate amplification. It also has the four dNTPs which are the building blocks the polymerase uses to build new DNA strands. There’s also a reaction buffer that keeps the pH and salt conditions stable so the enzyme works properly.
Week 07 HW: Genetic Circuits II
Genetic Circuits II 1.What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Traditional genetic circuits function as digital logic gates, processing inputs in a strictly binary manner — a signal is either present or absent, ON or OFF. While this approach is sufficient for simple regulatory tasks, it is inherently limited in its capacity to handle the complexity characteristic of many biological environments. Intracellular Analog Neural Networks (IANNs) offer several notable advantages over this paradigm. For example, IANNs operate on continuous, graded signals which is typically the concentration of transcription factors, proteins, or regulatory RNAs, it could rather than discrete binary states. This allows them to perform sophisticated pattern recognition and non-linear classification that Boolean logic gates are fundamentally incapable of. Also, IANNs can assign differential weights to distinct inputs, enabling the circuit to be more responsive to certain signals than others, which more accurately reflects the nuanced regulatory logic observed in natural biological systems.
- Advantages of CFPS The main advantage is that CFPS is an open system. Unlike in vivo methods, there is no cell membrane, allowing direct access to the reaction. Flexibility: You can adjust $Mg^{2+}$ levels, add chaperones, or use non-natural amino acids easily. Toxic Proteins: You can produce proteins that would normally kill a living host cell. Speed: It enables “benchtop” production in hours rather than days of cell culture. 2. Main Components Cell Extract (Lysate): The “machinery” (ribosomes, tRNAs, enzymes). DNA Template: The “instructions” for the protein. Energy System: ATP/GTP and a regeneration substrate (e.g., PEP). Amino Acids: The “building blocks.” Salts/Cofactors: Specifically $Mg^{2+}$ and $K^{+}$ for ribosome function. 3. Energy Provision Why it’s critical: Protein synthesis is energy-expensive. Without a regeneration system, ATP is depleted in minutes by side reactions, stopping production.
Week 10 HW: Advanced imaging & measurement technology
HTGAA 2026 — Week 10 Homework Advanced imaging & measurement technology Component Functions E. coli Lysate BL21 (DE3) Star Lysate (T7 RNA Polymerase-expressing) Provides the endogenous transcription–translation machinery, including ribosomes, tRNAs, and metabolic enzymes. T7 RNA polymerase enables high-efficiency transcription from T7 promoters.
Week 11 HW: Cell-Free Protein Synthesis
HTGAA 2026 — Week 11 Homework Cell-Free Protein Synthesis & Collaborative BioArt What I Liked About the Project The experiment beautifully demonstrated that biological creativity and collective computation can be bridged through a structured, yet open-ended collaborative format. What stood out most was the emergent complexity: each individual’s single-pixel decision was locally simple, but globally the artwork encoded recognizable biological imagery. This mirrors how distributed cellular systems encode complex phenotypes from individual gene expression events. The fact that the “canvas” was limited to 1,536 pixels (a deliberate constraint) also made each contribution weighty and meaningful — a great lesson in resource allocation at biological scale.
Week 2 HW: DNA READ, WRITE, & EDIT
DNA Design Challenge Protein: GFP (Green Fluorescent Protein) Reason: Because GFP is commonly used as a biological marker to visualize various cellular processes due to its green fluorescence. sp|P42212|GFP_AEQVI Green fluorescent protein OS=Aequorea victoria OX=6100 GN=GFP PE=1 SV=1 MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTL VTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLV NRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLAD HYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence GFP DNA >ATGTCCAAGGGTGAGGAGCTGTTTACCGGCGTGGTTCCGATTCTTGTGGAATTAGACGGCGATGTCAACGGCCACTTCTCCGTTTCT GGCGAGGGCGAGGGAGGCGACGCCACGTATGGCAAATTGACCCTGAAGTTTATTTGCACGACCGGAAAATTGCCTGTACCGTGGCCCACACTTTGGT CACTACCGTTATCAATGTTTCTCGCTATCCGGACCACATGAAGCAGCATGACTTCTTTAAAAGTGCAATGCCCGAGGGTTATGTTCAAGAGCGGACCA TCTTTTTTAAAAGACGACGGCAACTACAAGACGCGCGAGGTGAAGTTCGAGGGCGACACGCTGGTGAATCGGATTGAGTTAAAAGGAATTGACTTTAA AAGATGACGGCAACATCCTTGGACATAAGTTAGAGTACAATTATAATTCAAACCACGTGTACATCATGGCCGACAAACAAAAAAACGGCATCAAGGTA AACTTTAAAATTAGACATAATATCGAGGATGGCAGTGTTCAATTAGCCGACCATTACCAACAGAACACCGATAGGCGGACGGTCCTGTATTACCTGAC AACCATTACCTTAGCACGCAGTCTGCACTGTCCAAGGACCCAAATGAGAAACGAGGACCATATGGTGTTGCTAGAGTTCGTTACCGCAGCAGGAATAAC Codon optimization The selected organism: Escherichia coli (E. coli) Reason: This is because E. coli is a common model organism for the production of recombinant proteins due to its speed, low cost and ease of manipulation.
Week 5 HW: PROTEIN DESIGN PART II
HTGAA 2026 — Week 5 Homework PROTEIN DESIGN PART II Part 1: Generating Peptides with PepMLM SOD1 A4V Sequence (Human): MATVAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ Generated Peptides: We obtained approximately 4 peptides plus one control peptide, each showing a perplexity score. The sequence modification made was Alanine to Valine substitution at position 4.