Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
- First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about. 🧬 Bio-Hybrid Fusion Blanket Research Context: I am currently a research assistant investigating Magnetohydrodynamics (MHD), specifically focusing on the complex interactions between magnetic fields and 150-million-degree plasma. My work involves optimizing plasma confinement within Tokamak reactors. At these extreme temperatures, the behaviour of the plasmas is governed by a delicate balance of magnetic pressure and fluid dynamics, creating an environment that is incredibly hostile to the physical structures surrounding it.
Week 2 HW: DNA Design Challenge
⚙️ 3.1 Choose a protein I chose the ATP synthase beta subunit because it’s essentially a biological motor and connects to my broader interest in energy systems: Protons flow down their gradient across the mitochondrial membrane, almost like current moving through a circuit, and that flow physically spins part of the protein like a tiny turbine. That rotation drives changes in the beta subunits, which catalyze the formation of ATP from ADP and phosphate.
Week 3 HW: OpenTrons and Python
OpenTrons, Python and Hypotrochoid Patterns 🧪 We learned how to use the Opentrons Python API to write a protocol, essentially a set of instructions that controls the robot’s pipettes. Instead of manually pipetting, we defined coordinates, volumes, and movement steps in code so the robot could deposit liquid precisely into specific wells to create a defined pattern. Also we could simulate the protocol before running it on the actual robot. This let us preview how the design would look, check for mistakes, and adjust the pattern in software first.
🔵 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) Protein in 500 g of meat: 100 g → 26 g protein 500 g → 130 g protein Mass of one amino acid: 1 Dalton = 1.66 × 10⁻²⁴ g Average amino acid ≈ 100 Da → 100 × 1.66 × 10⁻²⁴ = 1.66 × 10⁻²² g
🧬 Part 1 Generate Binders with PepMLM Human SOD1 Sequence: https://www.uniprot.org/uniprotkb/P00441/entry https://www.uniprot.org/uniprotkb/P00441/entry#sequences SOD1 sequence MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLS RKHGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLGDHCIIGRTLVVHEKADDLGKGGNEESTKT GNAGSRLACGVIGIAQ SOD1 sequence with A4V mutation MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLS RKHGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLGDHCIIGRTLVVHEKADDLGKGGNEESTKT GNAGSRLACGVIGIAQ Here is a table with the binders ranked and compared against a known binder: Rank Peptide Source Sequence Pseudo Perplexity 1 Reference (Experimental) FLYRWLPSRRGG 2.2833 2 PepMLM (Candidate 0) KLVPAVVLAHKX 7.4714 3 PepMLM (Candidate 1) KRSYPTALRHWX 10.1367 4 PepMLM (Candidate 2) WRYPVAABHGK 11.0383 5 PepMLM (Candidate 3) WHVYVVGLRHKE 25.8914 The perplexity metric measures how perplexed or “surprised” as it were, a model is by a sequence. Hence a lower score represents higher model confidence or predicted affinity. Here, the known binder FLYRWLPSRRGG acts as a benchmark, scoring 2.28 on the pseudo perplexity rating, which is significantly lower than the newly generated designs. As you can see, I have ranked the binders in order of their respective perplexity ratings.
🧬 1. What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose? Phusion High-Fidelity PCR Master Mix contains several important components needed for accurate DNA amplification during PCR. The main component is Phusion DNA Polymerase, which is a highly accurate and thermostable enzyme that quickly copies DNA while minimizing mistakes. This makes it especially useful for applications such as cloning and DNA sequencing where precision is important.
Week 7 HW: Genetic Circuits II
🧠 1. What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Traditional genetic circuits based on Boolean logic work in a binary way, where genes are basically either on or off. In contrast, IANNs use analog signalling, meaning they can process information in a more continuous and brain-like way. Instead of just sensing whether a signal is there or not, they can also respond to how strong the signal is, which is important because biological systems are noisy and constantly changing.
🧪 Homework Part A: General and Lecturer-Specific Questions 🧬 1. Explain the main advantages of cell-free protein synthesis over traditional in vivo methods, specifically in terms of flexibility and control over experimental variables. Name at least two cases where cell-free expression is more beneficial than cell production. Cell-free protein synthesis essentially uses biology as an engineering tool without needing living cells. Traditional in vivo systems require cells to stay alive, meaning you constantly need to maintain the correct conditions such as nutrients, water, gases, temperature, pressure, and energy supply. In contrast, cell-free systems remove many of these constraints, giving much greater flexibility and control over experimental variables. Since there are no living cells, researchers can directly tune reaction conditions, add or remove components easily, and rapidly test biological circuits or protein designs without worrying about cell survival or toxicity.
Week 10 HW: Imaging & Measurement Technology
🧪 Final Project 📋 For your final project: Please identify at least one (ideally many) aspect(s) of your project that you will measure. It could be the mass or sequence of a protein, the presence, absence, or quantity of a biomarker, etc. Please describe all of the elements you would like to measure, and furthermore describe how you will perform these measurements. What are the technologies you will use (e.g., gel electrophoresis, DNA sequencing, mass spectrometry, etc.)? Describe in detail. For my final project, I would measure whether GFP was successfully conjugated to magnetic beads and whether those GFP-coated magnetic beads can activate anti-GFP synNotch/SNIPR-style receptors in cells. The main thing I care about is whether magnetic presentation of the ligand changes receptor activation compared to normal soluble GFP.
Week 11 HW: Bioproduction and Cloud Labs
🎨 The 1,536 Pixel Artwork Canvas Everyone on the HTGAA network contributed to this global piece of artwork: https://rcdonovan.com/synbiobeta (I contributed by adding a few yellow cells in the bottom centre of the plate for the design. Shout out to Ronan Donovan our TA. I think its absolutely awesome turning biology into a medium for artistic expression! This gave me a fun idea - the pixel art aesthetic kind of reminds me of conway's game of life. What if we made a little simulation where cells of fluorescent proteins/bo pixels evolved over time using the rules from the game of life like a living fluorescent colony - might vibe code this up as a fun weekend project :)