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. I have worked with the concept of CA before within design and 3d space generative making through creating tools for generating patterns and environments, so it was really fascinating to see it being brought up during class. So, for my idea I’d like to merge my previous digital experience with CA and synthetic biology tooling in a form of a computer aided design tool for spatial synthetic biology
Week 2 HW: DNA Read, Write, & Edit
Part 1: Benchling & In-silico Gel Art Here’s my version of made in Benchling pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks :) .. It’s a frog!!! https://benchling.com/s/seq-E1w2i5oXtxnqS8BWnwcZ?m=slm-mz29OlZAWUwDeJTn4nir I also played around separately to that drawing with Ronan’s tool, and this is what I got ( I wanted to go for something more abstract this time)
Week 3 HW: Principles and Practices
For this lab, I made a little bunny drawing! I used Ronan’s tool as a base and then fixed it to work with colab environment and colours that we have in our node. from opentrons import types import math metadata = { 'protocolName': 'Bunny', 'author': 'HTGAA', 'source': 'HTGAA 2026', 'apiLevel': '2.20' } # Coordinate Data mkate2_points = [(12.1, 29.7),(14.3, 29.7),(16.5, 29.7),(18.7, 29.7),(20.9, 29.7),(23.1, 29.7),(9.9, 27.5),(12.1, 27.5),(16.5, 27.5),(18.7, 27.5),(20.9, 27.5),(23.1, 27.5),(-1.1, 25.3),(1.1, 25.3),(3.3, 25.3),(5.5, 25.3),(7.7, 25.3),(20.9, 25.3),(23.1, 25.3),(-3.3, 23.1),(-1.1, 23.1),(7.7, 23.1),(14.3, 23.1),(20.9, 23.1),(25.3, 23.1),(27.5, 23.1),(-7.7, 20.9),(14.3, 20.9),(20.9, 20.9),(29.7, 20.9),(12.1, 18.7),(16.5, 18.7),(18.7, 18.7),(29.7, 18.7),(-12.1, 16.5),(12.1, 16.5),(14.3, 16.5),(16.5, 16.5),(18.7, 16.5),(20.9, 16.5),(23.1, 16.5),(25.3, 16.5),(27.5, 16.5),(-14.3, 14.3),(12.1, 14.3),(16.5, 14.3),(18.7, 14.3),(25.3, 14.3),(-14.3, 12.1),(27.5, 12.1),(-16.5, 9.9),(27.5, 9.9),(-16.5, 7.7),(29.7, 7.7),(-16.5, 5.5),(-14.3, 5.5),(29.7, 5.5),(-16.5, 3.3),(-14.3, 3.3),(31.9, 3.3),(-14.3, 1.1),(-12.1, 1.1),(31.9, 1.1),(-12.1, -3.3),(34.1, -3.3),(-16.5, -5.5),(-14.3, -5.5),(34.1, -5.5),(-27.5, -7.7),(-25.3, -7.7),(-23.1, -7.7),(-18.7, -7.7),(-16.5, -7.7),(34.1, -7.7),(-27.5, -9.9),(-23.1, -9.9),(-20.9, -9.9),(-18.7, -9.9),(-16.5, -9.9),(-7.7, -9.9),(-5.5, -9.9),(34.1, -9.9),(-27.5, -12.1),(-18.7, -12.1),(-16.5, -12.1),(-14.3, -12.1),(-12.1, -12.1),(-9.9, -12.1),(-7.7, -12.1),(-5.5, -12.1),(34.1, -12.1),(-25.3, -14.3),(-16.5, -14.3),(-14.3, -14.3),(-12.1, -14.3),(-29.7, -16.5),(-27.5, -16.5),(-25.3, -16.5),(-23.1, -16.5),(-12.1, -16.5),(-9.9, -16.5),(31.9, -16.5),(-29.7, -18.7),(-20.9, -18.7),(-29.7, -20.9),(-18.7, -20.9),(-16.5, -20.9),(-14.3, -20.9),(-12.1, -20.9),(27.5, -20.9),(-27.5, -23.1),(-12.1, -23.1),(25.3, -23.1),(27.5, -23.1),(-27.5, -25.3),(-25.3, -25.3),(25.3, -25.3),(-23.1, -27.5),(-20.9, -27.5),(-18.7, -27.5),(-16.5, -27.5),(1.1, -27.5),(3.3, -27.5),(5.5, -27.5),(7.7, -27.5),(23.1, -27.5),(-14.3, -29.7),(-12.1, -29.7),(-9.9, -29.7),(-7.7, -29.7),(-3.3, -29.7),(5.5, -29.7),(7.7, -29.7),(9.9, -29.7),(12.1, -29.7),(14.3, -29.7),(18.7, -29.7),(20.9, -29.7)] mcerulean3_points = [(14.3, 25.3),(27.5, 20.9),(23.1, 18.7),(25.3, 18.7),(27.5, 18.7),(-5.5, 12.1),(-7.7, 9.9),(-5.5, 9.9)] TIP_RACK_DECK_SLOT = 9 COLORS_DECK_SLOT = 6 AGAR_DECK_SLOT = 5 PIPETTE_STARTING_TIP_WELL = 'A1' well_colors = { 'A1' : 'Purple', 'B1' : 'Pink', 'C3' : 'Cyan' } def run(protocol): tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips') pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul]) temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT) color_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul', 'Cold Plate') agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate') center_location = agar_plate['A1'].top() pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL) def location_of_color(color_string): for well,color in well_colors.items(): if color.lower() == color_string.lower(): return color_plate[well] raise ValueError(f"No well found with color {color_string}") def dispense_and_detach(pipette, volume, location): above_location = location.move(types.Point(z=location.point.z + 5)) pipette.move_to(above_location) pipette.dispense(volume, location) pipette.move_to(above_location) drop_vol = 0.5 # Draw mcerulean3_points using Pink pipette_20ul.pick_up_tip() pipette_20ul.aspirate(len(mcerulean3_points) * drop_vol, location_of_color('Pink')) for x, y in mcerulean3_points: dispense_and_detach(pipette_20ul, drop_vol, center_location.move(types.Point(x, y))) pipette_20ul.drop_tip() # Draw mkate2_points using Blue pipette_20ul.pick_up_tip() num_batches = math.ceil((len(mkate2_points) * drop_vol) / 18) points_per_batch = math.floor(len(mkate2_points) / num_batches) for b in range(num_batches): start = b * points_per_batch end = min((b + 1) * points_per_batch, len(mkate2_points)) current_batch = mkate2_points[start:end] pipette_20ul.aspirate(len(current_batch) * drop_vol, location_of_color('Cyan')) for x, y in current_batch: dispense_and_detach(pipette_20ul, drop_vol, center_location.move(types.Point(x, y))) pipette_20ul.drop_tip() Post-Lab Questions Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.
Week 4 HW: Protein Design Part I
Part A. Conceptual Questions 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) 500g of meat = ~20% protein 100g ÷ 110 g/mol ≈ 0.91 mol 0.91 × 6.022 × 10²³ ≈ 5.5 × 10²³ molecules* Why are there only 20 natural amino acids?
Week 5 HW: Protein Design Part II
Part A: SOD1 Binder Peptide Design (From Pranam) Part 1: Generate Binders with PepMLM sp|P00441|SODC_HUMAN Superoxide dismutase [Cu-Zn] OS=Homo sapiens OX=9606 GN=SOD1 PE=1 SV=2 MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTS AGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVV HEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ (taken from https://rest.uniprot.org/uniprotkb/P00441.fasta ) -> muted form A-> V MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTS AGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVV HEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ (2-5) # Binder Pseudo Perplexity Notes 0 WHYGAAQAAHWX 7.60026377248636 High confidence 1 WLYGASAAAWKK 7.46473740432208 Highest confidence!!! 2 WLYGAAGVAWKE 10.9325804754158 Moderate confidence 3 WLYYPQAAKLKK 15.5499787120909 Lowest confidence — FLYRWLPSRRGG 20.9180890005569 Known binder (control) Part 2: Evaluate Binders with AlphaFold3
Week 6 HW: Genetic Circuits Part I: Assembly Technologies
Assignment: DNA Assembly What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose? Based on Protocol for Phusion™ High-Fidelity PCR Master Mix with GC Buffer (https://www.neb.com/en-gb/protocols/protocol-for-phusion-high-fidelity-pcr-master-mix-with-gc-buffer-m0532) Phusion DNA Polymerase - a high-fidelity enzyme that synthesises new DNA strands with 3’→5’ exonuclease activity for proofreading dNTPs - building blocks for DNA synthesis (200 µM each at 1X concentration) MgCl₂ - Cofactor required for polymerase activity (1.5 mM at 1X concentration) Possibly optional as the question doesn’t mention it (GC Buffer - optimised buffer for amplifying difficult templates with high GC content or secondary structure) DMSO (optional additive) - helps denature secondary structures in GC-rich or difficult templates (recommended at 3% final concentration)
Week 7 HW: Genetic Circuits Part II: Neuromorphic Circuits
Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Analogue quality of computations (not just on and off, but rather treat them more like waves ->easier to work with) -> Thresholds finetuning works You can also work with more inputs simultaneously that will automatically become a weighted sum Which also means they can be trained like neural networks! whereas Boolean functions are just True/False with operators AND, OR, and NOT
Homework Part A: General and Lecturer-Specific Questions General homework questions 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. As the reaction happens outside living cells, you have much more flexibility and control over experimental variables!
Week 10 HW: Advanced Imaging & Measurement Technology
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. molecular validation (to confirm whether the Twist plasmids are correct and transformed properly) through the presence of the sender and receiver plasmid, correct insert size and sequence identity, then successful bacterial transformation functional signalling validation / AHL sensing ability through receiver activation threshold, GFP expression strength, dose-response curve, signal saturation spatial pattern formation to measure whether LuxI actually produces AHL through sender-generated AHL activity, effective AHL equivalent concentration, and signalling consistency between cultures ML/CAD dataset features through OT-2 plate maps, fluorescence reads, OD600 and metadata logs 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.
Week 11 HW: Bioproduction & Cloud Labs
Part A: The 1,536 Pixel Artwork Canvas | Collective Artwork At this point, I’m not entirely sure what I’ve contributed to, as the artwork changed quite a lot. I’ve added a few yellow and green pixels when we first got access to the board, but as time went it changed so much that I don’t think any of them were left in the same spots. I did like the collaborative aspect of it, but going forward, I kind of wish everyone could contribute to just one/two pixels that couldn’t be overwritten. That would make the pointing-out aspect of it so much easier
See week 12~