Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
pre-fire post-fire Week 1 Class Assignment First, describe a biological engineering application or tool you want to develop and why. I want to develop a biologically integrated microsensing tool that can detect heavy metal intoxication in living Lemna minor tissue at the cellular level. This idea grew out of a group project facilitated by Dr. Andrew Scarpelli through the ChiTownBio node, where we demonstrated Lemna minor’s ability to phytoremediate heavy metals using a ceramic kiln burnout process to visualize metal accumulation. While this approach confirmed effective metal uptake, its irreversible thermal processing of the plant tissue to generate a readout, making the method qualitative and not easily scalable. Developing a microsensing system that can read biochemical or protein-level responses in living plant cells would allow for more accurate, non-destructive, and low-cost detection of environmental metal contamination.
Week 10 HW: Advanced Imaging & Measurement Technology
Homework: 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. Homework: Waters Part I — Molecular Weight Homework: Waters Part II — Secondary/Tertiary structure Homework: Waters Part III — Peptide Mapping - primary structure Homework: Waters Part IV — Oligomers Homework: Waters Part V — Did I make GFP?
Week 2 HW: DNA Read, Write, & Edit
Part 1: Benchling & In-silico Gel Art Part 2: Gel Art - Restriction Digests and Gel Electrophoresis please visit the week2-lab page here: pages.htgaa.org/2026a/daniel-tseng/labs/week-02-lab-dna-gel-art/ Part 3: DNA Design Challenge 3.1. Choose your protein.
GUI design Opentron Python Script for Opentrons Artwork from opentrons import protocol_api from opentrons.protocol_api import SINGLE from opentrons.types import Point metadata = { 'protocolName': 'Multichannel Color Dispensing (Single Tip) - Dani', 'author': '', 'description': 'Aspirate colors column-wise with a single tip on an ' '8-channel pipette and dispense into virtual grid ' 'positions in a reservoir.', 'apiLevel': '2.20' } # ————————————————————————— # USER CONFIGURATION # ————————————————————————— VOLUME_PER_DROP = 2 DISPENSE_HEIGHT = 0 START_TIP = "E1" DROP_TIP_IN_TRASH = True # Usable volume per well (leave margin below 250 µL max) WELL_CAPACITY = 220 # µL # ————————————————————————— # COLOR MAP # IMPORTANT: use "source_wells" (plural), not "source_well" # ————————————————————————— mscarlet_i_points = [(-7.7, 38.5),(-5.5, 38.5),(-3.3, 38.5),(-1.1, 38.5),(1.1, 38.5),(3.3, 38.5),(5.5, 38.5),(7.7, 38.5),(-14.3, 36.3),(-12.1, 36.3),(-9.9, 36.3),(-7.7, 36.3),(-5.5, 36.3),(-3.3, 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-20.9),(-31.9, -23.1),(-29.7, -23.1),(-27.5, -23.1),(-25.3, -23.1),(-23.1, -23.1),(-20.9, -23.1),(-18.7, -23.1),(-16.5, -23.1),(-14.3, -23.1),(-12.1, -23.1),(-9.9, -23.1),(-7.7, -23.1),(-5.5, -23.1),(-3.3, -23.1),(-1.1, -23.1),(1.1, -23.1),(3.3, -23.1),(5.5, -23.1),(7.7, -23.1),(9.9, -23.1),(12.1, -23.1),(14.3, -23.1),(16.5, -23.1),(18.7, -23.1),(20.9, -23.1),(23.1, -23.1),(25.3, -23.1),(27.5, -23.1),(29.7, -23.1),(31.9, -23.1),(-29.7, -25.3),(-27.5, -25.3),(-25.3, -25.3),(-23.1, -25.3),(-20.9, -25.3),(-18.7, -25.3),(-16.5, -25.3),(-14.3, -25.3),(-12.1, -25.3),(-9.9, -25.3),(-7.7, -25.3),(-5.5, -25.3),(-3.3, -25.3),(-1.1, -25.3),(1.1, -25.3),(3.3, -25.3),(5.5, -25.3),(7.7, -25.3),(9.9, -25.3),(12.1, -25.3),(14.3, -25.3),(16.5, -25.3),(18.7, -25.3),(20.9, -25.3),(23.1, -25.3),(25.3, -25.3),(27.5, -25.3),(29.7, -25.3),(-27.5, -27.5),(-25.3, -27.5),(-23.1, -27.5),(-20.9, -27.5),(-18.7, -27.5),(-16.5, -27.5),(-14.3, -27.5),(-12.1, -27.5),(-9.9, -27.5),(-7.7, -27.5),(-5.5, -27.5),(-3.3, -27.5),(-1.1, -27.5),(1.1, -27.5),(3.3, -27.5),(5.5, -27.5),(7.7, -27.5),(9.9, -27.5),(12.1, -27.5),(14.3, -27.5),(16.5, -27.5),(18.7, -27.5),(20.9, -27.5),(23.1, -27.5),(25.3, -27.5),(27.5, -27.5),(-25.3, -29.7),(-23.1, -29.7),(-20.9, -29.7),(-18.7, -29.7),(-16.5, -29.7),(-14.3, -29.7),(-12.1, -29.7),(-9.9, -29.7),(-7.7, -29.7),(-5.5, -29.7),(-3.3, -29.7),(-1.1, -29.7),(1.1, -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),(16.5, -29.7),(18.7, -29.7),(20.9, -29.7),(23.1, -29.7),(25.3, -29.7),(-23.1, -31.9),(-20.9, -31.9),(-18.7, -31.9),(-16.5, -31.9),(-14.3, -31.9),(-12.1, -31.9),(-9.9, -31.9),(-7.7, -31.9),(-5.5, -31.9),(-3.3, -31.9),(-1.1, -31.9),(1.1, -31.9),(3.3, -31.9),(5.5, -31.9),(7.7, -31.9),(9.9, -31.9),(12.1, -31.9),(14.3, -31.9),(16.5, -31.9),(18.7, -31.9),(20.9, -31.9),(23.1, -31.9),(-20.9, -34.1),(-18.7, -34.1),(-16.5, -34.1),(-14.3, -34.1),(-12.1, -34.1),(-9.9, -34.1),(-7.7, -34.1),(-5.5, -34.1),(-3.3, -34.1),(-1.1, -34.1),(1.1, -34.1),(3.3, -34.1),(5.5, -34.1),(7.7, -34.1),(9.9, -34.1),(12.1, -34.1),(14.3, -34.1),(16.5, -34.1),(18.7, -34.1),(20.9, -34.1),(-14.3, -36.3),(-12.1, -36.3),(-9.9, -36.3),(-7.7, -36.3),(-5.5, -36.3),(-3.3, -36.3),(-1.1, -36.3),(1.1, -36.3),(3.3, -36.3),(5.5, -36.3),(7.7, -36.3),(9.9, -36.3),(12.1, -36.3),(14.3, -36.3),(-7.7, -38.5),(-5.5, -38.5),(-3.3, -38.5),(-1.1, -38.5),(1.1, -38.5),(3.3, -38.5),(5.5, -38.5),(7.7, -38.5)] azurite_points = [(-5.4, 23.4),(-1.8, 23.4),(1.8, 23.4),(5.4, 23.4),(-9, 19.8),(-5.4, 19.8),(-1.8, 19.8),(1.8, 19.8),(5.4, 19.8),(9, 19.8),(12.6, 19.8),(12.6, 16.2),(12.6, 12.6),(-12.6, 9),(-1.8, 9),(1.8, 9),(5.4, 9),(12.6, 9),(-12.6, 5.4),(12.6, 5.4),(-12.6, 1.8),(12.6, 1.8),(-12.6, -1.8),(-9, -1.8),(-5.4, -1.8),(-1.8, -1.8),(1.8, -1.8),(5.4, -1.8),(-12.6, -5.4),(-5.4, -12.6)] mko2_points = [(9, 23.4),(-12.6, 16.2),(12.6, -1.8),(-9, -5.4),(1.8, -5.4),(16.2, -9),(-12.6, -12.6),(-9, -16.2),(-5.4, -16.2),(-1.8, -16.2),(-12.6, -23.4),(-9, -23.4),(-5.4, -23.4),(-1.8, -23.4),(1.8, -23.4),(5.4, -23.4),(9, -23.4),(-5.4, -30.6),(-1.8, -30.6),(1.8, -30.6),(5.4, -30.6),(9, -30.6),(12.6, -30.6),(16.2, -34.2)] sfgfp_points = [] COLOR_MAP = [ {'name': 'Red', 'source_wells': ['A1','B1','C1','D1','E1','F1','G1','H1'], 'points': mscarlet_i_points} ] # ————————————————————————— # RESERVOIR BOUNDS # ————————————————————————— RESERVOIR_X_MM = 107.0 RESERVOIR_Y_MM = 67.0 ALLOW_OUT_OF_BOUNDS = False # ————————————————————————— # PROTOCOL # ————————————————————————— def run(protocol: protocol_api.ProtocolContext): # — Labware — tip_rack = protocol.load_labware('opentrons_96_tiprack_20ul', 6) plate = protocol.load_labware('scienfocus_96_wellplate_250ul', 1) reservoir = protocol.load_labware('thermo_1_reservoir_90000ul', 2) # — Pipette — pipette = protocol.load_instrument('p20_multi_gen2', 'left', tip_racks=[tip_rack]) pipette.configure_nozzle_layout(style=SINGLE, start="H1") # — Reference locations — reservoir_well = reservoir['A1'] dispense_base = reservoir_well.bottom(DISPENSE_HEIGHT) # — Tip ordering — ordered_tips = [] for col in tip_rack.columns(): ordered_tips.extend(col) start_tip_well = tip_rack.wells_by_name()[START_TIP] try: next_tip_index = ordered_tips.index(start_tip_well) except ValueError as exc: raise RuntimeError(f"START_TIP '{START_TIP}' is not in the loaded tip rack.") from exc # ———————————————————————– # MAIN LOOP # ———————————————————————– for color in COLOR_MAP: if not color['points']: continue source_wells = [plate.wells_by_name()[w] for w in color['source_wells']] current_well_index = 0 remaining_volume = WELL_CAPACITY n_dispenses = len(color['points']) protocol.comment( f"— {color['name']} | {len(source_wells)} wells | " f"{n_dispenses} × {VOLUME_PER_DROP} µL —" ) # Pick up one tip per color if next_tip_index >= len(ordered_tips): raise RuntimeError("Out of tips for the configured colors.") pipette.pick_up_tip(ordered_tips[next_tip_index]) next_tip_index += 1 for (x_offset, y_offset) in color['points']: # — Bounds check — if (abs(x_offset) > RESERVOIR_X_MM / 2) or (abs(y_offset) > RESERVOIR_Y_MM / 2): protocol.comment( f"WARNING: {color['name']} point ({x_offset}, {y_offset}) mm is out " f"of bounds ±{RESERVOIR_X_MM/2:.1f} x ±{RESERVOIR_Y_MM/2:.1f}" ) if not ALLOW_OUT_OF_BOUNDS: continue # — Switch wells if needed — if remaining_volume < VOLUME_PER_DROP: current_well_index += 1 if current_well_index >= len(source_wells): raise RuntimeError( f"Ran out of volume for {color['name']} " f"(increase number of source wells)" ) remaining_volume = WELL_CAPACITY protocol.comment( f"Switching to well {color['source_wells'][current_well_index]}" ) source_well = source_wells[current_well_index] # — Aspirate — pipette.aspirate(VOLUME_PER_DROP, source_well) remaining_volume -= VOLUME_PER_DROP # — Move & dispense — target = dispense_base.move(Point(x=x_offset, y=y_offset, z=0)) pipette.move_to(target) pipette.dispense(VOLUME_PER_DROP, target) # — Tip handling — if DROP_TIP_IN_TRASH: pipette.drop_tip() else: pipette.return_tip() Post-Lab Questions
Week 4 HW: Protein Design Part I
COPT1 AlphaFold Part A: Conceptual Questions Answer any NINE of the following questions from Shuguang Zhang: (i.e. you can select two to skip)
Week 5 HW: Protein Design Part II
Part A: SOD1 Binder Peptide Design (From Pranam) Part 1: Generate Binders with PepMLM Begin by retrieving the human SOD1 sequence from UniProt (P00441) and introducing the A4V mutation. Using the PepMLM Colab linked from the HuggingFace PepMLM-650M model card: Generate four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence. To your generated list, add the known SOD1-binding peptide FLYRWLPSRRGG for comparison. Record the perplexity scores that indicate PepMLM’s confidence in the binders. index Type Binder Pseudo Perplexity 0 Known Binder FLRYWLPSRRGG 21.73592494089691 1 Generated AWWPVYVGVKAWRKX 12.725233370840693 2 Generated AWWGVYTVRYAWAAX 12.277514856931905 3 Generated AWYPVLVAVYELKAA 20.11580123195301 4 Generated WWWGPYAAVKELRKK 16.584271595654002 The known SOD1-binding peptide FLYRWLPSRRGG yielded a pseudo-perplexity score of ~21.74, suggesting moderate model confidence. This value provides a baseline for comparing PepMLM-generated peptides, where lower scores would indicate potentially stronger or more compatible binders.
Week 6 HW: Genetic Circuits Part I
Assignment: DNA Assembly Answer these questions about the protocol in this week’s lab: What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose? High‑fidelity DNA polymerase: Catalyzes DNA synthesis with very low error rates, ensuring accurate amplification. dNTPs (deoxynucleotide triphosphates): Serve as the building blocks incorporated into the newly synthesized DNA strands. Reaction buffer: Maintains optimal pH and ionic strength so the polymerase can function efficiently. Mg²⁺ ions: Act as an essential cofactor required for polymerase activity and proper primer–template interaction. Primers: Short DNA sequences that define the start and end points of the region to be amplified. What are some factors that determine primer annealing temperature during PCR? Primer length: Longer primers generally have a higher melting temperature (Tm), which increases the annealing temperature needed for stable binding. GC content: G–C pairs form three hydrogen bonds (vs. two for A–T), so primers with higher GC content have higher Tm values and require higher annealing temperatures. Sequence mismatches: Imperfect complementarity between primer and template lowers the effective Tm, reducing binding stability and lowering the optimal annealing temperature. Salt concentration (ionic strength): Higher salt stabilizes DNA duplex formation by shielding negative charges on the phosphate backbone, increasing Tm. Lower salt does the opposite. There are two methods from this class that create linear fragments of DNA: PCR, and restriction enzyme digests. Compare and contrast these two methods, both in terms of protocol as well as when one may be preferable to use over the other. Comparing PCR and Restriction Enzyme Digests for Generating Linear DNA Fragments:
Week 7 HW: Genetic Circuits Part II
Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Intracellular Artificial Neural Network (IANNs) operate on analog signals because their core components—promoters, transcription factors, and regulatory elements—produce continuously variable expression levels rather than binary ON/OFF states. In these systems, transcription factor concentration acts as the input value, promoter strength functions as the weight, and the Hill‑function response provides the nonlinear activation curve, directly mirroring the structure of artificial neural networks. Instead of flipping between 0 and 1 like Boolean genetic circuits, each input gene generates a graded expression level that becomes the signal strength entering the network. This allows IANNs to process subtle differences in input concentration and integrate weighted contributions, where some signals influence the output more strongly than others. Because the regulatory responses are nonlinear, IANNs can implement thresholding, sigmoidal activation, and multi‑layer logic that far exceed the capabilities of simple AND/OR/NOT gates. They also scale more efficiently: adding new inputs doesn’t require exponentially more genetic parts, avoiding the combinatorial explosion typical of Boolean circuits. Finally, the analog nature of weighted integration makes IANNs more robust to biological noise, smoothing fluctuations that would destabilize traditional digital logic circuits.
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. Cell-free systems remove cellular constraints, allowing direct control over reaction conditions; in terms of flexibility, in cell-free system, you can directly add/remove componenets (DNA/RNA/proteins) without maintaining cell viability, allowing rapid protytyping.in terms of control, cell-free systems allows fine tunning transcripiton/translation rates, concentration of enzymes (agent/reagent). Cell-free system would be benefitical while incorporating non-naturla amino acids, as well as used to synthesize toxic or hard-to-express protein.