Week 01: Principles and Practices

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Contact info

Homework

Week 1 HW: Principles and Practices

1. Application Goal

I want to use CRISPR Cas-9 to knockout the LFY (LEAFY) gene in Arabidopsis thaliana. This serves as a biological engineering tool to provide students with a clear visual confirmation of a successful gene edit—the plant will fail to produce flowers.

2. Governance and Policy Goals

The primary goal is to ensure this tool contributes to an “ethical” future by serving as a standardized educational platform. It allows students to learn gene editing techniques within a framework that provides immediate visual feedback and built-in biosafety (non-reproductive plants).

3. Proposed Governance Actions

Action 1: Standardized Educational CRISPR-LFY Kit

• Purpose: Provide a safe, vetted “kit” for schools to reduce unsafe improvisation.
• Design: * Physical: Use non-integrating systems or low-fertility lines.
  • Protocol: SOPs for containment and autoclaving disposal.
  • Governance: Mandatory Material Transfer Agreements (MTAs).
• Assumptions: Institutions have BSL-1 facilities; teachers follow SOPs.
• Risks: Failure of containment due to small seed size; success leads to off-target effects if handled poorly.

Action 2: Mandatory Ethics & Risk Training

• Purpose: Ensure students understand the “why” and “should,” not just the “how.”
• Design: A required module covering gene editing ethics and case studies.
• Assumptions: Instructors have the support to teach ethics; students engage meaningfully.
• Risks: Ethics treated as a “checkbox”; success might make students overly cautious.

Action 3: Institutional Oversight & Registration

• Purpose: Ensure all gene editing activities are visible to faculty and Biosafety Officers.
• Design: Registry of constructs used, genes targeted, and disposal methods.
• Assumptions: Biosafety Officers have specific expertise in plant gene editing.
• Risks: Excessive bureaucracy could stifle innovation.

4. Scoring & Prioritization

Prioritization: I prioritize a combination of Option 1 and Option 3. The kit (Option 1) provides the physical safety mechanism (the LFY knockout ensures no reproduction), while the Biosafety Officer (Option 3) ensures oversight.

Week 2 Lecture Prep

Questions from Professor Jacobson

• DNA Polymerase Error Rate: Approximately 1 in 10 million base pairs.
• Comparison to Genome: The human genome is ~3 billion base pairs. This discrepancy is managed by advanced proofreading and error correction mechanisms.
• Coding Diversity: An average protein (400 amino acids) can be encoded by roughly $10^{194}$ different DNA sequences.
• Constraint Realities: Most of these codes fail due to constraints in transcription, mRNA stability, translation efficiency, and protein folding.

Questions from Dr. LeProust

• Oligo Synthesis: The most common method is Phosphoramidite Chemistry.
• 200nt Limit: Difficult because error rates are cumulative; the yield of pure, correct sequence drops too low.
• 2000bp via Direct Synthesis: Not viable because the probability of a perfect sequence over that length is statistically near zero with current error rates.

Questions from George Church

• The 10 Essential Amino Acids: Arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine.
• Lysine Contingency: This concept is flawed because all animals already require lysine from their diet; they do not produce it themselves.
• Aspirin-like Stability: To make protein medicines stable, I would circularize the protein (joining the ends) to prevent degradation by heat, similar to the 2014 Heidelberg iGEM project.

Labs

• Week 1 Lab: Pipetting

Projects

• Individual Final Project
• Group Final Project

Week 2 – DNA Read, Write, and Edit

Laboratory Notebook Entry

Metadata

Abstract

This laboratory exercise explored the modern molecular biology workflow: restriction digest simulation, wet-lab electrophoresis, gene design, codon optimization, DNA synthesis preparation, and genome read/write/edit technologies. A developmental transcription factor (MSX-1) was selected, reverse-translated, optimized, and engineered into an expression cassette.

1. Restriction Digest Simulation & Gel Art

Objective

Simulate Lambda DNA restriction digests and generate a gel-art visualization.

Enzymes Used

EcoRI · HindIII · BamHI · KpnI · EcoRV · SacI · SalI

Methods

• Imported Lambda genome into Benchling
• Performed multi-enzyme restriction digest
• Visualized predicted fragment sizes

Result

Figure 1. Simulated restriction digest of Lambda DNA.

2. Wet Lab Restriction Digest

Objective

Perform physical restriction digestion and electrophoresis following the designed protocol.

3. Gene Design Workflow

3.1 Protein Selection

Target Protein:
MSX-1 (Homeobox protein, Homo sapiens)

Rationale:
MSX-1 regulates developmental gene pathways and limb formation.

3.2 Reverse Translation

Full reverse-translated nucleotide sequence:

atggcccccgccgccgacatgaccagcctgcccctgggcgtgaaggtggaggacagcgcc
ttcggcaagcccgccggcggcggcgccggccaggcccccagcgccgccgccgccaccgcc
gccgccatgggcgccgacgaggagggcgccaagcccaaggtgagccccagcctgctgccc
ttcagcgtggaggccctgatggccgaccacagaaagcccggcgccaaggagagcgccctg
gcccccagcgagggcgtgcaggccgccggcggcagcgcccagcccctgggcgtgcccccc
ggcagcctgggcgcccccgacgcccccagcagccccagacccctgggccacttcagcgtg
ggcggcctgctgaagctgcccgaggacgccctggtgaaggccgagagccccgagaagccc
gagagaaccccctggatgcagagccccagattcagccccccccccgccagaagactgagc
ccccccgcctgcaccctgagaaagcacaagaccaacagaaagcccagaacccccttcacc
accgcccagctgctggccctggagagaaagttcagacagaagcagtacctgagcatcgcc
gagagagccgagttcagcagcagcctgagcctgaccgagacccaggtgaagatctggttc
cagaacagaagagccaaggccaagagactgcaggaggccgagctggagaagctgaagatg
gccgccaagcccatgctgccccccgccgccttcggcctgagcttccccctgggcggcccc
gccgccgtggccgccgccgccggcgccagcctgtacggcgccagcggccccttccagaga
gccgccctgcccgtggcccccgtgggcctgtacaccgcccacgtgggctacagcatgtac
cacctgacc

3.3 Codon Optimization

Full codon-optimized sequence:

ATG GCT CCT GCC GCT GAC ATG ACA TCC CTC CCT CTG GGC GTG AAA GTC GAA GAC TCT GCC TTC GGA AAA CCA GCT GGA GGA GGT GCA GGC CAA GCG CCC TCA GCC GCC GCC GCA ACT GCC GCG GCA ATG GGC GCG GAT GAA GAA GGA GCA AAG CCT AAA GTC TCA CCC TCT TTG CTC CCC TTC TCT GTT GAG GCA CTC ATG GCC GAC CAC AGG AAA CCT GGC GCC AAA GAG TCA GCA CTT GCT CCA TCT GAG GGC GTG CAG GCT GCC GGT GGG TCT GCC CAG CCA CTC GGC GTT CCT CCT GGG TCT CTC GGT GCC CCC GAC GCC CCT AGC TCT CCA CGC CCT CTT GGG CAC TTT AGC GTG GGC GGG CTG CTG AAA CTT CCA GAA GAC GCA CTC GTT AAG GCC GAA AGT CCT GAG AAA CCC GAG CGA ACC CCT TGG ATG CAG TCA CCC AGG TTC TCA CCC CCT CCC GCT AGG AGG CTC TCC CCC CCA GCA TGT ACT CTC CGG AAA CAT AAG ACA AAT AGA AAA CCC CGC ACC CCG TTT ACC ACC GCC CAG CTG CTG GCC CTT GAG AGA AAG TTC CGG CAG AAG CAG TAC CTC TCC ATC GCC GAA CGG GCT GAG TTC TCC TCC TCC TTG TCC CTC ACC GAG ACA CAG GTT AAG ATT TGG TTC CAG AAC CGC CGG GCA AAG GCA AAA CGG CTG CAA GAA GCC GAG CTG GAG AAG CTT AAG ATG GCA GCT AAA CCC ATG CTC CCT CCA GCA GCG TTT GGC CTC AGT TTT CCA CTG GGC GGC CCA GCT GCA GTG GCA GCT GCG GCC GGC GCC TCC CTC TAT GGA GCC TCC GGG CCG TTC CAA CGG GCC GCA CTT CCC GTA GCA CCA GTC GGG TTG TAC ACT GCA CAT GTC GGC TAC AGC ATG TAC CAC CTG ACC

4. Expression Cassette Engineering

Full Expression Cassette Sequence

TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGCCATTAAAGAGGAGAAAGGTACCatggcccccgccgccgacatgaccagcctgcccctgggcgtgaaggtggaggacagcgccttcggcaagcccgccggcggcggcgccggccaggcccccagcgccgccgccgccaccgccgccgccatgggcgccgacgaggagggcgccaagcccaaggtgagccccagcctgctgcccttcagcgtggaggccctgatggccgaccacagaaagcccggcgccaaggagagcgccctggcccccagcgagggcgtgcaggccgccggcggcagcgcccagcccctgggcgtgccccccggcagcctgggcgcccccgacgcccccagcagccccagacccctgggccacttcagcgtgggcggcctgctgaagctgcccgaggacgccctggtgaaggccgagagccccgagaagcccgagagaaccccctggatgcagagccccagattcagccccccccccgccagaagactgagcccccccgcctgcaccctgagaaagcacaagaccaacagaaagcccagaacccccttcaccaccgcccagctgctggccctggagagaaagttcagacagaagcagtacctgagcatcgccgagagagccgagttcagcagcagcctgagcctgaccgagacccaggtgaagatctggttccagaacagaagagccaaggccaagagactgcaggaggccgagctggagaagctgaagatggccgccaagcccatgctgccccccgccgccttcggcctgagcttccccctgggcggccccgccgccgtggccgccgccgccggcgccagcctgtacggcgccagcggccccttccagagagccgccctgcccgtggcccccgtgggcctgtacaccgcccacgtgggctacagcatgtaccacctgaccCATCACCATCACCATCATCACTAACCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCG

Annotated Elements

Promoter

TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGCCATTAAAG

RBS

AGGAGAAAGG

Spacer

TACC

6×His Tag

CATCACCATCACCATCATCA

Terminator

AGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCG

Benchling Construct Map

Figure 2. Benchling construct showing CDS, His-tag, and terminator.

5. DNA Read · Write · Edit

5.1 DNA Read

Target genome: Secretariat (racehorse)
Technology: Nanopore sequencing

Workflow:

1. DNA extraction
2. Purification
3. Library preparation
4. Sequencing
5. Base calling
6. Assembly

5.2 DNA Write

Technology: Phosphoramidite synthesis and gene synthesis providers

Steps:

1. Oligo synthesis
2. Gibson Assembly
3. Validation sequencing

Limitations:

• Fragment size limits
• Time and cost
• Assembly complexity

5.3 DNA Edit

Technology: CRISPR knock-in

Mechanism:

1. Guide RNA design
2. Cas9 cleavage
3. Homology-directed repair
4. Screening

Limitations:

• Off-target effects
• Efficiency variability

Week 3 – Lab Automation

Opentrons Script from opentrons import types

metadata = { ‘protocolName’: ‘HTGAA Robotic Patterning - Misu Optimized Full’, ‘author’: ‘HTGAA Virtual Exercise’, ‘source’: ‘HTGAA 2022 Modified’, ‘apiLevel’: ‘2.20’ }

TIP_RACK_DECK_SLOT = 9 COLORS_DECK_SLOT = 6 AGAR_DECK_SLOT = 5 PIPETTE_STARTING_TIP_WELL = ‘A1’

well_colors = { ‘A1’ : ‘Red’, ‘B1’ : ‘Yellow’, ‘C1’ : ‘Green’, ‘D1’ : ‘Cyan’, ‘E1’ : ‘Blue’ }

def run(protocol):

# -----------------------------
# Speed Optimization
# -----------------------------
protocol.max_speeds.update({
    'X': 300,
    'Y': 300,
    'Z': 80,
    'A': 80
})

# -----------------------------
# Labware
# -----------------------------
tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT)
pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])

temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)
temperature_plate = temperature_module.load_labware(
    'opentrons_96_aluminumblock_generic_pcr_strip_200ul'
)

agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT)
center_location = agar_plate['A1'].top()

pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

# -----------------------------
# Helpers
# -----------------------------
def location_of_color(color_string):
    for well, color in well_colors.items():
        if color.lower() == color_string.lower():
            return temperature_plate[well]
    raise ValueError(f"No well found with color {color_string}")

def dispense_and_detach_fast(pipette, volume, location):
    near = location.move(types.Point(z=location.point.z + 2))
    pipette.move_to(near)
    pipette.dispense(volume, location)
    pipette.move_to(location.move(types.Point(x=0.8)))
    pipette.move_to(near)

# -----------------------------
# FULL MISU Coordinate List
# -----------------------------
mturquoise2_points = [

(-4.5, 15.5),(-4.5, 14.5),(-3.5, 14.5),(-2.5, 14.5),(-3.5, 13.5),(-2.5, 13.5), (-1.5, 13.5),(-0.5, 13.5),(-3.5, 12.5),(-2.5, 12.5),(-1.5, 12.5),(-0.5, 12.5), (-3.5, 11.5),(-2.5, 11.5),(-1.5, 11.5),(-0.5, 11.5),(-3.5, 10.5),(-2.5, 10.5), (-1.5, 10.5),(-0.5, 10.5),(-3.5, 9.5),(-2.5, 9.5),(-1.5, 9.5),(-0.5, 9.5), (5.5, 9.5),(6.5, 9.5),(7.5, 9.5),(-3.5, 8.5),(-2.5, 8.5),(-1.5, 8.5), (5.5, 8.5),(6.5, 8.5),(7.5, 8.5),(8.5, 8.5),(-3.5, 7.5),(-2.5, 7.5), (-1.5, 7.5),(4.5, 7.5),(5.5, 7.5),(6.5, 7.5),(7.5, 7.5),(8.5, 7.5), (-3.5, 6.5),(-2.5, 6.5),(-1.5, 6.5),(4.5, 6.5),(5.5, 6.5),(6.5, 6.5), (7.5, 6.5),(-3.5, 5.5),(-2.5, 5.5),(-1.5, 5.5),(3.5, 5.5),(4.5, 5.5), (5.5, 5.5),(-7.5, 4.5),(-6.5, 4.5),(-3.5, 4.5),(-2.5, 4.5),(-1.5, 4.5), (2.5, 4.5),(3.5, 4.5),(4.5, 4.5),(-10.5, 3.5),(-9.5, 3.5),(-8.5, 3.5), (-7.5, 3.5),(-6.5, 3.5),(-5.5, 3.5),(-3.5, 3.5),(-2.5, 3.5),(-1.5, 3.5), (1.5, 3.5),(2.5, 3.5),(3.5, 3.5),(-13.5, 2.5),(-12.5, 2.5),(-11.5, 2.5), (-10.5, 2.5),(-9.5, 2.5),(-7.5, 2.5),(-6.5, 2.5),(-5.5, 2.5),(-3.5, 2.5), (-2.5, 2.5),(-1.5, 2.5),(-0.5, 2.5),(0.5, 2.5),(1.5, 2.5), (-14.5, 1.5),(-13.5, 1.5),(-12.5, 1.5),(-8.5, 1.5),(-7.5, 1.5), (-6.5, 1.5),(-3.5, 1.5),(-2.5, 1.5),(-1.5, 1.5),(0.5, 1.5),(1.5, 1.5), (-13.5, 0.5),(-9.5, 0.5),(-8.5, 0.5),(-7.5, 0.5),(-6.5, 0.5), (-3.5, 0.5),(-2.5, 0.5),(-1.5, 0.5),(0.5, 0.5),(1.5, 0.5),(2.5, 0.5), (-10.5, -0.5),(-9.5, -0.5),(-8.5, -0.5),(-7.5, -0.5),(-3.5, -0.5), (-2.5, -0.5),(-1.5, -0.5),(2.5, -0.5),(3.5, -0.5),(4.5, -0.5), (-10.5, -1.5),(-9.5, -1.5),(-8.5, -1.5),(-3.5, -1.5),(-2.5, -1.5), (-1.5, -1.5),(3.5, -1.5),(4.5, -1.5),(-11.5, -2.5),(-10.5, -2.5), (-9.5, -2.5),(-3.5, -2.5),(-2.5, -2.5),(-1.5, -2.5),(4.5, -2.5), (5.5, -2.5),(-11.5, -3.5),(-10.5, -3.5),(-9.5, -3.5),(-3.5, -3.5), (-2.5, -3.5),(-1.5, -3.5),(4.5, -3.5),(5.5, -3.5),(6.5, -3.5),(7.5, -3.5), (-12.5, -4.5),(-11.5, -4.5),(-3.5, -4.5),(-2.5, -4.5),(-1.5, -4.5), (5.5, -4.5),(6.5, -4.5),(7.5, -4.5),(8.5, -4.5),(-13.5, -5.5), (-12.5, -5.5),(-11.5, -5.5),(-3.5, -5.5),(-2.5, -5.5),(-1.5, -5.5), (5.5, -5.5),(6.5, -5.5),(7.5, -5.5),(8.5, -5.5),(9.5, -5.5), (-14.5, -6.5),(-13.5, -6.5),(-3.5, -6.5),(-2.5, -6.5),(-1.5, -6.5), (6.5, -6.5),(7.5, -6.5),(8.5, -6.5),(9.5, -6.5),(10.5, -6.5), (11.5, -6.5),(-14.5, -7.5),(-3.5, -7.5),(-2.5, -7.5),(-1.5, -7.5), (-0.5, -7.5),(6.5, -7.5),(7.5, -7.5),(8.5, -7.5),(9.5, -7.5), (10.5, -7.5),(11.5, -7.5),(12.5, -7.5),(13.5, -7.5),(-15.5, -8.5), (-3.5, -8.5),(-2.5, -8.5),(-1.5, -8.5),(-0.5, -8.5),(7.5, -8.5), (8.5, -8.5),(9.5, -8.5),(10.5, -8.5),(11.5, -8.5),(12.5, -8.5), (13.5, -8.5),(14.5, -8.5),(-3.5, -9.5),(-2.5, -9.5),(-1.5, -9.5), (-0.5, -9.5),(-5.5, -10.5),(-4.5, -10.5),(-3.5, -10.5),(-2.5, -10.5), (-1.5, -10.5),(-0.5, -10.5),(-4.5, -11.5),(-3.5, -11.5),(-2.5, -11.5), (-1.5, -11.5),(-0.5, -11.5),(-4.5, -12.5),(-3.5, -12.5),(-2.5, -12.5), (-1.5, -12.5),(-3.5, -13.5),(-2.5, -13.5),(-1.5, -13.5), (-3.5, -14.5),(-2.5, -14.5),(-2.5, -15.5) ]

# -----------------------------
# Printing
# -----------------------------
pipette_20ul.pick_up_tip()

volume_per_drop = 1
max_batch_volume = 18
drops_per_batch = int(max_batch_volume / volume_per_drop)

total_points = len(mturquoise2_points)
index = 0

while index < total_points:

    batch = mturquoise2_points[index:index + drops_per_batch]

    pipette_20ul.aspirate(
        len(batch) * volume_per_drop,
        location_of_color('Cyan')
    )

    for x_offset, y_offset in batch:
        dispense_location = center_location.move(
            types.Point(x=x_offset, y=y_offset)
        )
        dispense_and_detach_fast(
            pipette_20ul,
            volume_per_drop,
            dispense_location
        )

    index += drops_per_batch

pipette_20ul.drop_tip()