Week 3: Lab Automation & Opentrons Art

Part 1: Opentrons Agar Art - Biohazard Symbol

Design Concept

I conceptualised the biohazard symbol, which I thought of creating using parametric algorithms. I used the assistance of Claude (Anthropic) to generate the python code which I ran on Google Colab, in the copy provided by HTGAA. The results are shown below.

The artwork I conceptualised is a biohazard symbol rendered in fluorescent bacteria on black agar. I specifically chose this design because my final project focuses on engineering Bacillus subtilis to combat hospital-acquired infections (HAIs), which kill over 200,000 people annually in India alone.

Python Code

The complete Python script is embedded below and was also submitted via the course form. I used Claude (Anthropic) to help write the code, debug coordinate math and validate the Opentrons API calls, while I designed the concept, chose the geometry, and structured the protocol logic.

from opentrons import types
import math

metadata = {    # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata
    'author': 'Anirudh Gangadharan',
    'protocolName': 'Biohazard Biosensor Symbol',
    'description': 'Parametric biohazard symbol representing engineered B. subtilis biosensor: purple crescents (threat detection), blue hub (sensing logic), pink safety ring (biocontainment)',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

##############################################################################
###   Robot deck setup constants - don't change these
##############################################################################

TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

well_colors = {
    'A1' : 'Red',
    'B1' : 'Blue',
    'C1' : 'Green'
}


def run(protocol):
  ##############################################################################
  ###   Load labware, modules and pipettes
  ##############################################################################

  # Tips
  tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')

  # Pipettes
  pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])

  # Modules
  temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)

  # Temperature Module Plate
  temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul',
                                                      'Cold Plate')
  # Choose where to take the colors from
  color_plate = temperature_plate

  # Agar Plate
  agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')  ## TA MUST CALIBRATE EACH PLATE!
  # Get the top-center of the plate, make sure the plate was calibrated before running this
  center_location = agar_plate['A1'].top()

  pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

  ##############################################################################
  ###   Patterning
  ##############################################################################

  ###
  ### Helper functions for this lab
  ###

  # pass this e.g. 'Red' and get back a Location which can be passed to aspirate()
  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}")

  # For this lab, instead of calling pipette.dispense(1, loc) use this: dispense_and_detach(pipette, 1, loc)
  def dispense_and_detach(pipette, volume, location):
      """
      Move laterally 5mm above the plate (to avoid smearing a drop); then drop down to the plate,
      dispense, move back up 5mm to detach drop, and stay high to be ready for next lateral move.
      5mm because a 4uL drop is 2mm diameter; and a 2deg tilt in the agar pour is >3mm difference across a plate.
      """
      assert(isinstance(volume, (int, float)))
      above_location = location.move(types.Point(z=location.point.z + 5))  # 5mm above
      pipette.move_to(above_location)       # Go to 5mm above the dispensing location
      pipette.dispense(volume, location)    # Go straight downwards and dispense
      pipette.move_to(above_location)       # Go straight up to detach drop and stay high

  ###
  ### BIOHAZARD BIOSENSOR SYMBOL
  ###

  # --- Geometry Parameters ---
  LOBE_DIST = 8.5       # distance from center to each lobe center
  LOBE_R_OUTER = 10.0   # outer radius of each crescent lobe
  LOBE_R_INNER = 5.5    # inner cutout radius
  GAP_ANGLE = 25        # degrees of gap between lobes
  DOT_SPACING = 2.0     # mm between dots (>= 2mm to avoid merging)
  PLATE_R = 33.0        # max radius from center (within 40mm safety limit)

  lobe_degs = [90, 210, 330]  # three lobes at 120 degree intervals

  # --- Generate Red points: Three crescent lobes (threat/pathogen detection) ---
  red_pts = []
  for bd in lobe_degs:
      br = math.radians(bd)
      cx, cy = LOBE_DIST * math.cos(br), LOBE_DIST * math.sin(br)
      for gx in range(-15, 16):
          for gy in range(-15, 16):
              x, y = gx * DOT_SPACING / 2, gy * DOT_SPACING / 2
              px, py = cx + x, cy + y
              dl = math.sqrt(x**2 + y**2)
              do = math.sqrt(px**2 + py**2)
              if dl > LOBE_R_OUTER or do <= LOBE_R_INNER or do > PLATE_R:
                  continue
              ang = math.degrees(math.atan2(py, px)) % 360
              gap = False
              for gc in [150, 270, 30]:
                  ad = abs(ang - gc)
                  if ad > 180: ad = 360 - ad
                  if ad < GAP_ANGLE / 2: gap = True; break
              if gap: continue
              if not any(math.sqrt((px-ex)**2 + (py-ey)**2) < DOT_SPACING * 0.85 for ex, ey in red_pts):
                  red_pts.append((round(px, 1), round(py, 1)))

  # --- Generate Blue points: Central hub (sensing/logic core) ---
  blue_pts = [(0.0, 0.0)]
  for i in range(8):
      a = 2 * math.pi * i / 8
      blue_pts.append((round(2.5 * math.cos(a), 1), round(2.5 * math.sin(a), 1)))

  # --- Generate Green points: Outer safety ring (biocontainment) ---
  green_pts = []
  for i in range(44):
      a = 2 * math.pi * i / 44
      green_pts.append((round(22 * math.cos(a), 1), round(22 * math.sin(a), 1)))
  for i in range(44):
      a = 2 * math.pi * (i + 0.5) / 44
      green_pts.append((round(24.5 * math.cos(a), 1), round(24.5 * math.sin(a), 1)))

  # --- Helper: pick up tip, aspirate color, dispense all points, drop tip ---
  def pick_up_and_draw(color_name, points):
      pipette_20ul.pick_up_tip()
      batch_size = 19  # aspirate up to 19uL at a time (19 dots at 1uL each)
      for i in range(0, len(points), batch_size):
          batch = points[i:i + batch_size]
          pipette_20ul.aspirate(len(batch), location_of_color(color_name))
          for (px, py) in batch:
              loc = center_location.move(types.Point(x=px, y=py))
              dispense_and_detach(pipette_20ul, 1, loc)
      pipette_20ul.drop_tip()

  # --- Execute patterning ---
  pick_up_and_draw('Red', red_pts)      # Crescents: threat detection
  pick_up_and_draw('Blue', blue_pts)    # Hub: sensing logic gate
  pick_up_and_draw('Green', green_pts)  # Ring: biocontainment boundary

  # Done! All tips dropped.

After running the above code, I ran the code shown below to visualise the image and it is provided below.

# Execute Simulation / Visualization -- don't change this code block
protocol = OpentronsMock(well_colors)
run(protocol)
protocol.visualize()
=== VOLUME TOTALS BY COLOR ===
	Blue:		 aspirated 9	 dispensed 9
	Red:		 aspirated 168	 dispensed 168
	Green:		 aspirated 88	 dispensed 88
	[all colors]:	[aspirated 265]	[dispensed 265]

=== TIP COUNT ===
	 Used 3 tip(s)  (ideally exactly one per unique color)
biohazard_visualisation biohazard_visualisation

AI Usage Disclosure

I used Claude (Anthropic) to write the code for the biohazard geometry.

Part 2: Post-Lab Questions

Published Paper Utilizing Laboratory Automation for Novel Biological Applications

Paper: Semiautomated Production of Cell-Free Biosensors

Citation: Brown, D.M., Phillips, D.A., Garcia, D.C., et al. (2025). Semiautomated Production of Cell-Free Biosensors. ACS Synthetic Biology, 14(3), 979–986. DOI: 10.1021/acssynbio.4c00703

Affiliations: Northwestern University (Department of Chemical & Biological Engineering, Center for Synthetic Biology); U.S. Army DEVCOM Chemical Biological Center.

Summary

Brown et al. (2025) present the first systematic study comparing manual versus robot-assembled cell-free biosensor reactions, using the Opentrons OT-2 liquid handling robot to automate the assembly of cell-free gene expression (CFE) reactions across full 384-well plates.

Cell-free biosensors represent a powerful class of synthetic biology diagnostics: DNA-encoded genetic circuits are executed in cell-free transcription-translation (TX-TL) systems, freeze-dried for ambient storage and field distribution, then rehydrated at the point of use to detect target analytes via colorimetric or fluorescent output. Previous work - notably Pardee et al. (2016) demonstrating Zika virus detection using toehold switches in cell-free systems - established the diagnostic potential of this platform. However, manufacturing has remained a manual, low-throughput bottleneck that limits translation from proof-of-concept to deployable diagnostics.

research_paper research_paper
Automation Platform and Workflow

The OT-2 performed semiautomated assembly of CFE reactions, handling precise nanoliter-to-microliter transfers of cell extract, energy buffer, DNA template, and water into each well of a 384-well plate. The team benchmarked two cell-free reporter systems:

  • A constitutive LacZ colorimetric reporter (absorbance-based readout)
  • A GFP fluorescent reporter (fluorescence-based readout)

For each system, reactions were assembled both manually and via OT-2, enabling direct comparison of precision, reproducibility, and biosensor performance. The culminating experiment deployed a complete 384-well fluoride riboswitch biosensor array - a genetically encoded sensor element that activates gene expression in the presence of fluoride ions - with every reaction assembled by the OT-2.

Key Findings

  1. Semiautomated OT-2 assembly produced biosensor reactions with comparable or improved consistency relative to manual assembly, reducing the well-to-well coefficient of variation that plagues hand-pipetted plates and directly degrades diagnostic reliability.

  2. A full 384-well fluoride riboswitch biosensor array was successfully built and functionally validated in a single automated run, demonstrating the feasibility of medium-throughput biosensor production on affordable hardware.

  3. The study provides the field’s first rigorous benchmarking data for automated versus manual CFE biosensor manufacturing, systematically quantifying where robotic and manual assembly diverge in performance characteristics.

Why Automation Was Essential

Three factors render automation indispensable for this application. First, cell-free reactions are exquisitely sensitive to pipetting precision - small volumetric errors in extract or DNA template concentration produce disproportionately large signal variability, which is unacceptable for a diagnostic biosensor that must deliver a reliable binary answer. The OT-2’s reproducible liquid handling directly addresses this failure mode. Second, scaling from proof-of-concept to deployment requires manufacturing hundreds to thousands of biosensor reactions per batch; manual assembly of 384-well plates is tedious, error-prone, and fundamentally unscalable. Third, the low cost of the OT-2 (~$5,000–$10,000) means that academic laboratories, field-deployed diagnostic operations, and resource-limited clinical settings can adopt semiautomated manufacturing without the capital expenditure of biofoundry-grade liquid handlers such as the Hamilton STAR or Beckman Biomek platforms.

Relevance to My Final Project

This paper is directly relevant to my proposed work on autonomous antimicrobial surfaces for hospital-acquired infection prevention. My project design incorporates both the OT-2 (for lysostaphin zone-of-inhibition assays and serial dilutions) and the Ginkgo Nebula cloud laboratory (for high-throughput toehold switch screening using the Echo → Bravo → PHERAstar workflow). Brown et al. demonstrate precisely the principle that underpins my automation strategy: affordable liquid handling robots can achieve the precision and throughput required to screen synthetic biology constructs at a scale that manual methods cannot support, particularly for cell-free biosensor applications where reaction assembly variability is a primary performance bottleneck. Their validation of OT-2-assembled cell-free biosensor arrays provides direct precedent for my planned Week 11 toehold switch screening experiments on Nebula’s cell-free platform.

Reference: Brown, D.M., Phillips, D.A., Garcia, D.C., et al. (2025). Semiautomated Production of Cell-Free Biosensors. ACS Synthetic Biology, 14(3), 979–986. https://doi.org/10.1021/acssynbio.4c00703

Question 2 : Automation Plan for Final Project

My final project - an engineered Bacillus subtilis surface colonizer for hospital-acquired infection prevention - has two primary components that benefit from automation: antimicrobial peptide optimization and biosensor screening.

Automation Scale 1: OpenTrons OT-2 (Bench-Scale, London Node)

Goal: Serial dilution and zone-of-inhibition assays for lysostaphin antimicrobial activity.

Pseudocode:

PROTOCOL: Lysostaphin Activity Screening
LABWARE:
  - Slot 1: 96-well deep-well plate (lysostaphin variants in columns 1-6)
  - Slot 2: 12-well reservoir (LB broth, S. aureus overnight culture)
  - Slot 4: Tiprack 200 uL
  - Slot 7: Tiprack 20 uL

PROCEDURE:
  # Step 1: Serial dilution of lysostaphin across rows
  FOR each column c in [1..6]:  # 6 construct variants
    pick_up_tip(200uL)
    aspirate(100uL, reservoir['LB'])
    FOR each row r in [B..H]:  # 7-point serial dilution
      dispense(50uL, plate[r][c])
      mix(3, 50uL)              # Mix by pipetting up/down
    drop_tip()

  # Step 2: Add S. aureus to all test wells
  FOR each well in plate[A1:H6]:
    pick_up_tip(20uL)
    aspirate(10uL, reservoir['S_aureus'])
    dispense(10uL, well)
    drop_tip()

  # Step 3: Incubate 16h at 37°C (manual)
  # Step 4: Read OD600 on plate reader (manual)

This protocol generates a dose-response matrix: 6 lysostaphin variants × 7 concentrations × triplicate, with automated serial dilution ensuring precise and reproducible concentration gradients.

Automation Scale 2: Ginkgo Nebula Cloud Lab (High-Throughput)

Goal: Screen toehold switch biosensor library for mecA mRNA detection (connects to Week 11 biosensor module).

Nebula Workflow (RAC Plate Flow):

Step 1: ECHO - Acoustic transfer of 384 toehold switch DNA constructs
        (NUPACK-optimized designs targeting mecA mRNA)
        Source: 384-well plate with switch DNA variants
        Destination: 384-well assay plate
        Volume: 100 nL per well

Step 2: BRAVO - Stamp CFPS reagent master mix into all 384 wells
        Source: Reservoir with PURExpress or custom CFPS lysate
        Volume: 5 uL per well

Step 3: MULTIFLO - Dispense mecA trigger RNA at 4 concentrations
        Quadrants of plate get 0, 10, 100, 1000 nM trigger
        Volume: 2 uL per well

Step 4: PLATELOC - Heat-seal the plate

Step 5: INHECO - Incubate at 37°C for 2 hours
        (Cell-free expression of toehold switch + GFP reporter)

Step 6: XPEEL - Remove seal

Step 7: PHERASTAR - Fluorescence readout (excitation 485nm, emission 520nm)
        Kinetic read: every 10 min for 6 hours
        Output: ON/OFF ratio matrix for all 384 designs

ANALYSIS:
  - Rank switches by ON/OFF ratio (trigger vs. no-trigger)
  - Select top 10 switches for integration into B. subtilis chassis
  - Feed results back to NUPACK for next design round (DBTL cycle)

This cloud lab workflow screens 384 biosensor designs in a single automated run - something that would take months manually. The top-performing switches will be integrated into my B. subtilis chassis to create the sense-and-respond circuit for MRSA detection.

Why Two Scales Matter

The OpenTrons protocol handles the effector optimization (how well does lysostaphin kill S. aureus?), while the Nebula workflow handles the sensor optimization (how sensitively can we detect MRSA?). Together, they close the loop on both halves of the sense-and-respond system - all through automation.

Preliminary Computational Results: Toehold Switch Design for mecA

To validate the feasibility of my biosensor approach, I wrote a Python pipeline to computationally design toehold switch candidates targeting the mecA mRNA of S. aureus N315 (GenBank: D86934). The mecA gene encodes PBP2a, the penicillin-binding protein that confers methicillin resistance - making it the definitive genetic marker for MRSA.

Method

I implemented the Series B toehold switch architecture from Green et al. (2014) Cell 159:925-939, screening 89 candidate positions across the first 303 nucleotides of the mecA coding sequence. Each candidate was evaluated on six criteria: trigger GC content (ideal 35-55%), toehold GC content (ideal 25-50%), absence of homopolymer runs >4 nt, absence of internal AUG codons, estimated stem thermodynamic stability, and predicted ON/OFF ratio.

Key Results

  • 89 candidates screened, of which 30 scored ≥ 10/12 (high-confidence)
  • Best candidate: Position 234, targeting the region encoding the transpeptidase catalytic domain
    • Trigger: 5'─AUAAAUACAGGAACUUCAGCAGCUGAUACAGGAGAU─3'
    • Trigger GC: 38.9% | Toehold GC: 41.7%
    • No internal AUG codons
    • Predicted ON/OFF ratio: 9.1×
    • Maximum homopolymer: 3 nt (synthesis-compatible)
  • The position 150-260 region of mecA contains a cluster of high-scoring candidates, suggesting favorable mRNA secondary structure accessibility in this region

Significance

This computational screen identifies candidate toehold switches ready for experimental validation on Ginkgo Nebula during the Week 11 biosensor module. The top 10 designs can be synthesized by Twist Bioscience and screened in cell-free reactions using the cloud lab workflow described in my automation plan above.

Toehold Switch Design Results Toehold Switch Design Results

Figure: Computational screening of toehold switch biosensor candidates targeting mecA mRNA. (A) Composite score distribution across the mecA 5’ coding region. (B) GC content profiles of top 10 candidates. (C) Predicted ON/OFF ratios. (D) Architecture of the top-ranked candidate at position 234.

Next Steps

  1. Validate top 5 candidates with NUPACK (nupack.org) for full minimum free energy analysis
  2. BLAST trigger sequences against the complete S. aureus transcriptome to confirm specificity
  3. Order synthesis of top 10 switches from Twist Bioscience
  4. Screen in cell-free system on Ginkgo Nebula during Week 11 biosensor module
  5. Integrate the best-performing switch into the B. subtilis sense-and-respond chassis

The complete Python code and candidate data (JSON) are available in my documentation.

Part 3: Three Final Project Ideas

Idea 1 : Cell-Free Lysostaphin Optimization via AI-Guided DBTL on Nebula

What: Optimize recombinant lysostaphin expression in a cell-free protein synthesis system using automated Design-Build-Test-Learn cycles on Ginkgo Nebula’s cloud laboratory.

How: Codon-optimize lysostaphin variants for cell-free expression. Screen hundreds of reaction conditions (DNA concentration, Mg²⁺, temperature, incubation time) in parallel using Nebula’s RAC workflow: Echo acoustic transfer → Bravo master mix stamping → Inheco incubation → PHERAstar fluorescence readout. Validate top candidates via OpenTrons OT-2 serial dilution and zone-of-inhibition assays against S. aureus.

Expected output: Optimized cell-free lysostaphin expression protocol with quantified MIC data against clinical S. aureus isolates.

References: J. Hérisson, A. N. Hoang, A. El-Sawah, M. M. Khalil, J.-L. Faulon bioRxiv 2024.10.28.619828; doi: https://doi.org/10.1101/2024.10.28.619828

Idea 2 : Toehold Switch MRSA Detection Coupled to Antimicrobial Secretion in B. subtilis

What: Design RNA-based toehold switch biosensors targeting mecA mRNA (the methicillin resistance determinant) and integrate them with a T7 RNAP amplification cascade driving lysostaphin secretion in B. subtilis - creating an autonomous sense-and-kill genetic circuit.

How: Computationally screen toehold switch candidates across the mecA coding sequence using thermodynamic scoring (GC content, stem stability, predicted ON/OFF ratio). Validate top designs via cell-free screening on Nebula (Week 11 biosensor module). Integrate a two-stage amplification architecture: toehold switch → T7 RNA polymerase → T7 promoter → lysostaphin, converting weak linear signal into sharp sigmoidal response. Preliminary computational screen: 89 candidates evaluated, top candidate at position 234 with 9.1× predicted ON/OFF ratio.

Preliminary results (completed): I built a computational pipeline implementing the Series B toehold architecture and screened 89 candidate positions across the first 303 nt of mecA from S. aureus N315 (GenBank: D86934). 30 candidates scored ≥ 10/12 on a composite metric. Top candidate at position 234 (transpeptidase catalytic domain): 9.1× predicted ON/OFF ratio, 38.9% trigger GC, no internal AUG codons. These designs are ready for synthesis and experimental validation on Nebula.

Expected output: Validated toehold switch with >10-fold ON/OFF ratio + functional genetic circuit design for B. subtilis integration.

References: Valeri, J.A. et al. (2020). Sequence-to-function deep learning frameworks for engineered riboregulators. Nature Communications, 11, 5058. https://doi.org/10.1038/s41467-020-18676-2

Idea 3 : Deployable Living Antimicrobial Surface with Evolutionary Robustness for Resource-Limited Hospitals

What: Engineer a complete B. subtilis chassis that autonomously detects, reports, and kills antibiotic-resistant pathogens on hospital surfaces - designed for deployment across 25,000 Indian government hospitals where HAI rates exceed 30% in ICU settings.

How: Three integrated modules: (1) Sensing - multiplexed toehold switches detecting resistance markers for MRSA, VRE, and CRKP with T7 RNAP signal amplification; (2) Response - modular effector library (lysostaphin for MRSA, dispersin B for biofilms, Art-175 for Gram-negatives) with toxin-antitoxin addiction systems to enforce evolutionary stability; (3) Biocontainment - spo0A knockout (prevents sporulation) + synthetic amino acid auxotrophy; (4) Surveillance - MinION nanopore sequencing of the surface resistome to guide effector selection over time.

Expected output: Complete system design, computational modeling, and cell-free proof-of-concept for one sensor-effector pair. Deployable prototype targeting <$0.10/m² manufacturing cost.

References: Mehta, A., Rosenthal, V.D., Mehta, Y. et al. (2007). Device-associated nosocomial infection rates in intensive care units of seven Indian cities: Findings of the International Nosocomial Infection Control Consortium (INICC). Journal of Hospital Infection, 67(2), 168–174. https://doi.org/10.1016/j.jhin.2007.07.008