Week 2 HW: Lab Automatation

Post Lab - Question

I. Find and describea published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

I used a 2025 research report titled “Open Source Cell Culture Automation System with Integrated Cell Counting for Microplate Culture Grading,” which features the Automated Cell Culture Divider (ACCS). This system uses an Opentrons OT-2 liquid handling robot to solve the challenge of manually grading cells into 96-well microplates, a tedious and error-prone process.

New Biological Application: The ACCS The ACCS’s main innovation is the integration of a custom cell count imager (CCI) into the platform. While standard automation often relies on fixed dilution factors, this system measures cell concentrations in real time to adjust seeding volumes per well.

Key Features and Impact: • Precision Seeding: The system achieves a well-to-well coefficient of variation (CV) of less than 11%, significantly outperforming manual methods where visual estimation of confluence leads to inconsistencies. • High-performance imaging: A key driver for this tool was the OpenCell project, which requires optimal and uniform cell density for live 3D fluorescence imaging. Plates seeded with ACCS achieved a 92% preservation rate at usable imaging sites, compared to only 52% with manual seeding.

• Efficiency: The system reduces intervention time by 61%, allowing researchers to be offline for nearly three hours during a run.

• Open-source design: To facilitate community adoption, the authors released all hardware designs (e.g., 3D-printed waste containers and tilt plate adapters) and Python-based software under open-source licenses.

Question 2: Description of your Final Project (Write a description)

Final Project Description: Automation of the “Bricolage” Inoculum and Nash Automaton a. Overview and Cloud Synthesis

The central objective of the project, titled “Lake Bio-Synthetic Bridge,” is to develop a multi-species consortium for the bioremediation of Lake Budi, mitigating eutrophication and system collapse. The first phase of automation will leverage cloud-based laboratory platforms (such as Ginkgo Nebula) for the high-throughput synthesis of our “Bioremediation Logic Circuit.” This in vivo circuit will operate with a strict AND logic gate, activating hydrolase expression only in the simultaneous presence of skatole and hydrogen sulfide (H2S). Additionally, we will commission the synthesis of variant libraries for our biosecurity module, which uses the MazF/MazE toxin-antitoxin (Kill-Switch) system to induce programmed cell death if microorganisms escape the target chemical niche.

b. Physical Platform and Custom Hardware For experimental validation, we will design 3D-printed holders compatible with the Opentrons OT-2 robot deck. These holders will house microplates where we will deposit our physical matrix of pumice and Bokashi. This matrix will act as a low-entropy skeleton to anchor the native halotolerant bacteria (“chassis”), transforming the physical support into a biological processor. The use of the 3D-printed holders will ensure that the liquid handling robot can interact precisely with a semi-solid matrix without damaging the pipette tips.

c. The “Nash Automaton”: Environmental Stress Automation

The core of the robotic automation will consist of performing iterative metabolic tests to subject the native consortium to automated artificial selection. Using the Opentrons OT-2, we will program pipetting routines that distribute precise gradients of critical environmental stressors from Lake Budi (Salinity, Nitrogen, Phosphorus, H2S) across the 96-well plate. The robot will inoculate the different edited bacterial strains into these stress matrices. The goal of this automation is to physically execute a Minimax algorithm: subjecting the bacteria to the worst possible environmental stress scenario to select the genetic variant and consortium proportion that best withstands and stabilizes the system.

d. Data Reading and Feedback Loop: After automated incubation on the robot’s deck (using temperature control modules), the plate will be sealed and transferred to a high-throughput microplate reader (e.g., PHERAstar) to measure metabolic performance and the fluorescent signals associated with AND gate activation. This data will feed our population models (Lotka-Volterra) to refine the master inoculum in the next robotic iteration, forcing obligate syntrophy until a biological Nash equilibrium is reached.

e. Opentrons Code

‘from opentrons import protocol_api

metadata = { ‘apiLevel’: ‘2.13’, ‘protocolName’: ‘Nash Automaton: Minimax Stress Matrix’, ‘author’: ‘HTGAA Student’, ‘description’: ‘Automated distribution of stressors for consortium selection.’ }

def run(protocol: protocol_api.ProtocolContext):

1. Load Labware and Hardware

Custom 3D printed holder for the pumice/Bokashi matrix plate

benthic_plate = protocol.load_labware(‘corning_96_wellplate_360ul_flat’, ‘1’) stressors_res = protocol.load_labware(’nest_12_reservoir_15ml’, ‘2’) inoculum_res = protocol.load_labware(’nest_12_reservoir_15ml’, ‘3’) tiprack = protocol.load_labware(‘opentrons_96_tiprack_300ul’, ‘4’)

Instruments (P300 Single Channel Gen2)

p300 = protocol.load_instrument(‘p300_single_gen2’, ‘right’, tip_racks=[tiprack])

2. Define Reagents

h2s_toxin = stressors_res.wells()[0] master_inoculum = inoculum_res.wells()[0]

3. Execution: Nash Automaton Logic (Safe Gradient)

Start at 10uL, increase by 2.5uL per well to avoid P300 and well overflow

Max volume: 10 + (95 * 2.5) = 247.5uL (Safe for 360uL well)

current_vol = 10 vol_increment = 2.5

protocol.comment(“Starting automated distribution of H2S stress gradient…”)

p300.pick_up_tip() for well in benthic_plate.wells(): p300.transfer(current_vol, h2s_toxin, well, mix_after=(2, 20), new_tip=‘never’) current_vol += vol_increment p300.drop_tip()

4. Final Inoculation

protocol.comment(“Inoculating the Lacustrine Consortium into the stress matrix…”) p300.pick_up_tip() for well in benthic_plate.wells(): # Adding 25uL of the master inoculum p300.transfer(25, master_inoculum, well, new_tip=‘never’) p300.drop_tip()

protocol.comment(“Nash Automaton setup complete. Plate ready for incubation.”)

f. Parallel Strategies in Automation The ACCS system provides information and technical concepts that can improve the understanding and optimization of a workflow such as biosensor screening:

• Accuracy and Consistency: Just as Echo is used for high-precision construct transfer, ACCS was specifically designed to address the reproducibility issues of manual estimation. By integrating a custom cell count imager (CCI), the system controls seeding density with a coefficient of variation (CV) of less than 11%, significantly outperforming manual methods.

• Protocol Simulation and Safety Checks: Your complex multi-instrument workflow could benefit from the ACCS offline analysis tool. This tool allows researchers to simulate protocols to verify final liquid volumes, mixtures, and pipette tip usage before the actual analysis, a crucial verification step for high-throughput campaigns.

• Environmental Management: In your workflow, Inheco incubates the plate at 37°C. The ACCS addresses the challenge of keeping cells out of the incubator for 2–3 hours by proactively including HEPES as a pH buffer in the growth medium. This suggests that, for cell-free synthesis (CFPS), monitoring environmental exposure during transit between Multiflo and PlateLoc is critical for maintaining consistency across the entire array.

• Communication and Monitoring: A key feature of the ACCS is its “alert bot” program, which transmits critical log events and status updates to a dedicated Slack channel. In a multi-step cloud lab process involving sealing (PlateLoc) and peeling (XPeel), an automated notification system would be vital to alert you to the completion of the synthesis phase or any mechanical errors in the sequence.

g. Hardware and Workflow Optimization The sources describe several “custom hardware accessories” that facilitate the movement of plates and reagents, similar to the functions performed by Bravo and Multiflo:

• Reagent Access: The ACCS uses a reagent channel lift to raise reagents and facilitate access to pipettes. In its workflow, optimizing the height and position of the CFPS lysate for the Multiflo would be a comparable mechanical optimization to ensure efficient operation without manual intervention.

• Dispensing Techniques: To protect delicate biological layers, the ACCS uses an 8° angled adapter that allows the robot to dispense liquids against the side wall of the well rather than directly onto the sample. This technique could be relevant when the Multiflo dispenses CFPS lysate to ensure that the biosensor constructs and previously transferred cofactors mix smoothly and completely without detachment.

h. Cites

Courville, G., Vaid, S., Toruño, A., Lebel, P., Cabrera, J. P., Raghavan, P., … & Gómez-Sjöberg, R. (2025). Open-source cell culture automation system with integrated cell counting for passaging microplate cultures. PNAS nexus, 4(12), pgaf385.

i. Sobre el uso de inteligencia artificial For the development of the Opentrons script, the Gemini language model (Google) was used as a technical review assistant. The AI ​​processed the biological requirements of the Nash Automaton and compared them with the precision and hardware design standards of the ACCS system (Courville et al., 2025), optimizing the stress gradients and ensuring the volumetric safety of the protocol.