Week-3-hw-lab-automatition

‘code’ metada = { ’ protocolName’: ‘Opentrons artistic draeing’ ‘Author’: ‘Ana/HTGAA student’ ‘description’:’ A protocol to draw designs unsing GUI coordinates or math.’, ‘aiplevel’: 2.13 } def run(protocol: protocol_api.ProtocolContext): TIPRACK_SLOT = ‘1’ CANVAS_SLOT =‘3’ PIPETE_MOUNT =‘LEFT’ tiprack = protocol.load.labware(’ opentrons_96_tiprack_300ul’, TIPRACK_SLOT) canvas = protocol.load.labware(’ opentrons_96_wellplate_360_flat’. CANVAS_SLOT) pipette = protocol.load.instrument(‘p300_single_gene2’ PIPETTE_MOUNT, tip_racks=[tiprack])

points = [ (10,10), (20,50), (80,50) (exemples) ] def dwaw_path(coords): if not coords: return pipette.pick_up_tip

first_pt = coords[0] start_ location = canvas. well()[0].move(protocol_api.labware.Point(x=first_pt[0], y=first_pt[1], z=2)) pipette.move_to(start_locatiom) pipette.move_to(start_locatiom.move(protocol_api.labware.Point(z= -2)))

for x,y in coords[1:]:
  target = canvas.well()[0].top().move(protocol_api.labware.Point(x=x, y=y,z= -2))
     pipette.move_to(target)
  pipette.move_to(target. move(protocol_api.labeware.Point(z=10)))

draw_path(points)

  1. Published Paper: AssemblyTron (2023) Paper Title: AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots Journal: Synthetic Biology (Oxford Academic) Authors: Bryant et al. Description and Novelty AssemblyTron is an open-source Python package that bridges the gap between DNA design software (like j5) and the physical execution on an Opentrons OT-2. Historically, “building” DNA (assembling multiple fragments into a functional plasmid) was a manual bottleneck that required hours of tedious pipetting and was prone to human error. Novel Biological Application: The researchers utilized the tool to automate complex scarless, multipart DNA assemblies and site-directed mutagenesis. Specifically:Optimal Annealing Calculation: It uses an algorithm to calculate the best temperatures for PCR across different fragment lengths simultaneously.Complex Assembly: It successfully performed Golden Gate assembly and in vivo assemblies (IVA) with a fidelity (accuracy) comparable to expert human scientists.Significance: By making the “Build” step of the Design-Build-Test-Learn (DBTL) cycle hands-free and open-source, they demonstrated that high-end synthetic biology can be done at a fraction of the cost of traditional proprietary industrial platforms.