Week 3 HW: Lab Automation

HTGAA Week 03 - Lab Automation

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

https://www.nature.com/articles/s42003-019-0305-x?utm_source=chatgpt.com

This paper used a Tecan liquid handler to enable high-throughput patient-derived tumor organoid drug screening, moving beyond pipetting into a biologically novel precision medicine application. The researchers wanted to determine whether patient-derived tumor organoids (miniature 3D tumors grown from real patient cancer tissue) could be used to rapidly identify effective anti-cancer drugs for individual patients. The Tecan system automated several otherwise tedious and error-prone steps: precise dispensing of organoid cultures into multiwell plates, automated addition of large drug libraries at multiple concentrations, and standardization of timing and reagent handling to reduce variability between samples. This helped make organoid-based drug screening much more scalable as a workflow for research.

Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more.

One possible application would involve automated liquid handling platforms, such as Tecan or Opentrons systems, to standardize microbial growth assays and peptide screening. For example, engineered bacterial strains expressing candidate antifungal peptides (dermaseptins or related amphibian antimicrobial peptides) could be cultured in multiwell plates and automatically co-incubated with fungal models or Batrachochytrium dendrobatidis analog systems. Optical density measurements and inhibition assays would improve experimental consistency while enabling larger-scale screening of secretion strategies with varying peptides. Computational automation could also support the project through Python-based analysis pipelines. Example pseudocode may be developed to automate experimental data processing, including colony growth quantification, fluorescence analysis of GFP expression, inhibition zone measurement from plate images, and statistical comparisons between engineered strains. Computer vision tools could also potentially be used to quantify fungal inhibition from photographs of agar plates, reducing subjectivity in phenotype scoring.

Opentrons python CoLab Link: https://colab.research.google.com/drive/1cCUVts6OrGXWZABfKc7FsJzPb-HbeuaY#scrollTo=J147sPzt2rJL