Week 3 Lab: Lab Automation

OpenTron Designs

I tried to push OpenTron to the limit and chose a fairly hard design. Specifically, I chose the Mitsudomoe design, which is a type of “Kamon” or traditional family crest, associated with my family. The design didn’t come out particularly well but with higher resolution and/or non-sequential pipetting (for speed) it would be a more tractable design.

Mitsudomoe Design & OpenTron Version

Lab Automation

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

The paper I chose is “AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots” by Bryant et al., published in Synthetic Biology (2023). The authors developed an open-source Python package called AssemblyTron that connects j5 DNA assembly design software to an Opentrons OT2 liquid handling robot, allowing users to go from a digital DNA design to a physically assembled construct with minimal hands-on work.

What makes this paper compelling is that it automates the entire “Build” step of the Design–Build–Test–Learn cycle, which is traditionally the most manual and error prone part. AssemblyTron handles PCR setup (including calculating optimal annealing temperature gradients), DpnI digestion, and final multi-fragment assembly all on the OT2. The authors validated the system by performing Golden Gate assemblies and in vivo assemblies of four fragment chromoprotein reporter plasmids, achieving fidelity comparable to manual assembly. They also demonstrated automated site directed mutagenesis. The key takeaway is that affordable, open source automation can make DNA assembly more reproducible, less wasteful, and accessible to labs without expensive biofoundry infrastructure.

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. You may reference this week’s recitation slide deck for lab automation details.

In general, I want to use my adaptive AI system for scientific discovery at a small scale, something realistic as a final project given the resources we have from Twist and Ginkgo Bioworks.

My first idea is a promoter design project to maximize expression. I would order oligos from Twist, clone them into reporters, and observe expression in E. coli. Fluorescence intensity would be recorded as the reward signal. I could possibly do two rounds of this.

As a second idea, the most feasible version would be to ditch the lab-in-the-loop entirely by performing validation in silico. This would also allow for much more complex protein designs since there wouldn’t be a constraint on what is physically feasible to test given the project budget.

As an ideal final project, which is totally not doable in this timeframe or budget, I would use my system to discover higher order transcription factor combinations that forward program iPSCs into a target cell type. The computational engine uses Bayesian optimization to predict TF combinations, balancing exploration and exploitation based on experimental results. To handle the cloning overhead, I would outsource synthesis of polycistronic lentiviral transfer vectors to Ginkgo Bioworks’ Nebula platform, which algorithmically assembles the DNA and returns plasmids in a high throughput 96 well format. Each vector can carry 3 to 4 TFs linked by 2A peptides, and co-transduction with multiple vectors allows testing of even larger combinations.

The OT-2 would then automate lentivirus production by dispensing transfection reagent into arrayed HEK293T packaging cells, harvesting viral supernatant, and transducing iPSC cultures. The robot would also handle the media change schedule post transduction. Because lentivirus integrates into the genome, TF expression is sustained throughout the differentiation window without repeated dosing. At the endpoint, high content phenotypic imaging quantifies differentiation efficiency in each well, and this data feeds directly back into the Bayesian model to predict a more refined batch of TF cocktails for the next automated run.