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Tan, Stephen

Publications and source records attributed to Tan, Stephen.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Carruthers, David N↗
DOE OSTI2025

EcoBOT: an AI/ML enabled automated phenotyping capability for model plants

Introduction: Advances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools. Methods: The EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated. Results: The results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement. Discussion: The findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.

AI image analysis↗
DOE OSTI2025

High-Throughput Microfluidic Electroporation (HTME): A Scalable, 384-Well Platform for Multiplexed Cell Engineering

Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME's core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform's ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME's potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck.

Gaillard, William R↗
DOE OSTI2024

General Applications for Hamilton Vantage (GenApps for Vantage) v0.6.1

General applications for Hamilton Vantage is a flexible liquid handling method used to automate the most widely applicable types of automated liquid transfers. General applications can also be used as a tool to onboard new fully-automated workflows by breaking them down step by step into single transfers. The goal of General Applications is to make using the Hamilton Vantage liquid handler as easy & practical as using a handheld pipette for the end user. The software supports plate-to-plate transfers for a variety of method types including: Stamp: One-to-One, Stamp: One-To-Many, Split, Combine, Hitpick, and Qtray plating. General Applications eliminates the need for automation engineers to customize individual methods for each new workflow that gets onboarded. Steps can be customized within GenApps according to the needs of the researcher. The software utilizes a GUI to prompt the users to input variables – Allowing for flexible control over plate types, transfer volumes, number of replicates, tip types, liquid classes, mixing steps, aspiration/dispense heights and more. General Applications also generates a deck image and setup instructions to guide the researcher on how to load the deck and start the instrument.

Yoder, Sam↗