Pre-Conference Workshops
Tuesday, March 26, 2024

Workshop A: 9am – 12pm

Advancing Ultra High Throughput Techniques to Enhance Phenotype Selectivity With a Speedy Turnaround

  • Hal Alper Professor of Chemical Engineering, University of Texas at Austin
  • Daniel Winter Protein Science Lead, All G Foods
  • Neil Adames Senior Scientist I, Strain Engineering, New Culture
  • Maciej Hołówko Head of Biofoundry, Nourish Ingredients


High throughput screening techniques could be the perfect solution to your engineering troubles, but there are still leaps and bounds to be made in satisfying your strain engineering requirements.

Accommodating the difficulties in selecting the strain that’s producing your ideal quantity and quality of protein, this workshop is here to empower your screening approach and free up time spent during strain engineering.

Join this workshop on high throughput to:

  • Adopt the capability of workflow to build a strong library of strains fit for screening
  • Discover how to rely on signal-to-noise ratio during strain selection to enable confidence in strain comparison for high productivity results
  • Deploy robust screening to select phenotypes producing the protein of interest with stable profiles fit for scaleup

Workshop B: 1pm – 4pm

Uncovering Data-Informed Machine Learning to Power Your Strain Optimization


Amidst the current landscape of automation, AI and digital progression, it’s become well established that machine learning can be used for predictive advantages. But how can strain optimization experts leverage this to their benefit?

Join this workshop to learn from data science and ML experts to:

  • Explore what’s realistic from a strain optimization standpoint in leveraging predictive tools to make selective gene edits
  • Learn to navigate large quantities of quality data to bridge the gap between computation and strain optimization, minimizing trial and error
  • Leveraging design-build-test-learn cycles applied to machine learning to speed up decision making in strain selection and gene editing