I rebuilt two published CHO cell-culture papers — a hybrid ODE + machine-learning growth model, and a genome-scale metabolic reduction pipeline — as an interactive digital twin that runs in the browser. Here’s how it works, what’s under the hood, and an honest take on what it’s good for. No hosted version yet; the code is on GitHub and a live instance is coming soon.
If you’ve ever read a paper on lysine production with Corynebacterium glutamicum and thought “right, but what do I actually do on Monday morning when I’m standing in front of the bioreactor” — this post is for you. I’m going to walk through the entire process of setting up and running a 5L bench-scale fed-batch… Read More: Running a 5-Litre Lysine Fermentation From Scratch. Everything Nobody Tells… »
Look, I’ll be honest. For years I ran bioreactors the way most of us do: set your temperature, set your pH, watch the DO trace, take a sample every few hours, and hope for the best. You tweak the feed rate because “it feels right.” You crank up the stirrer because the DO is dropping.… Read More: Why I Stopped Trusting My Gut (and Started Trusting My… »
Why Trastuzumab Is the Perfect Test Case When I built my antibody developability predictor, I knew I needed to validate it against a molecule where we actually know the manufacturing story. Trastuzumab (Herceptin) was the obvious choice. It’s one of the most successful therapeutic antibodies ever made, with decades of manufacturing data behind it. More… Read More: Case Study: Predicting Trastuzumab Developability »
A bioprocess engineer’s journey into machine learning and why the pharmaceutical industry desperately needs this bridge When I tell people I work in bioprocess engineering, I usually get blank stares. When I explain that I help manufacture proteins in giant tanks for therapeutic use, the response is often: “Oh, like brewing beer?” Not quite. But… Read More: How I Built a Machine Learning Tool to Predict Drug… »