So, EXPERIMENTS STILL MATTER! 🧪👩‍🔬👨‍🔬

AI & automation can support wet lab researchers—but skilled, creative scientists will always be at the heart of discovery.

#SynBio #Biotech #ScienceMatters

Instead, Salis calls for mechanistic ML models trained on well-defined, carefully annotated experiments.

AI is a tool—but not a replacement for rigorous, hypothesis-driven science.

One key takeaway from the @Nature discussion comes from Howard Salis, developer of the RBS Calculator. He warns against over-reliance on AI in biological design:

“It’s not science anymore—it’s literally a black box.” 🤖⚠️

For practical biotech applications nowadays, my take? Keep things simple.

Find nature’s best solution for a given problem and make only the minimal necessary genetic modifications to get the desired outcome.

Despite these challenges, the Design-Build-Test-Learn cycle in chassis strain development is rapidly advancing our understanding of fundamental biological principles. 🚀🔍

We are not just engineering life—we are learning from it.

As we dive deeper, we’re realizing that bacterial genomes are far more crowded than expected, packed with overlapping coding & regulatory sequences. 🧬

Life’s complexity continues to surprise us—but maybe it shouldn’t. After all, we’re up against billions of years of evolutionary fine-tuning.