Assembly Line

Artificial Intelligence for Synthetic Biology

📅 Date:

✍️ Authors: Mohammed Eslami, Aaron Adler, Rajmonda S. Caceres, Joshua G. Dunn

🔖 Topics: Synbio

Synthetic biology (synbio) aims to design biological systems to a specification (for example, cells that produce a desired amount of biofuel, or that react in a specific manner to an external stimulus). To this end, synthetic biologists leverage engineering design principles to use the predictability of engineering to control complex biological systems. These engineering principles include standardized genetic parts, and the Design-Build-Test-Learn (DBTL) cycle, iteratively used to achieve a desired outcome.

Synbio is primed to have a transformative impact on every activity sector in the world: food, energy, climate, medicine, and materials. Synbio has already produced insulin without the need to sacrifice pigs for their pancreases (in a previous stage, as genetic engineering), synthetic leather, parkas made of spider silk that have never seen a spider, antimalarial and anticancer drugs, meatless hamburgers that taste like meat, renewable biofuels, hoppy flavored beer produced without hops, the smell of extinct flowers, synthetic human collagen for cosmetic applications, and gene drives to eliminate dengue-bearing mosquitos. Many believe this is just the tip of the iceberg because the ability to engineer living beings provides seemingly unlimited possibilities, and there is a growing level of investment, both public and private, in this field.

Read more at Communications of the ACM

Fast and Efficient Plastic-Degrading Enzyme Developed Using AI

📅 Date:

✍️ Author: Julianna LeMieux

🔖 Topics: Synbio, Recycling

🏢 Organizations: University of Texas

Plastic waste build-up in the environment is an enormous ecological challenge. Indeed, 40% of plastic waste goes around collection systems and ends up residing in natural environments. Polyethylene terephthalate (PET) accounts for 12% of global solid waste. Enzymes that break down PET, PET hydrolases, have been previously developed but suffer from practical limitations with slow reaction rates and specific pH and temperature ranges.

Now, researchers have used a structure-based, machine learning algorithm to engineer a robust and active PET hydrolase. The enzyme, FAST-PETase (functional, active, stable, and tolerant PETase), can break down environment-throttling plastics that typically take centuries to degrade in just a matter of hours and days.

Read more at Genetic Engineering & Biotechonology News