Bringing whole-cell models to life: Development of physics-based AI for accelerated modeling

A major challenge in synthetic biology is constructing cells that autonomously grow, divide and adapt. Ribogenesis and nucleoid physical architecture are critical for this goal: together, they coordinate spatial organization and self-assembly of molecules in crowded cytoplasm, enabling complexification of matter into life. Synthetic systems lack this cytoplasmic spatial architecture, suggesting that sustained growth requires specific physical architecture. Here we identify specific physical conditions in cells that are essential to ribogenesis. To do so, we will use AI-enhanced multi-scale pipelines to expand our whole-cell models. We will use the resulting framework to achieve autonomous self-assembly of the ribosome's 30S subunit and the principles learned to improve autonomous growth outcomes for synthetic cells.