This study uncovers how cells adapt to environmental changes by modulating transcriptional organization,

a process fundamental to cellular specialization, epigenetic memory, and resilience. By combining bioengineering with mathematical and computational modeling, we focus on specialization of mitochondrial function in adipose tissue mesenchymal stem cells (AT-MSCs), which is crucial for metabolic fitness and disease resilience.

We find that cooperative nuclear organization, intermittent energetic state variations, and nutrient-induced pH fluctuations drive adaptive nuclear compartmentalization and epigenetic memory, leading to robust cellular specialization and resilience.

We formalize operational principles using a multi-scale mathematical framework (using algebraic topology and category theory), which capture adaptive organizations observed in biological systems. Conceptually, we use Maxwell's demon thought experiment to show how oscillations between compartmentalized and non-compartmentalized states enable recognition, optimization, and self-organization based on input features.

Computationally, we implement this in a novel machine learning algorithm, which exhibits adaptive, generative, and self-supervised behavior. Our AI system can memorize, organize, reconstruct, and abstract datasets in several tasks. It also leverages adaptive compartmentalization and accumulation of residuals - analoguous to epigenetic memory marks - to modulate self-organization and memory of specialization.

Theses findings offer foundational insights into cellular adaptation and bioengineering mechanisms for therapeutic applications. They also present a novel approach for computing natural and biological intelligence for adaptive machine learning. This approach has significant implications for the future of AI, potentially leading to more resilient, efficient, and intelligent systems capable of dynamic learning, specialization, and adaptation to fluctuating environments - much like cellular systems.