The physics of active biological matter, such as bacterial colonies and bird flocks, exhibiting interesting self-organizing dynamical behavior has gained considerable importance in recent years. Current theoretical advances use techniques from hydrodynamics, kinetic theory, and non-equilibrium statistical physics. However, for biological agents, these approaches do not seem to recognize explicitly their critical feature: namely, the role of survival-driven purpose and the attendant pursuit of maximum utility. Here, we propose a game-theoretic framework, statistical teleodynamics, that demonstrates that the bird-like agents self-organize dynamically into flocks to approach a stable arbitrage equilibriumof equal effective utilities. This is essentially the invisible handmechanism of Adam Smith’s in an ecological context. What we demonstrate is for ideal systems, similar to the ideal gas or Ising model in thermodynamics. The next steps would involve examining and learning how real swarms behave compared to their ideal versions. Our theory is not limited to just birds flocking but can be adapted for the self-organizing dynamics of other active matter systems.

Bioprocesses are inherently multiscale, spanning intracellular metabolism to production-scale reactors. Simulation models that integrate these scales offer potential strategies to study the effect of changing metabolic states and enable efficient integration of biological knowledge gathered from lab-scale experiments. In this study, we demonstrate the potential of such simulation model towards the production of mevalonate, an important pharmaceutical drug compound produced through fermentation of a fungal species Aspergillus terreus. We integrate a genome-scale metabolic model of the organism with a plant-wide simulation model for the bioprocess that encompasses several upstream and downstream unit operations. Through this integration, we identify potential targets for metabolic engineering towards increased product flux and simultaneously estimate the associated oxygen requirements. This framework serves as a foundation for developing digital twins of bioprocesses that bridges strain engineering with process design and operations.