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Templeton.org is in English. Only a few pages are translated into other languages.

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Both the physical and the biological worlds produce emergent structures and dynamics at multiple scales. In self-driven active matter, the energy injected at smaller scales by individual biotic or abiotic components can self-organize into collective work at larger scales. In evolving living systems, functioning components combine into modules that in turn organize into more complex structures at higher levels. Both processes can repeat many times and span multiple spatiotemporal scales, from molecular actin-myosin interactions driving cell motion to specialized organisms behaving as modular components of adaptable ecologies.

This project aims to develop a theoretical framework to address the overarching question: what is the role of multiscale structures and dynamics in the drive towards increasing levels of self-organized complexity observed in active matter and living systems?

Our activities will follow two complementary tracks. The first will use agent-based simulations, data, and theory to study how the focusing of energy and information into collective modes produces coherent active dynamics. The second will use evolutionary models of Boolean and adaptive networks, existing datasets, and artificial life simulations to explore the origins and consequences of multiscale modular order in biological networks.

By advancing a new perspective on the role of multiscale processes in self-organization, we aim to produce groundbreaking research that changes our view of emergent phenomena across multiple disciplines. We will develop new theoretical descriptions of active systems (from collective molecular motion to animal swarms) and of multiscale modularity (from metabolic processes to ecologies). This will help formulate testable hypotheses for future experiments and build urgently needed conceptual foundations to interpret large complex datasets and manage and control complex systems with a multiscale understanding of their underlying organizational principles.