Abstract: Biological and living organisms sense and process information from their surroundings, typically having access only to a subset of external observables for a limited amount of time. In this Letter, we uncover how biological systems can exploit these accessible degrees of freedom to transduce information from the inaccessible ones with a limited energy budget. We find that optimal transduction strategies may boost information harvesting over the ideal case in which all degrees of freedom are known, even when only finite-time trajectories are observed, at the price of higher dissipation. We apply our results to red blood cells, inferring the implemented transduction strategy from membrane flickering data and shedding light on the connection between mechanical stress and transduction efficiency. Our framework offers novel insights into the adaptive strategies of biological systems under nonequilibrium conditions.

Abstract: Living systems are maintained out of equilibrium by external driving forces. At stationarity, they exhibit emergent selection phenomena that break equilibrium symmetries and originate from the expansion of the accessible chemical space due to nonequilibrium conditions. Here, we use the matrix-tree theorem to derive upper and lower thermodynamic bounds on these symmetry-breaking features in linear and catalytic biochemical systems. Our bounds are independent of the kinetics and hold for both closed and open reaction networks. We also extend our results to master equations in the chemical space. Using our framework, we recover the thermodynamic constraints in kinetic proofreading. Finally, we show that the contrast of reaction-diffusion patterns can be bounded only by the nonequilibrium driving force. Our results provide a general framework for understanding the role of nonequilibrium conditions in shaping the steady-state properties of biochemical systems.

Abstract: Complex systems are characterized by multiple spatial and temporal scales. A natural framework to capture their multiscale nature is that of multilayer networks, where different layers represent distinct physical processes that often regulate each other indirectly. We model these regulatory mechanisms through triadic higher-order interactions between nodes and edges. In this work, we focus on how the different timescales associated with each layer impact their reciprocal effective couplings. First, we rigorously derive a decomposition of the joint probability distribution of any dynamical process acting on such multilayer networks. By inspecting this probabilistic structure, we unravel the general principles governing how information propagates across timescales, elucidating the interplay between mutual information and causality in multiscale systems. In particular, we show that feedback interactions, i.e., those representing regulatory mechanisms from slow to fast variables, generate mutual information between layers. On the contrary, direct interactions, i.e., from fast to slow layers, can propagate this information only under certain conditions that depend solely on the structure of the underlying higher-order couplings. We introduce the mutual information matrix for multiscale observables to capture these emergent functional couplings. We apply our results to study archetypal examples of biological signaling networks and effective environmental dependencies in stochastic processes. Our framework generalizes to any dynamics on multilayer networks, paving the way for a deeper understanding of how the multiscale nature of real-world systems shapes their information content and complexity.