Jordi Piñero, Ricard Solé, and Artemy Kolchinsky
Phys. Rev. Research 6, 013275

Harvesting free energy from the environment is essential for the operation of many biological and artificial systems. We use techniques from stochastic thermodynamics to investigate the maximum rate of harvesting achievable by optimizing a set of reactions in a Markovian system, possibly under various kinds of topological, kinetic, and thermodynamic constraints. This question is relevant for the optimal design of new harvesting devices as well as for quantifying the efficiency of existing systems. We first demonstrate that the maximum harvesting rate can be expressed as a constrained convex optimization problem. We illustrate it on bacteriorhodopsin, a light-driven proton pump from Archaea, which we find is close to optimal under realistic conditions. In our second result, we solve the optimization problem in closed-form in three physically meaningful limiting regimes. These closed-form solutions are illustrated on two idealized models of unicyclic harvesting systems.

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Giona Casiraghi, Georges Andres
Changes in the timescales at which complex systems evolve are essential to predicting critical transitions and catastrophic failures. Disentangling the timescales of the dynamics governing complex systems remains a key challenge. With this study, we introduce an integrated Bayesian framework based on temporal network models to address this challenge. We focus on two methodologies: change point detection for identifying shifts in system dynamics, and a spectrum analysis for inferring the distribution of timescales. Applied to synthetic and empirical datasets, these methologies robustly identify critical transitions and comprehensively map the dominant and subsidiaries timescales in complex systems. This dual approach offers a powerful tool for analyzing temporal networks, significantly enhancing our understanding of dynamic behaviors in complex systems.

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Wagner, N.

Systems 2024, 12(3), 96

Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach.

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Tim Bayne, Anil K. Seth, Marcello Massimini, Joshua Shepherd, Axel Cleeremans, Stephen M. Fleming, Rafael Malach, Jason B. Mattingley, David K. Menon, Adrian M. Owen, Megan A.K. Peters, Adeel Razi, Liad Mudrik

Trends in Cognitive Science

Which systems/organisms are conscious? New tests for consciousness (‘C-tests’) are
urgently needed. There is persisting uncertainty about when consciousness arises in
human development, when it is lost due to neurological disorders and brain injury,
and how it is distributed in nonhuman species. This need is amplified by recent and
rapid developments in artificial intelligence (AI), neural organoids, and xenobot
technology. Although a number of C-tests have been proposed in recent years, most
are of limited use, and currently we have no C-tests for many of the populations in
which they are most urgently needed. Here, we identify challenges facing any attempt
to develop C-tests, propose a multidimensional classification of such tests, and identify
strategies that might be used to validate them.

Read the full article at: www.cell.com