José Ignacio Arroyo, Amahury J. Lopez-Diaz, Alejandro Maass, Carlos Gershenson, Pablo Marquet, Geoffrey West, Christopher P. Kempes

Understanding the extent to which genetic correlations change in response to environmental factors, such as temperature, is a poorly explored question, despite the importance of understanding how different processes will change with climate warming. Despite correlations between thermal performance traits having been reported in the literature for a few taxa and performance tasks, such as population growth rate, a comprehensive global analysis of the entire tree of life and multiple performance tasks remains an open challenge. To advance in this open question, we compile a database of 1,300 thermal response curves, encompassing 38 variable types related to individuals’ performance (including per capita population growth rate, photosynthetic rate, among others) and 1,125 different species, ranging from viruses to mammals, encompassing all major lineages of the tree of life. Our analysis reveals that among all possible relationships between traits and optimal performance, four traits form a line with a high goodness-of-fit, while the remaining traits exhibit a polygonal pattern, either a triangle or a tetrahedron. We derive a thermodynamic framework that explains the relationships described by a curve or line (as opposed to a surface or polygon), highlighting the linear relationship between maximum and minimum temperatures, as well as between maximum and optimum temperatures. We also discuss other generic trait evolution models, which could account for the other significant sublinear relationships, as well as the more general model, Pareto optimality theory, which could account for relationships in the form of lines or polygons. Our theoretical framework and empirical evidence suggest that, based on a single data point (e.g., minimum temperature), all critical temperature limits and maximum performance boundaries can be predicted using the estimated parameter from this study. Our results reveal universal scaling relationships in thermal performance, which could be useful for predicting changes in performance under scenarios of climate warming.

Read the full article at: www.biorxiv.org

See Also: A database of biological thermal performances

Xiangyi Meng, Benjamin Piazza, Csaba Both, Baruch Barzel, Albert-László Barabási

The brain’s connectome and the vascular system are examples of physical networks whose tangible nature influences their structure, layout, and ultimately their function. The material resources required to build and maintain these networks have inspired decades of research into wiring economy, offering testable predictions about their expected architecture and organisation. Here we empirically explore the local branching geometry of a wide range of physical networks, uncovering systematic violations of the long-standing predictions of length and volume minimisation. This leads to the hypothesis that predicting the true material cost of physical networks requires us to account for their full three-dimensional geometry, resulting in a largely intractable optimisation problem. We discover, however, an exact mapping of surface minimisation onto high-dimensional Feynman diagrams in string theory, predicting that with increasing link thickness, a locally tree-like network undergoes a transition into configurations that can no longer be explained by length minimisation. Specifically, surface minimisation predicts the emergence of trifurcations and branching angles in excellent agreement with the local tree organisation of physical networks across a wide range of application domains. Finally, we predict the existence of stable orthogonal sprouts, which not only are prevalent in real networks but also play a key functional role, improving synapse formation in the brain and nutrient access in plants and fungi.

Read the full article at: arxiv.org

Thomas F. Varley, Vaibhav P. Pai, Caitlin Grasso, Jeantine Lunshof, Michael Levin & Josh Bongard

Communicative & Integrative Biology 

Volume 18, 2025 – Issue 1

Understanding how populations of cells collectively coordinate activity to produce the complex structures and behaviors that characterize multicellular organisms, and which coordinated activities, if any, survive processes that reshape cells and tissues into organoids, are fundamental issues in modern biology. Here, we show how techniques from complex systems and multivariate information theory provide a framework for inferring the structure of collective organization in non-neural tissue. Many of these techniques were developed in the context of theoretical neuroscience, where these statistics have been found to be altered during different cognitive, clinical, or behavioral states, and are generally thought to be informative about the underlying dynamics linking biology to cognition. Here, we show that these same patterns of coordinated activity are also present in the aneural tissues of evolutionarily distant biological systems: preparations of embryonic Xenopus laevis tissue (known as “basal Xenobots”). These similarities suggest that such patterns of activity either arose independently in these two systems (epithelial constructs and brains); are epiphenomenological byproducts of other dynamics conserved across vastly different configurations of life; or somehow directly support adaptive behavior across diverse living systems. Finally, these results provide unambiguous support for the hypothesis that, despite their apparent simplicity as collections of non-neural epithelial cells, Xenobots are in fact integrated, complex systems in their own right, with sophisticated internal information structures.

Read the full article at: www.tandfonline.com

Dirk Helbing

This review article reflects on emerging societies using a data-driven, cybernetic governance approach. Such an approach implies great opportunities, but also considerable challenges and potential ethical issues, requiring scientific and pubic debate. We will start by discussing the role of the Internet of Things for cyber-physical systems and smart societies. After this, we will introduce converging technologies, which are able to connect information technologies with nano-, bio-, and other technologies. While these technologies are currently less known to the wider public, they can be game changers for societies. Among the possible applications, we will pay particular attention to the “Internet of Bodies” and to nano-neurotechnologies. The former can be used in the context of precision medicine, while the latter may eventually enable interactions with the real world just by thought. Both approaches use digital twins and have enormous opportunities , but the risks of accidental damage or intentional misuse are high. As it turns out, quantum technologies have further interesting implications, which may change emerging cybernetic societies as well. Last but not least we will discuss ethical issues and further challenges of cybernetic societies, leading to a call for action.

Read the full article at: www.researchgate.net

Leila Hedayatifar, Alfredo J. Morales, Dominic E. Saadi, Rachel A. Rigg, Olha Buchel, Amir Akhavan, Egemen Sert, Aabir Abubaker Kar, Mehrzad Sasanpour, Irving R. Epstein & Yaneer Bar-Yam 

Scientific Reports volume 15, Article number: 33854 (2025)

Predicting dynamic behaviors is one of the goals of science in general as well as essential to many specific applications of human knowledge to real world systems. Here, we introduce an analytic approach using the sigmoid growth curve to model the dynamics of individual entities within complex systems. Despite the challenges posed by nonlinearity and unpredictability, we demonstrate that sigmoid-like trajectories frequently emerge in systems where entities undergo phases of acceleration and deceleration of growth. Through case studies of (1) customer purchasing behavior and (2) U.S. legislation adoption, we show that these patterns can be identified and used to predict an entity’s ultimate state well in advance of reaching it. This provides valuable insights for business leaders and policymakers. Moreover, our characterization of individual component dynamics offers a framework to reveal the aggregate behavior of the entire system. Moreover, our classification of entity lifepaths contributes to understanding system-level structure by revealing how individual-level dynamics scale to aggregate behaviors. This study offers a practical modeling framework that captures commonly observed growth dynamics in diverse complex systems and supports predictive decision-making.

Read the full article at: www.nature.com