Anna Ciaunica, Michael Levin, Fernando Rosas, Karl Friston

The immune system is a central component of organismic function in humans. This paper addresses self-organisation of a biological system in relation to — and nested within — an other biological system in pregnancy. Pregnancy constitutes a fundamental state for human embodiment and a key step in the evolution and conservation of our species. While not all humans can be pregnant, our initial state of emerging and growing within another person’s body is universal. Hence, the pregnant state does not concern some individuals, but all individuals. Indeed, the hierarchical relationship in pregnancy reflects an even earlier autopoietic process in the embryo by which the number of individuals in a single blastoderm is dynamically determined by cell-cell interactions. The relationship, and the interactions between the two self-organising systems during pregnancy may play a pivotal role in understanding the nature of biological self-organisation per se in humans. Specifically, we consider the role of the immune system in biological self-organisation in addition to neural/brain systems that furnish us with a sense of self. We examine the complex case of pregnancy, whereby two immune systems need to negotiate exchange of resources and information in order to maintain viable self-regulation of nested systems. We conclude with a proposal for the mechanisms—that scaffold the complex relationship between two self-organising systems in pregnancy—through the lens of the Active Inference, with a focus on shared Markov blankets.

Read the full article at: psyarxiv.com

Sergey B. Yurchenko

Biosystems

Volume 229, July 2023, 104925

Is information the other face of causation? This issue cannot be clarified without discussing how these both are related to physical laws, logic, computation, networks, bio-signaling, and the mind-body problem. The relation between information and causation is also intrinsically linked to many other concepts in complex systems theory such as emergence, self-organization, synergy, criticality, and hierarchy, which in turn involve various notions such as observer-dependence, dimensionality reduction, and especially downward causation. A canonical example proposed for downward causation is the collective behavior of the whole system at a macroscale that may affect the behavior of each its member at a microscale. In neuroscience, downward causation is suggested as a strong candidate to account for mental causation (free will). However, this would be possible only on the condition that information might have causal power. After introducing the Causal Equivalence Principle expanding the relativity principle for coarse-grained and fine-grained linear causal chains, and a set-theoretical definition of multiscale nested hierarchy composed of modular ⊂-chains, it is shown that downward causation can be spurious. It emerges only in the eyes of an observer, though, due to information that could not be obtained by “looking” exclusively at the behavior of a system at a microscale. On the other hand, since biological systems are hierarchically organized, this information gain is indicative of how information can be a function of scale in these systems and a prerequisite for scale-dependent emergence of cognition and consciousness in neural networks.

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Antoine Allard, Cristopher Moore, Samuel V. Scarpino, Benjamin M. Althouse, and Laurent Hébert-Dufresne

SIAM Review Vol. 65, Iss. 2 (2023) 10.1137/20M1383811

Most models of epidemic spread, including many designed specifically for COVID-19, implicitly assume mass-action contact patterns and undirected contact networks, meaning that the individuals most likely to spread the disease are also the most at risk of contracting it from others. Here, we review results from the theory of random directed graphs which show that many important quantities, including the reproduction number and the epidemic size, depend sensitively on the joint distribution of in- and out-degrees (“risk” and “spread”), including their heterogeneity and the correlation between them. By considering joint distributions of various kinds, we elucidate why some types of heterogeneity cause a deviation from the standard Kermack–McKendrick analysis of SIR models, i.e., so-called mass-action models where contacts are homogeneous and random, and why some do not. We also show that some structured SIR models informed by realistic complex contact patterns among types of individuals (age or activity) are simply mixtures of Poisson processes and tend not to deviate significantly from the simplest mass-action model. Finally, we point out some possible policy implications of this directed structure, both for contact tracing strategy and for interventions designed to prevent superspreading events. In particular, directed graphs have a forward and a backward version of the classic “friendship paradox”—forward edges tend to lead to individuals with high risk, while backward edges lead to individuals with high spread—such that a combination of both forward and backward contact tracing is necessary to find superspreading events and prevent future cascades of infection.

Read the full article at: epubs.siam.org