Arthur N. Montanari, Chao Duan, Luis A. Aguirre, and Adilson E. Motter
PNAS January 4, 2022 119 (1) e2113750119;
Observing the states of a network is fundamental to our ability to explore and control the dynamics of complex natural, social, and technological systems. The problem of determining whether the system is observable has been addressed by network control researchers over the past decade. Progress on the further problem of actually designing and implementing efficient algorithms to infer the states from limited measurements has been hampered by the high dimensionality of large-scale networks. Noting that often only a small number of state variables in a network are essential for control, intervention, and monitoring purposes, this work develops a graph-based theory and highly scalable methods that achieve accurate estimation of target variables of network systems with minimal sensing and computational resources.
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This paper attempts to clarify the notion of goal-directedness, which is often misunderstood as being inconsistent with standard causal mechanisms. We first note that goal-directedness does not presuppose any mysterious forces, such as intelligent design, vitalism, conscious intention or backward causation. We then review attempts at defining goal-directedness by means of more operational characteristics: equifinality, plasticity, persistence, concerted action and negative feedback. We show that all these features can be explained by interpreting a goal as a far-from-equilibrium attractor of a dynamical system. This implies that perturbations that make the system deviate from its goal-directed trajectory are automatically compensated—at least as long as the system stays within the same basin of attraction. We argue that attractors and basins with the necessary degree of resilience tend to self-organize in complex reaction networks, thus producing self-maintaining “organizations”. These can be seen as an abstract model of the first goal-directed systems, and thus of the origin of life.
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C. A. Aguilar-Trigueros, L. Boddy, M. C. Rillig & M. D. Fricker
ISME Communications volume 2, Article number: 2 (2022)
Colonization of terrestrial environments by filamentous fungi relies on their ability to form networks that can forage for and connect resource patches. Despite the importance of these networks, ecologists rarely consider network features as functional traits because their measurement and interpretation are conceptually and methodologically difficult. To address these challenges, we have developed a pipeline to translate images of fungal mycelia, from both micro- and macro-scales, to weighted network graphs that capture ecologically relevant fungal behaviour. We focus on four properties that we hypothesize determine how fungi forage for resources, specifically: connectivity; relative construction cost; transport efficiency; and robustness against attack by fungivores. Constrained ordination and Pareto front analysis of these traits revealed that foraging strategies can be distinguished predominantly along a gradient of connectivity for micro- and macro-scale mycelial networks that is reminiscent of the qualitative ‘phalanx’ and ‘guerilla’ descriptors previously proposed in the literature. At one extreme are species with many inter-connections that increase the paths for multidirectional transport and robustness to damage, but with a high construction cost; at the other extreme are species with an opposite phenotype. Thus, we propose this approach represents a significant advance in quantifying ecological strategies for fungi using network information.
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Guim Aguadé-Gorgorió, Stuart Kauffman & Ricard Solé
Bulletin of Mathematical Biology volume 84, Article number: 24 (2022)
Phenotypic switching in cancer cells has been found to be present across tumor types. Recent studies on Glioblastoma report a remarkably common architecture of four well-defined phenotypes coexisting within high levels of intra-tumor genetic heterogeneity. Similar dynamics have been shown to occur in breast cancer and melanoma and are likely to be found across cancer types. Given the adaptive potential of phenotypic switching (PHS) strategies, understanding how it drives tumor evolution and therapy resistance is a major priority. Here we present a mathematical framework uncovering the ecological dynamics behind PHS. The model is able to reproduce experimental results, and mathematical conditions for cancer progression reveal PHS-specific features of tumors with direct consequences on therapy resistance. In particular, our model reveals a threshold for the resistant-to-sensitive phenotype transition rate, below which any cytotoxic or switch-inhibition therapy is likely to fail. The model is able to capture therapeutic success thresholds for cancers where nonlinear growth dynamics or larger PHS architectures are in place, such as glioblastoma or melanoma. By doing so, the model presents a novel set of conditions for the success of combination therapies able to target replication and phenotypic transitions at once. Following our results, we discuss transition therapy as a novel scheme to target not only combined cytotoxicity but also the rates of phenotypic switching.
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The cover of the January issue of the magazine Trends in Cognitive Sciences shows a human brain composed of a honeybee swarm. The artwork depicts two seemingly distant biological systems that present striking similarities in decision dynamics and properties of information processing. Inspired by the study of house-hunting honeybees, recent research has established that performance in decision-making is affected in predictable ways by the overall goal-relevant magnitude of the alternatives. Magnitude-sensitivity has been observed in humans performing a wide variety of tasks and in organisms as diverse as non-human primates and aneural slime molds. Angelo Pirrone and colleagues review the literature and highlight how prominent accounts of theoretical, descriptive, and normative decision-making had to be revisited to explain magnitude-sensitivity.
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