Cooperation, competition and the emergence of criticality in communities of adaptive systems

Hidalgo, Jorge; Grilli, Jacopo; Suweis, Samir; Maritan, Amos; Muñoz, Miguel A.

doi:10.1088/1742-5468/2016/03/033203

Cited by 13 publications

(13 citation statements)

References 41 publications

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“…Observed is a characteristic range of fold changes f , as determined by the basal and maximal activity levels (dashed lines), and threshold cell density fractions c f rac crit , as determined by the normalized optical densities of cell growth at which QS activity reaches half-maximal levels (solid lines). This is especially relevant in light of mounting evidence in recent years that biological systems display self-organized criticality, naturally evolving towards a state that is an optimal balance of order and disorder [35][36][37][38]. In the context of intercellular communication networks, being near such a critical point would allow for collective group decision making that is simultaneously robust and flexible.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Mapping quorum sensing onto neural networks to understand collective decision making in heterogeneous microbial communities

Yusufaly¹,

Boedicker²

2017

Phys. Biol.

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Microbial communities frequently communicate via quorum sensing (QS), where cells produce, secrete, and respond to a threshold level of an autoinducer (AI) molecule, thereby modulating gene expression. However, the biology of QS remains incompletely understood in heterogeneous communities, where variant bacterial strains possess distinct QS systems that produce chemically unique AIs. AI molecules bind to 'cognate' receptors, but also to 'non-cognate' receptors found in other strains, resulting in inter-strain crosstalk. Understanding these interactions is a prerequisite for deciphering the consequences of crosstalk in real ecosystems, where multiple AIs are regularly present in the same environment. As a step towards this goal, we map crosstalk in a heterogeneous community of variant QS strains onto an artificial neural network model. This formulation allows us to systematically analyze how crosstalk regulates the community's capacity for flexible decision making, as quantified by the Boltzmann entropy of all QS gene expression states of the system. In a mean-field limit of complete cross-inhibition between variant strains, the model is exactly solvable, allowing for an analytical formula for the number of variants that maximize capacity as a function of signal kinetics and activation parameters. An analysis of previous experimental results on the Staphylococcus aureus two-component Agr system indicates that the observed combination of variant numbers, gene expression rates and threshold concentrations lies near this critical regime of parameter space where capacity peaks. The results are suggestive of a potential evolutionary driving force for diversification in certain QS systems.Multicellular communities of microbes frequently coordinate changes in group behavior, which requires cellto-cell communication via the exchange of extracellular signaling molecules called autoinducers (AI), a process known as quorum sensing (QS) [1]. Microbes produce AI molecules at a default basal rate, and if the local concentration of AI molecules accumulates beyond a certain threshold, the AI production rate is amplified to an activated level via a positive-feedback loop, simultaneously regulating the expression of QS-controlled genes. This two-state QS regulatory network controls a wide array of collective behaviors in microbial communities, such as the formation of biofilms, the regulation of virulence, or lateral gene transfer [1][2][3][4][5].Although quorum sensing has traditionally been viewed as a process associated with homogeneous populations, several results in recent years have called this conventional wisdom into question [6][7][8]. In particular, it is by now well established that real microbial communities are frequently characterized by the stable coexistence of several variant QS systems in the population [9][10][11]. AI molecules produced by cells with one QS variant tend to activate QS in related kin cells that also express that same variant, with the corresponding native cognate receptor. However, when multiple ...

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Section: Discussion and Outlookmentioning

confidence: 99%

Mapping quorum sensing onto neural networks to understand collective decision making in heterogeneous microbial communities

Yusufaly¹,

Boedicker²

2017

Phys. Biol.

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“…It is well-known [13], [32], [52] that the FIM can be approximated by using a proper f -divergence measure computed between the parametric PDF of interest and a perturbed version of it. Notably, by expanding with Taylor (7) up to the second order we obtain:…”

Section: Non-parametric Estimatormentioning

confidence: 99%

Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization

Livi

Bianchi

Alippi

2018

IEEE Trans. Neural Netw. Learning Syst.

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Abstract-It is a widely accepted fact that the computational capability of recurrent neural networks (RNNs) is maximized on the so-called "edge of criticality." Once the network operates in this configuration, it performs efficiently on a specific application both in terms of: 1) low prediction error and 2) high shortterm memory capacity. Since the behavior of recurrent networks is strongly influenced by the particular input signal driving the dynamics, a universal, application-independent method for determining the edge of criticality is still missing. In this paper, we aim at addressing this issue by proposing a theoretically motivated, unsupervised method based on Fisher information for determining the edge of criticality in RNNs. It is proved that Fisher information is maximized for (finite-size) systems operating in such critical regions. However, Fisher information is notoriously difficult to compute and requires the analytic form of the probability density function ruling the system behavior. This paper takes advantage of a recently developed nonparametric estimator of the Fisher information matrix and provides a method to determine the critical region of echo state networks (ESNs), a particular class of recurrent networks. The considered control parameters, which indirectly affect the ESN performance, are explored to identify those configurations lying on the edge of criticality and, as such, maximizing Fisher information and computational performance. Experimental results on benchmarks and real-world data demonstrate the effectiveness of the proposed method.Index Terms-Echo state network (ESN), edge of criticality, Fisher information, nonparametric estimation.

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“…In recent years, games played on various random graphs and social networks have been investigated [1,6,10,11,20,23,26,29,31,38,50,54]. For example, the public goods game can be seen as a generalization of multiplayer N-player games and group interactions such as pattern formation and self-organization could be studied through this extension [18,46,62].…”

Section: Of 24mentioning

confidence: 99%

Evolutionary prisoner’s dilemma games coevolving on adaptive networks

Lee

Malik

Mucha

2017

Journal of Complex Networks

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We study a model for switching strategies in the Prisoner’s Dilemma game on adaptive networks of player pairings that coevolve as players attempt to maximize their return. We use a node-based strategy model wherein each player follows one strategy at a time (cooperate or defect) across all of its neighbors, changing that strategy and possibly changing partners in response to local changes in the network of player pairing and in the strategies used by connected partners. We compare and contrast numerical simulations with existing pair approximation differential equations for describing this system, as well as more accurate equations developed here using the framework of approximate master equations. We explore the parameter space of the model, demonstrating the relatively high accuracy of the approximate master equations for describing the system observations made from simulations. We study two variations of this partner-switching model to investigate the system evolution, predict stationary states, and compare the total utilities and other qualitative differences between these two model variants.

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Cooperation, competition and the emergence of criticality in communities of adaptive systems

Cited by 13 publications

References 41 publications

Mapping quorum sensing onto neural networks to understand collective decision making in heterogeneous microbial communities

Mapping quorum sensing onto neural networks to understand collective decision making in heterogeneous microbial communities

Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization

Evolutionary prisoner’s dilemma games coevolving on adaptive networks

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