The Emergence of Social Consensus in Boolean Networks

Green, David G.; Leishman, Tania Gaye; Sadedin, Suzanne

doi:10.1109/alife.2007.367823

Cited by 44 publications

(36 citation statements)

References 18 publications

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“…Boolean networks provide a useful framework to study generic dynamical systems with unknown or partially known structure or function [29][30][31]. The modelling scheme was first introduced by Kauffman [32,33] for studying gene transcriptional networks and subsequently applied successfully in different organization levels of biological regulatory networks within organisms [34][35][36][37], and also in various social networks [38,39]. We use Boolean modelling to analyse the dynamical behaviour of the networks and ask: how sensitive are the networks with respect to the smallest of perturbations?…”

Section: Boolean Modellingmentioning

confidence: 99%

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Nandi

Annagiri

Bhattacharya

2014

J. R. Soc. Interface.

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Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.

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Section: Boolean Modellingmentioning

confidence: 99%

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Nandi

Annagiri

Bhattacharya

2014

J. R. Soc. Interface.

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“…BNs have also been used for modeling interactions between simple agents and studying the emergence of social consensus (see, e.g. Green et al (2007)). …”

Section: Introductionmentioning

confidence: 99%

Controllability of Boolean control networks via the Perron–Frobenius theory

2012

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Boolean control networks (BCNs) are recently attracting considerable interest as computational models for genetic and cellular networks. Addressing control-theoretic problems in BCNs may lead to a better understanding of the intrinsic control in biological systems, as well as to developing suitable protocols for manipulating biological systems using exogenous inputs. We introduce two definitions for controllability of a BCN, and show that a necessary and sufficient condition for each form of controllability is that a certain nonnegative matrix is irreducible or primitive, respectively. Our analysis is based on a result that may be of independent interest, namely, a simple algebraic formula for the number of different control sequences that steer a BCN between given initial and final states in a given number of time steps, while avoiding a set of forbidden states.

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“…BNs provide useful modeling tools for dynamical systems whose statevariables can attain two possible values. Examples range from artificial neural networks with ON/OFF neurons (see, e.g., Hassoun (1995)) to models of peer interactions in social networks (see, e.g., Green et al (2007)). …”

Section: Introductionmentioning

confidence: 99%

Observability of Boolean networks: A graph-theoretic approach

2013

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Boolean networks (BNs) are discrete-time dynamical systems with Boolean state-variables and outputs. BNs are recently attracting considerable interest as computational models for genetic and cellular networks. We consider the observability of BNs, that is, the possibility of uniquely determining the initial state given a time sequence of outputs. Our main result is that determining whether a BN is observable is NP-hard. This holds for both synchronous and asynchronous BNs. Thus, unless P=NP, there does not exist an algorithm with polynomial time complexity that solves the observability problem. We also give two simple algorithms, with exponential complexity, that solve this problem. Our results are based on combining the algebraic representation of BNs derived by D. Cheng with a graph-theoretic approach. Some of the theoretical results are applied to study the observability of a BN model of the mammalian cell cycle.

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The Emergence of Social Consensus in Boolean Networks

Cited by 44 publications

References 18 publications

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Controllability of Boolean control networks via the Perron–Frobenius theory

Observability of Boolean networks: A graph-theoretic approach

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