1,721,011 research outputs found
Modelling gene regulatory networks: systems biology to complex systems
No full textDraft literature review on approaches to modelling gene regulatory networks
Book review of Thilo Gross and Hiroki Sayama's "Adaptive Networks: Theory, Models and Applications"
No full textModelling academic research funding as a resource allocation problem
No full textAcademic research funding is allocated through a competitive bidding process that may lead to inefficiency as excessive time is spent on proposal writing. We develop a simple agent-based model of the process and find that current systems are indeed likely to be inefficient. Alternative allocation schemes involving either a cap on individual effort or appraisal from the centre are indicated as improvements
Dynamical approaches to modeling developmental gene regulatory networks
No full textThe network of interacting regulatory signals within a cell comprises one of the most complex and powerful computational systems in biology. Gene regulatory networks play a key role in transforming the information encoded in a genome into morphological form. To achieve this feat, gene regulatory networks must respond to and integrate environmental signals with their internal dynamics in a robust and coordinated fashion. The highly dynamic nature of this process lends itself to interpretation and analysis in the language of dynamical models. Modelling provides a means of systematically untangling the complicated structure of gene regulatory networks, a framework within which to simulate the behaviour of reconstructed systems and, in some cases, suites of analytic tools for exploring that behaviour and its implications. This review provides a general background to the idea of treating a regulatory network as a dynamical system, and describes a variety of different approaches that have been taken to the dynamical modelling of gene regulatory networks
Reconstructing phylogeny from RNA secondary structure via simulated evolution
No full textDNA sequences of genes encoding functional RNA molecules (e.g., ribosomal RNAs) are commonly used in phylogenetics (i.e. to infer evolutionary history). Trees derived from ribosomal RNA (rRNA) sequences, however, are inconsistent with other molecular data in investigations of deep branches in the tree of life. Since much of te functional constraints on the gene products (i.e. RNA molecules) relate to three-dimensional structure, rather than their actual sequences, accumulated mutations in the gene sequences may obscure phylogenetic signal over very large evolutionary time-scales. Variation in structure, however, may be suitable for phylogenetic inference even under extreme sequence divergence. To evaluate qualitatively the manner in which structural evolution relates to sequence change, we simulated the evolution of RNA sequences under various constraints on structural change
Homophily and competition: a model of group affiliation
No full textHow can we understand the interaction between the social network topology of a population and the patterns of group affiliation in that population? Each aspect influences the other: social networks provide the conduits via which groups recruit new members, and groups provide the context in which new social ties are formed. While many social simulation models exhibit group formation as a part of their behaviour (e.g., opinion clusters or converged cultures), models that explicitly focus on group affiliation are rare. We introduce one such model, based upon the ecological theory of group affiliation, and use it to explore the effect of two system properties—bias toward the creation of homophilous ties and competition between groups—on the dynamics of social evolution and group formation
Spatial embedding as an enabling constraint: Introduction to a special issue of complexity on the topic of “Spatial Organisation”
Full text linkWe introduce and discuss the role of spatial embedding as an enabling constraint on complex system structure and function
Competition and the dynamics of group affiliation
Full text linkHow can we understand the interaction between the social network topology of a population and the patterns of group affiliation in that population? Each aspect influences the other: social networks provide the conduits via which groups recruit new members, and groups provide the context in which new social ties are formed. From an organisational ecology perspective, groups can be considered to compete with one another for the time and energy of their members. Such competition is likely to have an impact on the way in which social structure and group affiliation co-evolve. While many social simulation models exhibit group formation as a part of their behaviour (e.g., opinion clusters or converged cultures), models that explicitly focus on group affiliation are rare. We describe and explore the behaviour of a model in which, distinct from most current models, individual nodes can belong to multiple groups simultaneously. By varying the capacity of individuals to belong to groups, and the costs associated with group membership, we explore the effect of different levels of competition on population structure and group dynamics
Group formation and social evolution: a computational model
No full textThe tendency to organise into groups is a fundamental property of human nature. Despite this, many models of social network evolution consider the emergence of community structure as a side effect of other processes, rather than as a mechanism driving social evolution. We present a model of social network evolution in which the group formation process forms the basis of the rewiring mechanism. Exploring the behaviour of our model, we find that rewiring on the basis of group membership reorganises the network structure in a way that, while initially facilitating the growth of groups, ultimately inhibits it
Self-organising agent communities for autonomic resource management
No full textThe autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes
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