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Current approaches to gene regulatory network modelling

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TLDR
A hybrid model called Finite State Linear Model is described and some simple network dynamics can be simulated in this model, and the topology of gene regulatory networks in yeast is studied in more detail.
Abstract
Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

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AGRIS: the Arabidopsis Gene Regulatory Information Server, an update.

TL;DR: The Arabidopsis Gene Regulatory Information Server (AGRIS; http://arabidopsis.med.ohio-state.edu/) provides a comprehensive resource for gene regulatory studies.
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Consistency of community detection in networks under degree-corrected stochastic block models

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Consistency of community detection in networks under degree-corrected stochastic block models

TL;DR: It is found that methods based on the degree-corrected stochastic block model are consistent under a wider class of models and that modularity-type methods require parameter constraints for consistency, whereas likelihood-based methods do not.
References
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Journal ArticleDOI

Gene Ontology: tool for the unification of biology

TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Book

Introduction to Algorithms

TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Journal ArticleDOI

Statistical mechanics of complex networks

TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI

Error and attack tolerance of complex networks

TL;DR: It is found that scale-free networks, which include the World-Wide Web, the Internet, social networks and cells, display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealistically high failure rates.
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