Journal ArticleDOI
Review of uses of network and graph theory concepts within proteomics.
Peter Grindrod,Milla Kibble +1 more
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A number of approaches for exploiting modern network theory to help describe and analyze different data sets and problems associated with proteomic data are considered and may help scientists from a mathematics and physics background to understand where they may apply their expertise.Abstract:
The size and nature of data collected on gene and protein interactions has led to a rapid growth of interest in graph theory and modern techniques for describing, characterizing and comparing networks. Simultaneously, this is a field of growth within mathematics and theoretical physics, where the global properties, and emergent behavior of networks, as a function of the local properties has long been studied. In this review, a number of approaches for exploiting modern network theory to help describe and analyze different data sets and problems associated with proteomic data are considered. This review aims to help biologists find their way towards useful ideas and references, yet may also help scientists from a mathematics and physics background to understand where they may apply their expertise.read more
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Graph-based methods for analysing networks in cell biology
TL;DR: This review surveys the recent advances in the field of graph-driven methods for analysing complex cellular networks and offers a personal view of the key future trends and developments in graph-based analysis of large-scale datasets.
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Network Properties Revealed through Matrix Functions
TL;DR: A general class of measures based on matrix functions is introduced, and it is shown that a particular case involving a matrix resolvent arises naturally from graph-theoretic arguments.
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Chapter 4: Protein Interactions and Disease
Mileidy Gonzalez,Maricel G. Kann +1 more
TL;DR: The application of protein interaction networks as a translational approach to the study of human disease and the challenges faced by these approaches are described.
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Protein interactions and disease: computational approaches to uncover the etiology of diseases
TL;DR: This review explores recent approaches to the understanding of the mechanisms of disease at the molecular level through their underlying protein interactions through genetics, protein structure and protein interaction network analyses.
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Protein-protein interaction networks (PPI) and complex diseases.
Nahid Safari-Alighiarloo,Mohammad Taghizadeh,Mostafa Rezaei-Tavirani,Bahram Goliaei,Ali Asghar Peyvandi +4 more
TL;DR: A novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network.
References
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Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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Emergence of Scaling in Random Networks
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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.
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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.