Chapter 4: Applications of network biology #
Lethality and centrality in protein networks
Jeong et al. (2001) NatureThe classic paper (with the classic colourful protein interaction network image!) showing how highly connected proteins in a network are much more likely to be essential for (yeast) survival.
Nine quick tips for analyzing network data
Miele et al. (2020) PLoS Computational BiologyA good article emphasising important ““good practices”” for analysing network data.
The human disease network
Goh et al. (2007) Proceedings of the National Academy of Sciences USAAn excellent paper detailing the construction and analysis of bi-partite graphs to study diseases, symptoms, and genes.
Edgetic perturbation models of human inherited disorders
Zhong et al. (2009) Molecular Systems BiologyThe classic paper on ““edgetics””.
Differential network biology
Ideker and Krogan (2012) Molecular Systems BiologyThe classic paper describing DiNA, and its application to study some example disease conditions.
Network-based features enable prediction of essential genes across diverse organisms
Azhagesan et al. (2018) PLoS ONEEmphasises the remarkable utility of network-based features in predicting essential genes in interactomes.
The organisational structure of protein networks: revisiting the centrality—lethality hypothesis
Raman et al. (2014) Systems and Synthetic BiologyRe-examines the centrality—lethality hypothesis from the perspective of essentiality data from DEG, and interactomes from STRING.
Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks
Tripathi et al. (2019) Frontiers in GeneticsDiscusses multiple strategies to identify disease-relevant modules from biological networks.
Predicts essential genes across 2700+ organisms, capturing local, global and neighbourhood properties of STRING interactomes to extract features.
Assessment of network module identification across complex diseases
Choobdar et al. (2019) Nature MethodsA detailed assessment of numerous algorithms for community detection, as applied to identify disease modules in multiple types of networks.
Predicting novel metabolic pathways through subgraph mining
Sankar et al. (2017) BioinformaticsA method for retrosynthesis that uses only structural information to predict and rank possible biotransformations.
Protein structure: insights from graph theory
Vishveshwara et al. (2002) Journal of Theoretical and Computational ChemistryOne of the classic papers describing the application of graph theory to represent and understand protein structures.
The Protein Folding Network
Rao and Caflisch (2004) Journal of Molecular BiologyA detailed study of the network properties of protein folding pathways.
A General Mechanism for the Propagation of Mutational Effects in Proteins
Rajasekaran et al. (2017) BiochemistryA study that integrates data from published NMR experiments, molecular dynamics simulations and network theory to model the allosteric effects of point mutations.
Toward a quantitative description of microscopic pathway heterogeneity in protein folding
Gopi et al. (2017) Physical Chemistry Chemical PhysicsAn extraordinary paper that applies numerous tools from network theory to shed light on possible microstates in protein folding pathways.
Missing and spurious interactions and the reconstruction of complex networks
Guimera and Sales-Pardo (2009) Proceedings of the National Academy of Sciences USAA very interesting study on link prediction in networks.
Network representations and methods for the analysis of chemical and biochemical pathways
Sandefur et al. (2013) Molecular bioSystemsA nice review on various biological networks and their analyses, including chemical and biochemical reaction networks.