Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach

Scientific Reports
Matteo BersanelliGastone C Castellani

Abstract

In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation wa...Continue Reading

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Related Concepts

Malignant Neoplasm of Breast
Genes
Problem
Research
Frailty
Somatic Mutation
Biological Neural Networks
Positioning Attribute
Anatomic Node
Protein-Protein Interaction

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