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

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 was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context.

Nucleotide-based genetic networks: Methods and applications

“…In gene–gene networks, the flow of information was investigated in co-mutating genes using a model ‘perturbed master equation’ (pME) in order to identify the gene pairs responsible for network frailness in breast cancer (Bersanelli et al . 2020 ). In another study, enriched genes were identified in cancer pathways from co-mutation-based gene–gene networks of a large-scale study across 14 cancers with 2.5 million non-synonymous mutations and ∼6700 tumor exomes (Liu et al .…”

Resilience integrates concepts in aging research

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Resilience and Tipping Points Resilience is a central research topic in ecosystem ecology ( Scheffer et al., 2001 ). Originally arising from Dynamical Systems theory, a considerable body of precise mathematical theory has been developed to interrogate mechanisms underlying ecosystem stability, although the definition of stability has evolved over time ( Bersanelli et al., 2020 ; Grimm and Wissel, 1997 ). Early on, ecologists quantified resilience (or ‘elasticity’) by measuring the rates of recovery of ecosystems after a perturbation; however, in the 1970’s, ecologists realized that another approach was needed to capture the phenomenon whereby some systems, after a perturbation, would reach a tipping point where they would flip to a fundamentally different state.

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