Controllability in cancer metabolic networks according to drug targets as driver nodes

Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not...

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Veröffentlicht in:PloS one 2013-11, Vol.8 (11), p.e79397-e79397
Hauptverfasser: Asgari, Yazdan, Salehzadeh-Yazdi, Ali, Schreiber, Falk, Masoudi-Nejad, Ali
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creator Asgari, Yazdan
Salehzadeh-Yazdi, Ali
Schreiber, Falk
Masoudi-Nejad, Ali
description Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.
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subjects Algorithms
Antineoplastic Agents - therapeutic use
Biochemistry
Bioinformatics
Biology
Biophysics
Cancer
Cancer therapies
Cluster Analysis
Clusters
Complex systems
Computational Biology - methods
Control systems
Control theory
Controllability
Disease
Distributed control systems
Drugs
Gene expression
Genomes
Humans
Laboratories
Medicine
Metabolic networks
Metabolic Networks and Pathways
Metabolism
Models, Biological
Molecular Targeted Therapy
Neoplasms - drug therapy
Neoplasms - genetics
Neoplasms - metabolism
Networks
Nodes
Nonlinear systems
Physics
Proteins
Software
Stability
Structure-function relationships
title Controllability in cancer metabolic networks according to drug targets as driver nodes
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