Metabolic Networks: Beyond the Graph

Abstract

Drugs are devised to enter into the metabolism of an organism in order to produce a desired effect. From the chemical point of view, cellular metabolism is constituted by a complex network of reactions transforming metabolites one in each other. Knowledge on the structure of this network could help to develop novel methods for drug design, and to comprehend the root of known unexpected side effects. Many large-scale studies on the structure of metabolic networks have been developed following models based on different kinds of graphs as the fundamental image of the reaction network. Graphs models, however, comport wrong assumptions regarding the structure of reaction networks that may lead into wrong conclusions if they are not taken into account. In this article we critically review some graph-theoretical approaches to the analysis of centrality, vulnerability and modularity of metabolic networks, analyzing their limitations in estimating these key network properties, consider some proposals explicit or implicitly based on directed hypergraphs regarding their ability to overcome these issues, and review some recent implementation improvements that make the application of these models in increasingly large networks a viable option.

Maximizing output and recognizing autocatalysis in chemical reaction networks is NP-complete

“…Due to the size of reaction networks of 1 arXiv:1110.6051v1 [q-bio.MN] 27 Oct 2011 practical interest, efficient algorithms are required for their investigation. Chemical reaction networks cannot be modeled appropriately as graphs despite the many attempts in this direction [1]. Instead, they are canonically specified by their stoichiometric matrix S, augmented by information on catalysts.…”

Opportunities at the Interface of Network Science and Metabolic Modeling

“…In addition, all of these graphs can be directed/undirected (when the matrix A is asymmetric/symmetric), or weighted/unweighted (where the elements A ij can have weights encoding different properties). Such modeling choices can strongly influence the conclusions drawn from network analyses (Klamt et al, 2009;Bernal and Daza, 2011;Beguerisse-Díaz et al, 2018). For example, the existence of power law degree distributions (Jeong et al, 2000) and the small-world property in metabolism (Wagner and Fell, 2001), two cornerstone concepts in network science, have been disputed (Arita, 2004;Lima-Mendez and van Helden, 2009) and attributed to specific ways of constructing the network graph (Montañez et al, 2010;Bernal and Daza, 2011).…”

Opportunities at the interface of network science and metabolic modelling

“…In addition, all of these graphs can be directed/undirected (when the matrix A is symmetric/asymmetric), or weighted/unweighted (where the elements A ij can have weights encoding different properties). Such modelling choices have a dramatic influence on the results and conclusions drawn from network analyses 78,83,84 . For example, the existence of power law degree distributions 57 and the small-world property 59 , two widespread concepts in network science, have been disputed 85,86 and attributed to specific ways of constructing the metabolic network graph 84,87 .…”

“…Such modelling choices have a dramatic influence on the results and conclusions drawn from network analyses 78,83,84 . For example, the existence of power law degree distributions 57 and the small-world property 59 , two widespread concepts in network science, have been disputed 85,86 and attributed to specific ways of constructing the metabolic network graph 84,87 .…”