Flux-dependent graphs for metabolic networks

Abstract

Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topological and community structure, which capture the re-routing of metabolic fluxes and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions.

Opportunities at the interface of network science and metabolic modelling

“…Conversely, a graph where the nodes are reactions and the edges describe the sharing of metabolites as reactants or products 77,78 has an adjacency matrix…”

“…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 .…”

Defining Integrated Care Systems Through Patient Data From Referral Networks in the English National Health Service: A Graph-Based Clustering Study.

“…(12)(13)(14) These techniques the have previously been applied in healthcare to understand organisation of Primary Care Networks in London, to examine mismatches between supply and demand for planned surgical care in England, (7,15) and in other disciplines, to identify the underlying structure of metabolic networks, transport systems and power grids. (16,17) We then compare an optimal arrangement consisting of 42 communities to the pre-existing con guration of 42 STP organisations, in terms of the coverage of the population within the community registered at a GP practice within the same community, and the proportion of outpatient clinic appointment provided by hospitals in the same community as the referring GP practice. In doing so, we examine the extent to which existing organisational con gurations capture patterns of patient activity, and compare this to data-driven, optimised con gurations produced from MMCD.…”

Signed Distance Correlation (SiDCo): A network analysis application of distance correlation for identifying metabolic networks disrupted in Dementia with Lewy Bodies

“…Several tools from graph theory have been previously applied to the analysis of metabolic networks with some early examples exploring the network degree distribution 11 as well as community detection 12 . In metabolic network design, recent studies report using extensive knowledge of metabolism and resources such as RECON3 13 for the development of more accurate as well as directed and flux-dependent graphs 14 . However, when analysing the metabolome and lipidome, the issues of minimally described reaction networks and sparse coverage remain major hurdles.…”