Metabolic Flux-Based Modularity using Shortest Retroactive distances

Abstract

BackgroundGraph-based modularity analysis has emerged as an important tool to study the functional organization of biological networks. However, few methods are available to study state-dependent changes in network modularity using biological activity data. We develop a weighting scheme, based on metabolic flux data, to adjust the interaction distances in a reaction-centric graph model of a metabolic network. The weighting scheme was combined with a hierarchical module assignment algorithm featuring the preservation of metabolic cycles to examine the effects of cellular differentiation and enzyme inhibitions on the functional organization of adipocyte metabolism.ResultsOur analysis found that the differences between various metabolic states primarily involved the assignment of two specific reactions in fatty acid synthesis and glycerogenesis. Our analysis also identified cyclical interactions between reactions that are robust with respect to metabolic state, suggesting possible co-regulation. Comparisons based on cyclical interaction distances between reaction pairs suggest that the modular organization of adipocyte metabolism is stable with respect to the inhibition of an enzyme, whereas a major physiological change such as cellular differentiation leads to a more substantial reorganization.ConclusionTaken together, our results support the notion that network modularity is influenced by both the connectivity of the network’s components as well as the relative engagements of the connections.

Discovery of substrate cycles in large scale metabolic networks using hierarchical modularity

“…One way to address this limitation is to include activity data. Recently, we showed that weighting the edges with metabolic flux data results in different modularity, reflecting the metabolic state of the system [ 13 ]. We found that weighting the edges to reflect reaction engagements better ensured that highly active cycles are prioritized in partitioning the network.…”

“…The network was then partitioned into two subnetworks such that reaction pairs with larger ShReD values were split apart, whereas reaction pairs with shorter ShReD values were placed into the same sub-network. The ShReD-based partition was iterated on each successively formed subnetwork until the resulting partition no longer yielded a positive modularity score [ 13 ]. In case a partition produced a subnetwork that was not completely connected, the next iteration of the partition algorithm was performed on the connected components of this subnetwork.…”

Quantitative assessment of gene expression network module-validation methods

“…Modularity is a common characteristic of omics-based biological networks 1 2 3 . Module-based analyses that investigate or deconstruct omics-based biological networks have become a hot topic in recent years 4 5 . Various types of algorithms have been proposed to identify modules (also known as communities, clusters, and subnetworks), including network clustering 6 7 , heuristic search 8 9 , seed extension 10 , topology network 11 12 , and matrix decomposition 13 14 .…”