Model validation of simple-graph representations of metabolism

Abstract

The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure-network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate-product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules.

A statistical mechanics description of environmental variability in metabolic networks

“…These metabolic networks are the same as those studied in [20] which were constructed using the KEGG database [13]. Note that, there are a number of ways of representing the set of metabolic reactions of an organism as a simple graph [12], the most popular of which is a substrate-product graph whereby each node corresponds to a metabolite, and a connection is made between reacting substances (substrates) and the products of the reaction. One potential caveat of such an approach, is that it can lead to the erroneous identification of metabolic pathways.…”

The Network Organization of Cancer-associated Protein Complexes in Human Tissues

“…6 and 7, respectively. Then we applied overlap score to quantity the similarity between the two partitions of cancers respectively generated from gene expression and protein complex profiles4546 and got the value of overlap score as 0.72. We then generated 200 pairs of random clusters of the cancers, in which the cluster sizes are the same as in the real data.…”

“…We use overlap score to measure the overlap extent of cancer clusters respectively generated from gene expression and protein complex profiles4546. Consider two different categories A and B (for example, two partitions of cancers got by different clustering methods) and assume each cancer is associated with a subset (cluster) of the partitions of A and B.…”

Scarcity of scale-free topology is universal across biochemical networks

“…The problem of characterizing the global structure of real-world systems is further compounded by the fact there are often many ways to coarse-grain a real system to generate a network representation, each corresponding to a different way for set of interactions to be projected onto a graph. For example, metabolic networks may be represented as unipartite or bipartite graphs, depending on whether one chooses to focus solely on the statistics over molecules (or reactions) and their interactions (requiring a unipartite representation) or instead to include both molecules and reactions as explicit nodes in the graph (where molecules and reactions represent two classes of nodes in a bipartite representation) [32][33][34]. These graphs can have different large-scale topological properties, even when projected from the same underlying system.…”