Biological function through network topology: a survey of the human diseasome

Abstract

Molecular network data are increasingly becoming available, necessitating the development of well performing computational tools for their analyses. Such tools enabled conceptually different approaches for exploring human diseases to be undertaken, in particular, those that study the relationship between a multitude of biomolecules within a cell. Hence, a new field of network biology has emerged as part of systems biology, aiming to untangle the complexity of cellular network organization. We survey current network analysis methods that aim to give insight into human disease.

Figure 1:

If we consider a metabolite-centric map of the following irreversible metabolic reaction from substrates A and B to products C and D, A + B→C + D, we can chose (a) to link only the main substrate–product pair (say, A and C) while leaving out the transitive elements, such as energy or water (say, B and D). However, it is not always the case that a reaction has transitive elements. If there are no transitive elements in this reaction, (b) the metabolite-centric network map would usually link A to both C and D, as well as B to both C and D, even though this might not be completely biologically accurate, since for the production of C (and D) both A and B are needed together, and this subtlety is lost in this type of network representation. However, the issue could be solved by using more involved mathematical concepts, such as hypergraphs instead of graphs, as edges in hypergraphs consist of any sub-sets of nodes and not just node pairs. By using hypergraphs (c), {A, B, C} and {A, B, D} would be hyperedges, which would better describe the real-world product–substrate relationships. However, algorithms for analysing hypergraphs are far more mathematically and conceptually involved than those for graphs.

Figure 2:

Graphlets with up to five nodes. There are 30 of them, G₀, G₁, G₂, … G₂₉, and they contain 73 topologically unique node types, which are called ‘automorphism orbits’. Nodes belonging to the same orbit are of the same shade [98].

Figure 3:

An illustration of the graphlet degree vector (GDV) of node v. GDV represents one way in which graphlets can be used to describe topology around a node. GDV(v) = (2,1,1,0,0,1,0 … ,0), meaning that v is touched by two edges (Orbit 0, illustrated in the left panel), an end-node of one graphlet G₁ (Orbit 1, illustrated in the middle panel), the middle node of one graphlet G₁ (Orbit 2, illustrated in the left panel again), no nodes of a triangle (Orbit 3 in graphlet G₂), no end-node of graphlet G₃ (Orbit 4), one middle node of graphlet G₃ (Orbit 5, illustrated in the right panel), and no other orbits. In this way, GDV essentially ‘quantifies’ the four-level-deep topological environment of a node.