Understanding modularity in molecular networks requires dynamics

Abstract

The era of genome sequencing has produced long lists of the molecular parts from which cellular machines are constructed. A fundamental goal in systems biology is to understand how cellular behavior emerges from the interaction in time and space of genetically encoded molecular parts, as well as nongenetically encoded small molecules. Networks provide a natural framework for the organization and quantitative representation of all the available data about molecular interactions. The structural and dynamic properties of molecular networks have been the subject of intense research. Despite major advances, bridging network structure to dynamics-and therefore to behavior-remains challenging. A key concept of modern engineering that recurs in the functional analysis of biological networks is modularity. Most approaches to molecular network analysis rely to some extent on the assumption that molecular networks are modular-that is, they are separable and can be studied to some degree in isolation. We describe recent advances in the analysis of modularity in biological networks, focusing on the increasing realization that a dynamic perspective is essential to grouping molecules into modules and determining their collective function.

Fig. 1

Hubs, bottlenecks, cliques, and network motifs are features of the topological structure of molecular networks that have been well-studied and can sometimes be used to infer function. (A) Hubs are nodes in the network with more interaction partners than average; they tend to be essential. (B) Bottlenecks are nodes that lie on the shortest path between many pairs of nodes in the network; they often lie at the interface between modules and also tend to be essential. (C) Cliques in protein interaction networks are indicative of protein complexes that function together. (D) The feed-forward loop (FFL) and single-input module (SIM) are network motifs whose output behavior varies little when dynamical parameters are changed, whereas (E) the bifan motif can generate widely different dynamics from different dynamical parameter combinations (12, 13).

Fig. 2

Dynamical constraints on a signaling pathway involved in tissue differentiation in rat correlate with allelic variation at the population level in the same pathway in fruit flies, revealing a link between dynamics and evolution. The “stiffest” combination of parameters was associated with the Ras and Raf proteins at the top of the pathway, and in fruit flies Ras and Raf exhibit the least allelic variation of all of the components of the pathway, consistent with the concept that stiff parameter combinations are constrained during evolution. The dotted box indicates that some accessory components of the two cascades are excluded here for clarity of presentation.