Intrinsic limitations in mainstream methods of identifying network motifs in biology

James Fodor,Michael Brand,Rebecca J Stones,Ashley M Buckle

doi:10.1186/s12859-020-3441-x

Abstract

BackgroundNetwork motifs are connectivity structures that occur with significantly higher frequency than chance, and are thought to play important roles in complex biological networks, for example in gene regulation, interactomes, and metabolomes. Network motifs may also become pivotal in the rational design and engineering of complex biological systems underpinning the field of synthetic biology. Distinguishing true motifs from arbitrary substructures, however, remains a challenge.ResultsHere we demonstrate both theoretically and empirically that implicit assumptions present in mainstream methods for motif identification do not necessarily hold, with the ramification that motif studies using these mainstream methods are less able to effectively differentiate between spurious results and events of true statistical significance than is often presented. We show that these difficulties cannot be overcome without revising the methods of statistical analysis used to identify motifs.ConclusionsPresent-day methods for the discovery of network motifs, and, indeed, even the methods for defining what they are, are critically reliant on a set of incorrect assumptions, casting a doubt on the scientific validity of motif-driven discoveries. The implications of these findings are therefore far-reaching across diverse areas of biology.

Highlights

Network motifs are connectivity structures that occur with significantly higher frequency than chance, and are thought to play important roles in complex biological networks, for example in gene regulation, interactomes, and metabolomes
Network motif identification plays an important role in molecular and cell biology research, notably the study of gene regulation [3], interactomes [6], and metabolomes [7]
In the study of network motifs, it is critical to be able to determine whether a particular subgraph H observed in a network G is a motif, or merely a chance occurrence

Summary

Introduction

Network motifs are connectivity structures that occur with significantly higher frequency than chance, and are thought to play important roles in complex biological networks, for example in gene regulation, interactomes, and metabolomes. A network motif is a particular connected pattern of nodes and edges that appears in a network significantly more frequently than would be expected by chance. It is hypothesized that network motifs play a more important role in network function than arbitrary substructures. Network motif identification plays an important role in molecular and cell biology research, notably the study of gene regulation [3] (which describes regulatory relationships between transcription factors and their target genes), interactomes [6] (which describe protein-protein interactions), and metabolomes [7] (which describe the complete set of small molecules within a cell). In order to determine whether H is a motif of G, the following procedure (or a close variant thereof) is typically followed [8]: Step 1: Define SðGÞ to be the set of all networks similar to G, in the sense that they have the same

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Conclusion