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The road to modularity

Key Points

  • A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components.

  • Modularity has emerged as a rallying point for research in developmental and evolutionary biology, as well as in molecular systems biology.

  • Modularity has been found on all levels of biological organization: the structure of macromolecules, protein–protein interaction networks, gene regulation and the variation of quantitative traits.

  • Various mechanisms can lead to modularity, including gene duplication, neutral mutations and selection for robustness or selection in response to varying environmental conditions.

  • The main challenge for future research is to provide evidence to refute some of these models, and to determine whether modularity has an impact on the rate and pattern of evolution.

Abstract

A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components. Modularity has emerged as a rallying point for research in developmental and evolutionary biology (and specifically evo–devo), as well as in molecular systems biology. Here we review the evidence for modularity and models about its origin. Although there is an emerging agreement that organisms have a modular organization, the main open problem is the question of whether modules arise through the action of natural selection or because of biased mutational mechanisms.

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Figure 1: A protein network with two types of highly connected nodes (protein).
Figure 2: Correlations among the limb bones lead to variational modularity.
Figure 3: The developmental basis for variational independence among beak traits in Darwin finches.
Figure 4: Modularity of RNA secondary structure.
Figure 5: Evolution of modular circuits due to modular variation in the environment.

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Acknowledgements

In reviewing the literature we realized that the published work on this topic is by far too vast to be fairly covered in this paper. We apologize to all the authors whose work we could not mention. We thank A. Tanzer, A. Carson and J. Jarvis for comments about an earlier version of this paper. Work in the G.P.W. laboratory is supported by the US National Science Foundation (NSF) grant IOB-0445971. Work in the J.M.C. laboratory is supported by the NSF grant BCS-0725068 and the US National Institutes of Health grant DK055736. M.P. is supported by a Schroedinger Postdoctoral Fellowship of the Austrian Science Foundation (FWF).

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Glossary

Variational module

A set of covarying traits that vary relatively independently of other such sets of traits. Variational modules are recognized by higher than average correlations among traits.

Functional module

Features that act together in performing some discrete physiological function.

Developmental module

Either a part of an embryo that is quasi-autonomous with respect to pattern formation and differentiation, or an autonomous developmental signalling cascade.

Quasi-autonomy

A lower than average grade of connectedness: the elements of modules are highly interconnected, but to an increased extent are unconnected to other modules. Also called quasi-independence.

Cluster analytical methods

A family of computational methods used to classify a set of objects according to some measure of similarity or dissimilarity. Most frequently, hierarchical clustering methods are used, in which objects are put into a hierarchical scheme of classification.

Hub

A node of a network that is involved in a higher than average number of interactions with other nodes.

Correlation matrix

A table of the correlation coefficients among quantitative traits, which summarizes the degree to which different traits covary as a result of genetic and environmental influences. Sets of strongly covarying traits are called variational modules.

Serially homologous traits

Traits that are repeated within the organism, such as vertebrae in the body axis, teeth in the jaw or segments of repeated limbs.

Modular pleiotropy

A genetic architecture in which a set of genes tends to have pleiotropic effects on the same set of traits, but few and weaker effects on other traits.

Neural network

Neural networks are a class of mathematical and computational models that aim at simulating the activity of networks of nerve cells.

Differential epistasis

Describes a situation in which pleiotropic effects of a locus are affected differently by the genetic background.

Canalization

Coined by Conrad Waddington to describe the tendency of wild-type phenotypes to be more stable than mutant phenotypes. In recent literature, the same phenomenon is often called robustness and is intensely studied at all levels of organization from macromolecular structure to ecosystems.

rQTL

A region of the genome that influences the correlation (that is, the relationship) among two or more quantitative phenotypic traits, for example, the correlation between body size and limb length. These genomic regions thus affect the genetic architecture of the phenotype and potentially provide the genetic variation that is necessary for the evolution of modularity.

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Wagner, G., Pavlicev, M. & Cheverud, J. The road to modularity. Nat Rev Genet 8, 921–931 (2007). https://doi.org/10.1038/nrg2267

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