Specialization Can Drive the Evolution of Modularity

Organismal development and many cell biological processes are organized in a modular fashion, where regulatory molecules form groups with many interactions within a group and few interactions between groups. Thus, the activity of elements within a module depends little on elements outside of it. Modularity facilitates the production of heritable variation and of evolutionary innovations. There is no consensus on how modularity might evolve, especially for modules in development. We show that modularity can increase in gene regulatory networks as a byproduct of specialization in gene activity. Such specialization occurs after gene regulatory networks are selected to produce new gene activity patterns that appear in a specific body structure or under a specific environmental condition. Modules that arise after specialization in gene activity comprise genes that show concerted changes in gene activities. This and other observations suggest that modularity evolves because it decreases interference between different groups of genes. Our work can explain the appearance and maintenance of modularity through a mechanism that is not contingent on environmental change. We also show how modularity can facilitate co-option, the utilization of existing gene activity to build new gene activity patterns, a frequent feature of evolutionary innovations.     

Modularity in the mammalian dentition: mice and monkeys share a common dental genetic architecture

The concept of modularity provides a useful tool for exploring the relationship between genotype and phenotype. Here, we use quantitative genetics to identify modularity within the mammalian dentition, connecting the genetics of organogenesis to the genetics of population-level variation for a phenotype well represented in the fossil record. We estimated the correlations between dental traits owing to the shared additive effects of genes (pleiotropy) and compared the pleiotropic relationships among homologous traits in two evolutionary distant taxa-mice and baboons. We find that in both mice and baboons, who shared a common ancestor >65 Ma, incisor size variation is genetically independent of molar size variation. Furthermore, baboon premolars show independent genetic variation from incisors, suggesting that a modular genetic architecture separates incisors from these posterior teeth as well. Such genetic independence between modules provides an explanation for the extensive diversity of incisor size variation seen throughout mammalian evolution-variation uncorrelated with equivalent levels of postcanine tooth size variation. The modularity identified here is supported by the odontogenic homeobox code proposed for the patterning of the rodent dentition. The baboon postcanine pattern of incomplete pleiotropy is also consistent with predictions from the morphogenetic field model.     

A MODULAR FRAMEWORK CHARACTERIZES MICRO‐ AND MACROEVOLUTION OF OLD WORLD MONKEY DENTITIONS

The study of modularity can provide a foundation for integrating development into studies of phenotypic evolution. The dentition is an ideal phenotype for this as it is developmentally relatively simple, adaptively highly significant, and evolutionarily tractable through the fossil record. Here, we use phenotypic variation in the dentition to test a hypothesis about genetic modularity. Quantitative genetic analysis of size variation in the baboon dentition indicates a genetic modular framework corresponding to tooth type categories. We analyzed covariation within the dentitions of six species of Old World monkeys (OWMs) to assess the macroevolutionary extent of this framework: first by estimating variance–covariance matrices of linear tooth size, and second by performing a geometric morphometric (GM) analysis of tooth row shape. For both size and shape, we observe across OWMs a framework of anterior and postcanine modules, as well as submodularity between the molars and premolars. Our results of modularity by tooth type suggest that adult variation in the OWM dentition is influenced by early developmental processes such as odontogenesis and jaw patterning. This study presents a comparison of genotypic modules to phenotypic modules, which can be used to better understand their action across evolutionary time scales.     

The Developmental Basis of Variational Modularity: Insights from Quantitative Genetics,Morphometrics, and Developmental Biology

Groups of correlated characters (variational modules) often are considered to be the result of dissociated local developmental factors, i.e., of a modular genotype–phenotype map. But certain sets of pleiotropic factors can equally well induce modular phenotypic variation—no local developmental factors are necessary for a modular covariance structure. It is thus not possible to infer genetic or developmental modularity from standing variation alone. Yet, only for approximately linear genotype–phenotype maps is the induced covariance structure stable over changes of the phenotypic mean. For larger genetic and phenotypic variation, such as on a macroevolutionary level, developmental effects often are nonlinear and variational modularity remains stable only when it is realized by local dissociated developmental factors with no overlap of pleiotropic ranges. The evo-devo concept of modularity concurs only at this macroevolutionary level with the quantitative notion of variational modularity. Empirical evidence on the genetic and developmental architecture underlying phenotypic variation is inconclusive and partly subject to methodological problems. Many studies seem to indicate modularized phenotypic variation and local clusters of QTL effects, whereas other studies find support for several alternative models of modularity and report continuous distributions of QTL effects. This inconsistency partly results from the neglect of spatial relationships among the measured traits. Given the complex development of higher organisms, a combination of pleiotropic factors and more local developmental effects with a hierarchical, overlapping, and more or less continuous distribution appears most likely.     

Modularity in the evolution of yeast protein interaction network

Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.     

PERSPECTIVE: COMPLEX ADAPTATIONS AND THE EVOLUTION OF EVOLVABILITY

The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian process of mutation, recombination and selection is not universally effective in improving complex systems like computer programs or chip designs. For adaptation to occur, these systems must possess “evolvability,” i.e., the ability of random variations to sometimes produce improvement. It was found that evolvability critically depends on the way genetic variation maps onto phenotypic variation, an issue known as the representation problem. The genotype-phenotype map determines the variability of characters, which is the propensity to vary. Variability needs to be distinguished from variations, which are the actually realized differences between individuals. The genotype-phenotype map is the common theme underlying such varied biological phenomena as genetic canalization, developmental constraints, biological versatility, developmental dissociability, and morphological integration. For evolutionary biology the representation problem has important implications: how is it that extant species acquired a genotype-phenotype map which allows improvement by mutation and selection? Is the genotype-phenotype map able to change in evolution? What are the selective forces, if any, that shape the genotype-phenotype map? We propose that the genotype-phenotype map can evolve by two main routes: epistatic mutations, or the creation of new genes. A common result for organismic design is modularity. By modularity we mean a genotype-phenotype map in which there are few pleiotropic effects among characters serving different functions, with pleiotropic effects falling mainly among characters that are part of a single functional complex. Such a design is expected to improve evolvability by limiting the interference between the adaptation of different functions. Several population genetic models are reviewed that are intended to explain the evolutionary origin of a modular design. While our current knowledge is insufficient to assess the plausibility of these models, they form the beginning of a framework for understanding the evolution of the genotype-phenotype map.     

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with all compatible production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Overall, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.     

In search of the biological significance of modular structures in protein networks

Many complex networks such as computer and social networks exhibit modular structures, where links between nodes are much denser within modules than between modules. It is widely believed that cellular networks are also modular, reflecting the relative independence and coherence of different functional units in a cell. While many authors have claimed that observations from the yeast protein–protein interaction (PPI) network support the above hypothesis, the observed structural modularity may be an artifact because the current PPI data include interactions inferred from protein complexes through approaches that create modules (e.g., assigning pairwise interactions among all proteins in a complex). Here we analyze the yeast PPI network including protein complexes (PIC network) and excluding complexes (PEC network). We find that both PIC and PEC networks show a significantly greater structural modularity than that of randomly rewired networks. Nonetheless, there is little evidence that the structural modules correspond to functional units, particularly in the PEC network. More disturbingly, there is no evolutionary conservation among yeast, fly, and nematode modules at either the whole-module or protein-pair level. Neither is there a correlation between the evolutionary or phylogenetic conservation of a protein and the extent of its participation in various modules. Using computer simulation, we demonstrate that a higher-than-expected modularity can arise during network growth through a simple model of gene duplication, without natural selection for modularity. Taken together, our results suggest the intriguing possibility that the structural modules in the PPI network originated as an evolutionary byproduct without biological significance.     

SIZE VARIATION,GROWTH STRATEGIES,AND THE EVOLUTION OF MODULARITY IN THE MAMMALIAN SKULL

Allometry is a major determinant of within‐population patterns of association among traits and, therefore, a major component of morphological integration studies. Even so, the influence of size variation over evolutionary change has been largely unappreciated. Here, we explore the interplay between allometric size variation, modularity, and life‐history strategies in the skull from representatives of 35 mammalian families. We start by removing size variation from within‐species data and analyzing its influence on integration magnitudes, modularity patterns, and responses to selection. We also carry out a simulation in which we artificially alter the influence of size variation in within‐taxa matrices. Finally, we explore the relationship between size variation and different growth strategies. We demonstrate that a large portion of the evolution of modularity in the mammalian skull is associated to the evolution of growth strategies. Lineages with highly altricial neonates have adult variation patterns dominated by size variation, leading to high correlations among traits regardless of any underlying modular process and impacting directly their potential to respond to selection. Greater influence of size variation is associated to larger intermodule correlations, less individualized modules, and less flexible responses to natural selection.     

The relative efficiency of modular and non-modular networks of different size

Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity.     

The dichotomy in degree correlation of biological networks

Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled.     

THE EVOLVABILITY OF GROWTH FORM IN A CLONAL SEAWEED

Although modular construction is considered the key to adaptive growth or growth‐form plasticity in sessile taxa (e.g., plants, seaweeds and colonial invertebrates), the serial expression of genes in morphogenesis may compromise its evolutionary potential if growth forms emerge as integrated wholes from module iteration. To explore the evolvability of growth form in the red seaweed, Asparagopsis armata, we estimated genetic variances, covariances, and cross‐environment correlations for principal components of growth‐form variation in contrasting light environments. We compared variance–covariance matrices across environments to test environmental effects on heritable variation and examined the potential for evolutionary change in the direction of plastic responses to light. Our results suggest that growth form in Asparagopsis may constitute only a single genetic entity whose plasticity affords only limited evolutionary potential. We argue that morphological integration arising from modular construction may constrain the evolvability of growth form in Asparagopsis, emphasizing the critical distinction between genetic and morphological modularity in this and other modular taxa.     

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure

ABSTRACT: A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. RESULTS: We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. CONCLUSIONS: Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.     

DISSECTION OF EVOLUTIONARY NETWORKS TO ASSESS THEIR ROLE IN THE EVOLUTION OF ROBUSTNESS,FUNCTION, AND DIVERSIFICATION

Biological systems are remarkably robust in the face of environmental, mutational, and developmental perturbations. Analyses of molecular networks reveal recurrent features, such as modularity, that have been implicated in robustness and evolvability. Multiple theoretical models account for these features, yet few empirical tests of these models exist. Here I develop a set of broadly applicable methodologies to enable expanded empirical evaluation of model predictions. The methodologies focus on the inference and analysis of networks that depict evolutionary correlations among characters. I apply these methodologies to analyze an evolutionary network at a larger scale of organization among 42 stem anatomical and morphological characters of 52 species in the genus Adenia (Passifloraceae). I evaluate a model predicting that modular evolutionary networks will evolve in response to environmental change. The evolutionary network of Adenia is modular and “small‐world,” and the three diagnosed modules correspond roughly to functions of transport, storage, and mechanical support. The phylogenetically informed analyses suggest that the storage module is more impacted by environmental change than expected by chance. These results corroborate the hypothesis that modularity reduces the impact of environmental change, but this result requires further empirical evaluation that can be aided by the proposed methods in additional study systems.     

In search of morphological modules: a systematic review

Morphological modularity arises in complex living beings due to a semi‐independent inheritance, development, and function of body parts. Modularity helps us to understand the evolvability and plasticity of organismal form, and how morphological variation is structured during evolution and development. For this reason, delimiting morphological modules and establishing the factors involved in their origins is a lively field of inquiry in biology today. Although it is thought that modularity is pervasive in all living beings, actually we do not know how often modularity is present in different morphological systems. We also do not know whether some methodological approaches tend to reveal modular patterns more easily than others, or whether some factors are more related to the formation of modules or the integration of the whole phenotype. This systematic review seeks to answer these type of questions through an examination of research investigating morphological modularity from 1958 to present. More than 200 original research articles were gathered in order to reach a quantitative appraisal on what is studied, how it is studied, and how the results are explained. The results reveal an heterogeneous picture, where some taxa, systems, and approaches are over‐studied, while others receive minor attention. Thus, this review points out various trends and gaps in the study of morphological modularity, offering a broad picture of current knowledge and where we can direct future research efforts.     

Does nasal echolocation influence the modularity of the mammal skull?

In vertebrates, changes in cranial modularity can evolve rapidly in response to selection. However, mammals have apparently maintained their pattern of cranial integration throughout their evolutionary history and across tremendous morphological and ecological diversity. Here, we use phylogenetic, geometric morphometric and comparative analyses to test the hypothesis that the modularity of the mammalian skull has been remodelled in rhinolophid bats due to the novel and critical function of the nasal cavity in echolocation. We predicted that nasal echolocation has resulted in the evolution of a third cranial module, the ‘nasal dome’, in addition to the braincase and rostrum modules, which are conserved across mammals. We also test for similarities in the evolution of skull shape in relation to habitat across rhinolophids. We find that, despite broad variation in the shape of the nasal dome, the integration of the rhinolophid skull is highly consistent with conserved patterns of modularity found in other mammals. Across their broad geographical distribution, cranial shape in rhinolophids follows two major divisions that could reflect adaptations to dietary and environmental differences in African versus South Asian distributions. Our results highlight the potential of a relatively simple modular template to generate broad morphological and functional variation in mammals.     

Deciphering the Palimpsest: Studying the Relationship Between Morphological Integration and Phenotypic Covariation

Organisms represent a complex arrangement of anatomical structures and individuated parts that must maintain functional associations through development. This integration of variation between functionally related body parts and the modular organization of development are fundamental determinants of their evolvability. This is because integration results in the expression of coordinated variation that can create preferred directions for evolutionary change, while modularity enables variation in a group of traits or regions to accumulate without deleterious effects on other aspects of the organism. Using our own work on both model systems (e.g., lab mice, avians) and natural populations of rodents and primates, we explore in this paper the relationship between patterns of phenotypic covariation and the developmental determinants of integration that those patterns are assumed to reflect. We show that integration cannot be reliably studied through phenotypic covariance patterns alone and argue that the relationship between phenotypic covariation and integration is obscured in two ways. One is the superimposition of multiple determinants of covariance in complex systems and the other is the dependence of covariation structure on variances in covariance-generating processes. As a consequence, we argue that the direct study of the developmental determinants of integration in model systems is necessary to fully interpret patterns of covariation in natural populations, to link covariation patterns to the processes that generate them, and to understand their significance for evolutionary explanation.     

Modularity, evolvability, and adaptive radiations: a comparison of the hemi- and holometabolous insects

SUMMARY Despite recent attention given to the concept of modularity and its potential contribution to the evolvability of organisms, there has been little mention of how such a contribution may affect rates of diversification or how this would be assessed. A first key prediction is that lineages with relatively greater degrees of modularity in given traits should exhibit higher rates of diversification. Four general conditions for testing this prediction of the modular evolvability hypothesis are outlined here. The potential role of modularity as a deterministic factor in adaptive radiations is best examined by looking at historic patterns of diversification rather than just levels of extant diversity, the focus of most analyses of key innovations.
Recent developmental evidence supports the notion that phenotypes of juvenile and adult stages of insects with "complete" metamorphosis (Holometabola) are distinct developmental and evolvable modules compared to the highly correlated life stages of insects with "incomplete" metamorphosis (Hemimetabola). Family-level rates of diversification for these two groups were calculated from the fossil record. The Holometabola was found to have a significantly and characteristically higher rate of diversification compared to the less modular Hemimetabola, consistent with the idea that intrinsic differences in modularity can influence the long-term evolvability of organisms.
The modular evolvability hypothesis also makes a second key prediction: that characters in more modular clades will exhibit greater levels of variation due to their independence. This provides an independent, phenotypically based test of the hypothesis. We discuss here how this second prediction may be tested in the case of the Hemi- and Holometabola.     

The road to modularity

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.     

Analysis of a hyper-diverse seed dispersal network: modularity and underlying mechanisms

Mutualistic interactions involving pollination and ant-plant mutualistic networks typically feature tightly linked species grouped in modules. However, such modularity is infrequent in seed dispersal networks, presumably because research on those networks predominantly includes a single taxonomic animal group (e.g. birds). Herein, for the first time, we examine the pattern of interaction in a network that includes multiple taxonomic groups of seed dispersers, and the mechanisms underlying modularity. We found that the network was nested and modular, with five distinguishable modules. Our examination of the mechanisms underlying such modularity showed that plant and animal trait values were associated with specific modules but phylogenetic effect was limited. Thus, the pattern of interaction in this network is only partially explained by shared evolutionary history. We conclude that the observed modularity emerged by a combination of phylogenetic history and trait convergence of phylogenetically unrelated species, shaped by interactions with particular types of dispersal agents.