Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability

Department of Biological Science, Florida State University, Tallahassee, FL 32306, USA.
Biosystems (Impact Factor: 1.55). 06/2003; 69(2-3):83-94.
Source: PubMed

ABSTRACT Evolvability is the ability to respond to a selective challenge. This requires the capacity to produce the right kind of variation for selection to act upon. To understand evolvability we therefore need to understand the variational properties of biological organisms. Modularity is a variational property, which has been linked to evolvability. If different characters are able to vary independently, selection will be able to optimize each character separately without interference. But although modularity seems like a good design principle for an evolvable organism, it does not therefore follow that it is the only design that can achieve evolvability. In this essay I analyze the effects of modularity and, more generally, pleiotropy on evolvability. Although, pleiotropy causes interference between the adaptation of different characters, it also increases the variational potential of those characters. The most evolvable genetic architectures may often be those with an intermediate level of integration among characters, and in particular those where pleiotropic effects are variable and able to compensate for each other's constraints.

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Available from: Thomas F Hansen, Sep 26, 2015
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    • "A property of living systems that is well suited to explain the origin of morphological disparity is their modular organization (Wagner 1996; Hansen 2003; Wagner et al. 2007; Pavlicev and Hansen 2011). Organisms are hypothesized to be constructed from distinct sub-units termed modules that are highly integrated internally and behave quasi-independently during ontogeny and evolution (Wagner 1996; von Dassow and Munro 1999; Hansen et al. 2003; Klingenberg et al. 2003; Wagner et al. 2007; Kuratani 2009). Modularity is tightly linked to the concept of morphological integration, which postulates that functionally or developmentally related traits should form highly cohesive morphological units (Olson and Miller 1958; Cheverud 1982; Zelditch 1987; Cheverud 1996; Chernoff and Magwene 1999). "
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    ABSTRACT: Actinopterygians demonstrate high levels of morphological disparity, especially in the variation of fin positions, sizes and shapes. One hypothesis to explain the diversity of fin morphologies is that it is facilitated by a modular organization. According to this hypothesis, fin modules would be quasi-independent during ontogeny or evolution, facilitating their evolvability. We investigated variational modularity of fins in two cyprinid species, the zebrafish (Danio rerio) and the Northern redbelly dace (Chrosomus eos), to determine which subsets of fins are quasi-independent and which are most highly integrated in positioning. Hypotheses of modularity were evaluated using a combination of methods suitable for analyses of landmarks. The hypothesis that the dorsal and anal fins belong to a posterior trunk and tail module is strongly supported, a finding that can be explained by the use of subcarangiform locomotion in these two species. There is also some support for the hypothesis that the paired fins and head region each constitute variational modules. The support for fin variational modules is weaker than expected considering the wealth of developmental evidence supporting fin modularity. This might be related to a dissociation of the fin positioning modules during actinopterygian evolution, a process that had already been suggested for the dorsal and anal fins. Alternatively, the fin modules inferred from developmental data might not directly translate into variational modules: variational modules can incorporate the signals from numerous partially overlapping developmental processes so that one to one correspondence between developmental and variational modules is not always expected.
    Evolutionary Biology 06/2015; DOI:10.1007/s11692-015-9324-9 · 2.61 Impact Factor
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    • "phenotype map (e.g. Mezey et al. 2000; Cheverud 2001; Hansen 2003; Pigliucci 2010) has several components. To a large extent, difficulties in this area derive from the arbitrary way we dissect the phenotype into individual characters, in the usually vain hope to find a simple one-to-one correspondence between genes and characters. "
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    • "metabolic networks (Parter et al., 2007) and cancer signalling networks (Takemoto and Kihara, 2013)). Nevertheless, scepticism still exists regarding the impact of environmental variability on modularity in intracellular networks (Clune et al., 2013; Hansen, 2003; Holme, 2011; Takemoto, 2013, 2012). "
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    ABSTRACT: Ecological networks exhibit non-random structural patterns, such as modularity and nestedness, which indicate ecosystem stability, species diversity, and connectance. Such structure-stability relationships are well known. However, another important perspective is less well understood: the relationship between the environment and structure. Inspired by theoretical studies that suggest that network structure can change due to environmental variability, we collected data on a number of empirical food webs and mutualistic networks and evaluated the effect of climatic seasonality on ecological network structure. As expected, we found that climatic seasonality affects ecological network structure. In particular, an increase in modularity due to climatic seasonality was observed in food webs; however, it is debatable whether this occurs in mutualistic networks. Interestingly, the type of climatic seasonality that affects network structure differs with ecosystem type. Rainfall and temperature seasonality influence freshwater food webs and mutualistic networks, respectively; food webs are smaller, and more modular, with increasing rainfall seasonality. Mutualistic networks exhibit a higher diversity (particularly of animals) with increasing temperature seasonality. These results confirm the theoretical prediction that the stability increases with greater perturbation. Although these results are still debatable because of several limitations in the data analysis, they may enhance our understanding of environment-structure relationships.
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