October 23, 2012

Scale and Evolution: a phenomonological perspective

What are the effects of scale on the evolutionary process and its outcomes? I have done a fair amount of thinking about this topic, and have come up with a few diagrams to present this idea more clearly (Figures 1 and 2). This model I am presenting  is a phenomonological model for understanding physiological complexity in animals. As such, it does not focus on the complex features of populations, particularly natural selection or drift, in any explicit way. However, these features can be incorporated when appropriate, and so may be useful for understanding evolutionary dynamics [1].

While relevant to the idea of multilevel selection [2], multiscale evolution is not intended to resolve this controversy. When I say the word “scale”, I actually mean the confluence of two factors: time and organization. Temporal scale can be measured in years, and only by looking at multiple timescales can we understand how processes unfold. However, organizational scale is much less straightforward. 


Figure 1. The hypothesized multiscalar evolution space: temporal vs. organizational scale. The red branching structures are lineages consisting of different biological entities (e.g. molecular networks) that evolve over time. The black arrows (or influence arcs) show the flow of influence between different organizational scales.


In Figure 1, I have selected key transitions in complexity, from molecules to phyletic groups. These are not size scales per se, nor are they nested hierarchies. For example, although organs are bigger than molecules, this is not always the basis of organizational levels (see ecosystems vs. phyletic groups). The foundation for my organizational scheme involves transitions [3] that enable biocomplexity. Moreover, while molecules make up molecular complexes and tissues make up organs, these relationships are not nested (e.g. not all tissues are part of a “parent” organ). In many cases, organizational scales are linked via trophic relationships [4]. In other cases (such as organisms -- communities -- societies), the relationships can be interpreted as nested hierarchies. 

Figure 2. Examples of biological systems at various organizational scales.

Another notable feature of Figure 1: influence arcs, denoted by black arrows. These arrows symbolize the flow of information and/or constraints across levels. For example, there are processes (e.g. reproductive preferences) at the community and societal levels that influence the composition of species, ecosystems, and even phyletic groups. Likewise, physiological processes at the level of organism can constrain (in deterministic fashion) the behavior of cells, connectivity of molecular networks, and the activity of molecules. While this might suggest that transfer functions are necessary to characterize interactions between each level, such tools are rather elusive, especially across biological contexts.

Figure 2 shows the types of systems at each level. This graph is less theoretical, showing the types of systems typical of their level of organization (e.g. coral communities, coastal marsh ecosystems) at the timescale of 10-100 years. In a theoretical sense, systems representing a number of these scales (notably phyletic groups) can exist both at relatively short timescales (the emergence of cichlid diversity in Lake Victoria [5]) and much longer timescales (the adaptive radiation of neural architectures in mammals [6]). It is my hope that studying the highly complex nature of evolution using this framework may lead to new insights. Otherwise, it is a work in progress. In a future post, I will approach the problem of scale and evolution from a more quantitative perspective. In the mean time, the authors of [7] have provided us with a guide (Figure 3) to what mathematical modeling and experimental approaches are typically used with each scale of physiological complexity, from single molecules to the organism.

Figure 3. Diagram of physiological scales of complexity (center) in relation to commonly-used mathematical modeling strategies (left) and experimental strategies (right). COURTESY: Figure 1 in [7].

NOTES:
[1] flexibility with specificity is a key positive attribute of a phenomenological model.

[2] A number of relevant papers exist on this topic. Two that are particularly relevant to this post are:

a) Hogeweg, P.   Multilevel processes in evolution and development: Computational models. Lecture Notes in Physics (Biological Evolution and Statistical Physics), 585, 217-239 (2002).

b) Wade, M.J., Wilson, D.S., Goodnight, C., Taylor, D., Bar-Yam, Y., de Aguiar, M., Stacey, B., Werfel, J., Hoelzer, G., Brodie, E., Fields, P., Breden, F., Linksvayer, T., Fletcher, J., Richerson, P.J., Bever, J.D., Van Dyken, J.D., Zee, P.   Multilevel and kin selection in a connected world. Nature, 463, E8-E9 (2010).

[3] Although the term “evolutionary transition” was originally intended to characterize the origins of biocomplexity, the same concept can be used to characterize extant biodiversity. For original reference, please see: Szathmary, E. and Maynard-Smith, J.M.   The Major Transitions in Evolution. Oxford University Press, Oxford, UK (1995).

[4] For more information on trophic models of organizational scale, please see: Alicea, B. Hierarchies of Biocomplexity: modeling life’s energetic complexity. arXiv Repository, arXiv:0810.4547 [q-bio.PE, q-bio.OT] (2008).

For more information on other multiscale relationships (such as causality between genotype and phenotype), please see: Noble, D.     Genes and causation. Philosophical Transactions of the Royal Society A, 366, 3001-3015 (2008).

[5] For an example, please see: Terai, Y., Takahashi, K., Nishida, M., Sato, T., Okada, N.   Using SINEs to probe ancient explosive speciation: "hidden" radiation of African cichlids? Molecular Biology and Evolution, 20(6), 924-930 (2003).

[6] For an example, please see: de Winter, W.   Evolutionary radiations and convergences in the structural organizations of Mammalian brains. Nature, 409, 710-714 (2001).


[7] Meier-Schellersheim, M., Fraser, I.D.C. and Klauschen, F. (2009) Multi-scale modeling in cell biology. Wiley Interdisciplinary Review of Systems Biology in Medicine, 1(1), 4–14.



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