Making the right connections: biological networks in the light of evolution

Abstract

Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein-protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre-stage. We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves.

Figure 1

Recent examples of networks used in evolution-related studies in diverse areas. A: Metabolic network of central carbon metabolism in E. coli, as used for evaluating flux balance analysis (FBA) objective functions. B: Food-web network of species in the Burgess Shale. C: Correlation network of proteins affected in a bacterial experimental evolution. D: Gene regulatory network (GRN) for endomesodermal specification in sea star. E: Inferred ancestral chordate protein–protein interaction (PPI) network for bZIP transcription factors. F: Regulatory network of genes involved in the transition to flowering in Arabidopsis inferred from expression quantitative trait locus (eQTLs).

Figure 2

Illustration of some processes of network evolution. These processes range from A: the purely graph-theoretical concept of preferential attachment, via increasingly biologically motivated concepts of B: node duplication, C: re-wiring, D: node loss, E: sub-functionalization and F: neo-functionalization, to G: network duplication, analogous to a whole-genome duplication event.

Figure 3

Changing research paradigms in the study of biological network evolution. A: Throughout the development of network theory, biological networks have been of great interest as data-sets to be analysed alongside examples of technological (e.g. internet, world-wide-web, power grid) and social (e.g. friendship, collaboration) networks. Early work tended to focus on the development of simple models of archetypal network topologies. Although many authors were keen to address the evolution of biological networks, the evolutionary models developed were primarily designed to reproduce the simple topologies under consideration, and as such were rarely tested directly against the data. B: A more sophisticated research paradigm for studying the evolution of biological networks starts from the viewpoint that any evolutionary model should relate directly to the biological system under study, with reference to population genetics and genomics where appropriate. Using simulation and probabilistic inference methods, models of network evolution can be tested directly against the biological data, taking factors such as experimental uncertainties and biases into account.