The evolution of biological structure is driven by accretion and change.1 Accretion brings together disparate parts to form bigger wholes. Change provides opportunities for growth and innovation. Biological networks describe how parts associate with each other to form integrated systems, which are often structured hierarchically. Network structure can be explained with a biphasic (bow-tie) theory.2 In a first phase, parts are weakly linked and associate variously. As they diversify, they compete with each other and are selected for performance. The emerging interactions constrain their structure and associations. This causes parts to self-organize into modules with tight linkage. In a second phase, variants of the modules evolve and become new parts for a new generative cycle of higher-level organization. The paradigm predicts the rise of hierarchical modularity in evolving networks at different timescales and complexity levels, which was confirmed with phylogenomic and molecular simulation.3 Analyses of evolving networks describing the emergence of metabolism, the rise and diversification of the proteome, the evolution of the ribosome, and nanosecond-level change in protein loop dynamics consistently revealed an increase of hierarchical modularity and scale-free behavior as networks evolved.
We used MANET 3.0 to study the evolution of the structure of metabolic networks.4 Bipartite networks and their projections traced the evolutionary growth of metabolic pathways at mesonetwork, subnetwork and enzyme levels of organization (Fig. 1A). For example, the subnetwork projection of the subnetwork-enzyme bipartite network showed how nodes (subnetworks) became increasingly connected to each other through links (shared enzymes) as networks became structured with time (Fig. 1B). Conversely, the enzyme projection revealed growth of enzymes communities through sharing of subnetworks. While hierarchical modularity gradually increased in both projections, evolutionary constraints on network structure were stronger at lower levels of metabolic organization (Fig. 1C). These results support a ‘principle of granularity’ and Herbert A. Simon’s prediction: “Each of the parts of a nearly-decomposable system has strong internal links among its sub-parts, but the several top-level parts are bound together with each other only by comparatively weak linkages”.5


  1. Caetano-Anollés, G., Caetano-Anollés, K., Caetano-Anollés, D. (2018) Evolution of macromolecular structure: a ‘double tale’ of biological accretion and diversification. Sci. Prog. 101, 360-383.
  2. Mittenthal, J.E., Caetano-Anollés, D., Caetano-Anollés, G. (2012) Biphasic patterns of diversification and the emergence of modules. Front. Genet. 3, 147.
  3. Caetano-Anollés, G., Aziz, F.M., Mughal, F, Gräter, F., Koç, I., Caetano-Anollés, K., Caetano-Anollés, D. (2019) Emergence of hierarchical modularity in evolving networks uncovered by phylogenomic analysis. Evol. Bioinformatics 15, 1-18.
  4. Mughal, F. & Caetano-Anollés , G. (2019) MANET 3.0: Hierarchy and modularity in evolving metabolic networks. PLoS ONE, ms. submitted.
  5. Simon, H.A. (1997) Models of bounded rationality. MIT Press, Cambridge, MA.