Emergent Entanglement in Evolution of Biological Networks

Biological systems are made up of interacting parts that collectively form cohesive wholes. They are structured and highly dynamic, integrating molecular, cellular, and organismal processes. Systems can be modeled with networks, graph theoretic representations that describe how parts interact with each other at different time scales and levels of biological organization. A number of properties help understand network structure and dynamics, including measures of centrality, community structure, and hierarchy. Morphospaces of structure and hierarchical organization dissect what is possible and impossible in network makeup, locating biological networks in areas of phenotypic space that foster scalefreeness, modularity, and a bow-tie structure but away from randomness. Here we show the structure of time-dependent networks can hold deep evolutionary history. We focus on the oldest network and metabolism and study its origin and evolution. Tracing centrality measures onto a putative ancestral autotrophic metabolic core revealed the importance of purine biosynthesis and supporting metabolites of carbohydrate pathways. Vectors of protein fold abundance of enzymes layered metabolic subnetworks in a multidimensional space but placed nucleotide metabolism far away from the rest. Phylogenomic tree reconstructions revealed metabolism originated explosively in the purine biosynthesis subnetwork by gradual replacement of prebiotic chemistries and later evolved as a patchwork through waves of enzymatic recruitment. More importantly, phylogenomic analyses showed that hierarchical modularity and bow-tie structure were emergent and pervasive properties of network evolution. The rise of a nested hierarchy of modules in bow-tie entanglement substantiates a “linkage” theory of module emergence that explains biphasic evolutionary patterns of growth in various biological and technological systems through the coevolution of accretion and diversification processes. An “entanglement” theory extends the linkage theory by invoking an entropic-like force capable of driving network growth amid an interplay of neutral and adaptive evolutionary drivers. Our exploration of metabolic evolution not only deepens the understanding of life’s history but also charts a course for future investigations at the nexus of network science, evolutionary genomics, and structural biology.