Fig. 4 Scaling laws distinguish biochemical networks from random networks across levels of organization. Scaling laws distinguish biochemical networks.

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Fig. 4 Scaling laws distinguish biochemical networks from random networks across levels of organization. Scaling laws distinguish biochemical networks from random networks across levels of organization. Shown are random reaction networks created by sampling biochemical reactions from a flat distribution (left column), frequency-sampled random reaction networks created by sampling reactions based on the frequency distribution observed across all organisms (center column), and random genome networks (right column). Merged networks composed of individuals include bacteria only (light blue), archaea only (dark blue), eukarya only (blue-green), and all domains combined (purple). (A) Scaling of biochemical diversity. Diversity measures and fit are as described in Fig. 3. For reference, all real biochemical network data from Fig. 3 are shown in light gray. Additional measures are shown in fig. S5. (B) Scaling of network structure. Measure and fit descriptions match those described in Fig. 3. For reference, all real biological networks from Fig. 3 are shown in light gray. Additional measures are shown in fig. S5, and scaling for bipartite networks is shown in fig. S6. We found that random reaction networks do not recover the same fit functions as real biological networks for assortativity and clustering, whereas frequency-sampled random reaction networks and random genome networks only differed for assortativity, but nonetheless were statistically distinguishable from real biochemical networks for some measures. Hyunju Kim et al. Sci Adv 2019;5:eaau0149 Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).