Principles in the Evolution of Metabolic Networks
Hiroki R. Ueda, John B. Hogenesch
Abstract
Understanding design principles of complex cellular organization is one of the major challenges in biology. Recent analysis of the large-scale cellular organization has revealed the scale-free nature and robustness of metabolic and protein networks. However, the underlying evolutional process that creates such a cellular organization is not fully elucidated. To approach this problem, we analyzed the metabolic networks of 126 organisms, whose draft or complete genome sequences have been published. This analysis has revealed that the evolutional process of metabolic networks follows the same and surprisingly simple principles in Archaea, Bacteria and Eukaryotes; where highly linked metabolites change their chemical links more dynamically than less linked metabolites. Here we demonstrate that this rich-travel-more mechanism rather than the previously proposed rich-get-richer mechanism can generate the observed scale-free organization of metabolic networks. These findings illustrate universal principles in evolution of metabolic networks and suggest marked flexibility of metabolic network throughout evolution.
Create a lesson
Related papers
The influence of decay in dynamic models of gene regulatory networks
Oleksandr Pokutnyi, Felix Sadyrbaev
Enforcing Thermodynamic Consistency in Parameter Optimization and Bayesian Inference of Biochemical Reaction Networks
Federica Milinanni, Andrei Kramer-Miehe, Jan Glaubitz et al.
Uncertainty-aware integration of deuterium metabolic imaging with genome-scale metabolic modeling reveals diet-associated feasible-space remodeling in mouse skeletal muscle
Tin Yau Pang, Samia Maouhoub, Maruf Khan et al.
Artificial Intelligence-Assisted Systems Biology Reconstruction of Diabetic Bone Marrow Dysfunction
Paolo Madeddu
IMPPAT 3.0: An updated FAIR database of phytochemicals and formulations of Indian Medicinal plants
Shanmuga Priya Baskaran, Ajaya Kumar Sahoo, Priyotosh Sil et al.
Protocol for Discovering Convergent Molecular Networks across Multi-Omics Datasets Using Network Propagation
Charles O. Olwal, Declan M. Winters, Immy A. Ashley et al.