Understanding the evolution of functional redundancy in metabolic networks

Gayathri Sambamoorthy and Karthik Raman, Bioinformatics (2018).
Share
tweet

Abstract

Metabolic networks are highly robust to perturbations by employing redundant genes or reactions. Synthetic lethals are reaction pairs which upon deletion individually do not result in a lethal phenotype but when absent together cause lethality. Organisms often gain genes or reactions upon adaptation to new environments and thus acquire new functionalities. Our study reveals the adaptive functional redundancies that are acquired in the process of evolution and how selective pressures retain the existing lethal interactions. In this work, we aimed to understand functional redundancy of metabolic networks and how evolution shapes such redundancies. To this end, we generated random metabolic networks in glucose and analysed the single lethals and synthetic double lethals that occur in these random networks. Our analysis established that nature tends to harbour higher levels of functional redundancies when compared to random networks. Further, we could identify that a single reaction can partner with numerous other reactions in different scenarios to form a synthetic lethal pair. We also organisms can exhibit different compensations in various environments and can also have lethal pairs common across environments. Our analysis also revealed how reactions with very different functionalities could form a lethal pair to compensate for one another. Thus, our work establishes an understanding of the evolution of functional redundancy in metabolic networks and also throws light on various compensation mechanisms that exist to enhance robustness.