Spontaneous reaction silencing in metabolic optimization.
Metabolic reactions of single-cell organisms are routinely observed to become dispensable or even incapable of carrying activity under certain circumstances. Yet, the mechanisms as well as the range of conditions and phenotypes associated with this behavior remain very poorly understood. Here we predict computationally and analytically that any organism evolving to maximize growth rate, ATP production, or any other linear function of metabolic fluxes tends to significantly reduce the number of active metabolic reactions compared to typical nonoptimal states. The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organism to grow at all. We show that this massive spontaneous reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity. Our results help explain existing experimental data on intracellular flux measurements and the usage of latent pathways, shedding new light on microbial evolution, robustness, and versatility for the execution of specific biochemical tasks. In particular, the identification of optimal reaction activity provides rigorous ground for an intriguing knockout-based method recently proposed for the synthetic recovery of metabolic function.
Nishikawa
Takashi
T
Division of Mathematics and Computer Science, Clarkson University, Potsdam, New York, United States of America.
Gulbahce
Natali
N
Motter
Adilson E
AE
D000465:Algorithms; D001419:Bacteria / Q000235:genetics / Q000254:growth & development / Q000378:metabolism*; D001669:Biochemical Phenomena / Q000235:genetics / Q000502:physiology; D005075:Biological Evolution; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004777:Environment; D004926:Escherichia coli / Q000235:genetics / Q000254:growth & development / Q000378:metabolism*; D053858:Metabolic Networks and Pathways / Q000235:genetics / Q000502:physiology*; D008954:Models, Biological