Article,
Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal
PLOS Computational Biology, 13 (4): e1005494+ (Apr 18, 2017)
DOI: 10.1371/journal.pcbi.1005494
Abstract
Energy metabolism is central to cellular biology. Thus, genome-scale models of heterotrophic unicellular species must account appropriately for the utilization of external nutrients to synthesize energy metabolites such as ATP. However, metabolic models designed for flux-balance analysis (FBA) may contain thermodynamically impossible energy-generating cycles: without nutrient consumption, these models are still capable of charging energy metabolites (such as ADP→ATP or NADP+→NADPH). Here, we show that energy-generating cycles occur in over 85\% of metabolic models without extensive manual curation, such as those contained in the ModelSEED and MetaNetX databases; in contrast, such cycles are rare in the manually curated models of the BiGG database. Energy generating cycles may represent model errors, e.g., erroneous assumptions on reaction reversibilities. Alternatively, part of the cycle may be thermodynamically feasible in one environment, while the remainder is thermodynamically feasible in another environment; as standard FBA does not account for thermodynamics, combining these into an FBA model allows erroneous energy generation. The presence of energy-generating cycles typically inflates maximal biomass production rates by 25\%, and may lead to biases in evolutionary simulations. We present efficient computational methods (i) to identify energy generating cycles, using FBA, and (ii) to identify minimal sets of model changes that eliminate them, using a variant of the GlobalFit algorithm. Genome-scale metabolic models are routinely used to simulate the growth of unicellular organisms, and are likely to become an important tool in the medical sciences. The most popular method employed for this task is flux balance analysis (FBA), a simplified mathematical description able to describe the simultaneous activity of hundreds of biochemical reactions. Cellular functions are often dependent on the availability of sufficient energy, and thus a correct representation of energy metabolism appears crucial to metabolic modeling. However, we found that the majority of FBA models generated directly from genome sequences, as well as a minority of carefully curated models, are capable of generating energy out of thin air. These models charge energy metabolites such as ATP without any nutrient uptake. We named the corresponding sets of reactions ” erroneous energy generating cycles” (EGCs) and developed a high-throughput algorithm for their identification. We found EGCs in 238 (68\%) of 350 metabolic models from three different databases. We developed a second, fully automated method for EGC removal. Simulations on the corrected models typically showed growth rates that were 25\% slower than in the original models, demonstrating the importance of checking metabolic model reconstructions for EGCs.
