Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal.
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 c...
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doaj-54a6b497bcb04441a517b85e8557eeec2020-11-25T01:32:25ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-04-01134e100549410.1371/journal.pcbi.1005494Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal.Claus Jonathan FritzemeierDaniel HartlebBalázs SzappanosBalázs PappMartin J LercherEnergy 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.http://europepmc.org/articles/PMC5413070?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Claus Jonathan Fritzemeier Daniel Hartleb Balázs Szappanos Balázs Papp Martin J Lercher |
spellingShingle |
Claus Jonathan Fritzemeier Daniel Hartleb Balázs Szappanos Balázs Papp Martin J Lercher Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. PLoS Computational Biology |
author_facet |
Claus Jonathan Fritzemeier Daniel Hartleb Balázs Szappanos Balázs Papp Martin J Lercher |
author_sort |
Claus Jonathan Fritzemeier |
title |
Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. |
title_short |
Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. |
title_full |
Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. |
title_fullStr |
Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. |
title_full_unstemmed |
Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. |
title_sort |
erroneous energy-generating cycles in published genome scale metabolic networks: identification and removal. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2017-04-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC5413070?pdf=render |
work_keys_str_mv |
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