dc.contributor.author
Noor, Elad
dc.contributor.author
Flamholz, Avi
dc.contributor.author
Bar-Even, Arren
dc.contributor.author
Davidi, Dan
dc.contributor.author
Milo, Ron
dc.contributor.author
Liebermeister, Wolfram
dc.date.accessioned
2018-06-08T10:29:58Z
dc.date.available
2017-02-20T09:52:39.602Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/20538
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-23839
dc.description.abstract
Bacterial growth depends crucially on metabolic fluxes, which are limited by
the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount
per unit flux is a major determinant of metabolic strategies both in evolution
and bioengineering. It depends on enzyme parameters (such as kcat and KM
constants), but also on metabolite concentrations. Moreover, similar amounts
of different enzymes might incur different costs for the cell, depending on
enzyme-specific properties such as protein size and half-life. Here, we
developed enzyme cost minimization (ECM), a scalable method for computing
enzyme amounts that support a given metabolic flux at a minimal protein cost.
The complex interplay of enzyme and metabolite concentrations, e.g. through
thermodynamic driving forces and enzyme saturation, would make it hard to
solve this optimization problem directly. By treating enzyme cost as a
function of metabolite levels, we formulated ECM as a numerically tractable,
convex optimization problem. Its tiered approach allows for building models at
different levels of detail, depending on the amount of available data.
Validating our method with measured metabolite and protein levels in E. coli
central metabolism, we found typical prediction fold errors of 4.1 and 2.6,
respectively, for the two kinds of data. This result from the cost-optimized
metabolic state is significantly better than randomly sampled metabolite
profiles, supporting the hypothesis that enzyme cost is important for the
fitness of E. coli. ECM can be used to predict enzyme levels and protein cost
in natural and engineered pathways, and could be a valuable computational tool
to assist metabolic engineering projects. Furthermore, it establishes a direct
connection between protein cost and thermodynamics, and provides a physically
plausible and computationally tractable way to include enzyme kinetics into
constraint-based metabolic models, where kinetics have usually been ignored or
oversimplified.
en
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit
dc.subject.ddc
500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie
dc.title
The Protein Cost of Metabolic Fluxes
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation
PLoS Comput Biol: - 12 (2016), 11, Artikel Nr. e1005167
dc.title.subtitle
Prediction from Enzymatic Rate Laws and Cost Minimization
dcterms.bibliographicCitation.doi
10.1371/journal.pcbi.1005167
dcterms.bibliographicCitation.url
http://dx.doi.org/10.1371/journal.pcbi.1005167
refubium.affiliation
Charité - Universitätsmedizin Berlin
de
refubium.mycore.fudocsId
FUDOCS_document_000000026371
refubium.note.author
Der Artikel wurde in einer reinen Open-Access-Zeitschrift publiziert.
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000007714
dcterms.accessRights.openaire
open access