dc.contributor.author
Waldherr, Steffen
dc.contributor.author
Oyarzún, Diego A.
dc.contributor.author
Bockmayr, Alexander
dc.date.accessioned
2018-06-08T03:19:12Z
dc.date.available
2014-12-12T09:26:57.259Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/14905
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-19093
dc.description.abstract
The regulation of metabolic activity by tuning enzyme expression levels is
crucial to sustain cellular growth in changing environments. Metabolic
networks are often studied at steady state using constraint-based models and
optimization techniques. However, metabolic adaptations driven by changes in
gene expression cannot be analyzed by steady state models, as these do not
account for temporal changes in biomass composition. Here we present a dynamic
optimization framework that integrates the metabolic network with the dynamics
of biomass production and composition. An approximation by a timescale
separation leads to a coupled model of quasi-steady state constraints on the
metabolic reactions, and differential equations for the substrate
concentrations and biomass composition. We propose a dynamic optimization
approach to determine reaction fluxes for this model, explicitly taking into
account enzyme production costs and enzymatic capacity. In contrast to the
established dynamic flux balance analysis, our approach allows predicting
dynamic changes in both the metabolic fluxes and the biomass composition
during metabolic adaptations. Discretization of the optimization problems
leads to a linear program that can be efficiently solved. We applied our
algorithm in two case studies: a minimal nutrient uptake network, and an
abstraction of core metabolic processes in bacteria. In the minimal model, we
show that the optimized uptake rates reproduce the empirical Monod growth for
bacterial cultures. For the network of core metabolic processes, the dynamic
optimization algorithm predicted commonly observed metabolic adaptations, such
as a diauxic switch with a preference ranking for different nutrients, re-
utilization of waste products after depletion of the original substrate, and
metabolic adaptation to an impending nutrient depletion. These examples
illustrate how dynamic adaptations of enzyme expression can be predicted
solely from an optimization principle.
en
dc.rights.uri
http://www.elsevier.com/about/open-access/oa-and-elsevier/oa-license-policy#green-open-access
dc.subject.ddc
500 Naturwissenschaften und Mathematik::510 Mathematik
dc.title
Dynamic optimization of metabolic networks coupled with gene expression
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation
Journal of Theoretical Biology. - 365 (2015), S. 469-485
dcterms.bibliographicCitation.doi
10.1016/j.jtbi.2014.10.035
dcterms.bibliographicCitation.url
http://dx.doi.org/10.1016/j.jtbi.2014.10.035
refubium.affiliation
Mathematik und Informatik
de
refubium.mycore.fudocsId
FUDOCS_document_000000021447
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000004264
dcterms.accessRights.openaire
open access