5,579 research outputs found
Metabolite concentrations, fluxes and free energies imply efficient enzyme usage.
In metabolism, available free energy is limited and must be divided across pathway steps to maintain a negative ΔG throughout. For each reaction, ΔG is log proportional both to a concentration ratio (reaction quotient to equilibrium constant) and to a flux ratio (backward to forward flux). Here we use isotope labeling to measure absolute metabolite concentrations and fluxes in Escherichia coli, yeast and a mammalian cell line. We then integrate this information to obtain a unified set of concentrations and ΔG for each organism. In glycolysis, we find that free energy is partitioned so as to mitigate unproductive backward fluxes associated with ΔG near zero. Across metabolism, we observe that absolute metabolite concentrations and ΔG are substantially conserved and that most substrate (but not inhibitor) concentrations exceed the associated enzyme binding site dissociation constant (Km or Ki). The observed conservation of metabolite concentrations is consistent with an evolutionary drive to utilize enzymes efficiently given thermodynamic and osmotic constraints
Simulating rare events using a Weighted Ensemble-based string method
We introduce an extension to the Weighted Ensemble (WE) path sampling method
to restrict sampling to a one dimensional path through a high dimensional phase
space. Our method, which is based on the finite-temperature string method,
permits efficient sampling of both equilibrium and non-equilibrium systems.
Sampling obtained from the WE method guides the adaptive refinement of a
Voronoi tessellation of order parameter space, whose generating points, upon
convergence, coincide with the principle reaction pathway. We demonstrate the
application of this method to several simple, two-dimensional models of driven
Brownian motion and to the conformational change of the nitrogen regulatory
protein C receiver domain using an elastic network model. The simplicity of the
two-dimensional models allows us to directly compare the efficiency of the WE
method to conventional brute force simulations and other path sampling
algorithms, while the example of protein conformational change demonstrates how
the method can be used to efficiently study transitions in the space of many
collective variables
A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data
A major theme in constraint-based modeling is unifying experimental data,
such as biochemical information about the reactions that can occur in a system
or the composition and localization of enzyme complexes, with highthroughput
data including expression data, metabolomics, or DNA sequencing. The desired
result is to increase predictive capability resulting in improved understanding
of metabolism. The approach typically employed when only gene (or protein)
intensities are available is the creation of tissue-specific models, which
reduces the available reactions in an organism model, and does not provide an
objective function for the estimation of fluxes, which is an important
limitation in many modeling applications. We develop a method, flux assignment
with LAD (least absolute deviation) convex objectives and normalization
(FALCON), that employs metabolic network reconstructions along with expression
data to estimate fluxes. In order to use such a method, accurate measures of
enzyme complex abundance are needed, so we first present a new algorithm that
addresses quantification of complex abundance. Our extensions to prior
techniques include the capability to work with large models and significantly
improved run-time performance even for smaller models, an improved analysis of
enzyme complex formation logic, the ability to handle very large enzyme complex
rules that may incorporate multiple isoforms, and depending on the model
constraints, either maintained or significantly improved correlation with
experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS,
and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not
required to compile the software, as intermediate C source code is available,
and binaries are provided for Linux x86-64 systems. FALCON requires use of the
COBRA Toolbox, also implemented in MATLAB.Comment: 30 pages, 12 figures, 4 table
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