15,400 research outputs found
Flux networks in metabolic graphs
A metabolic model can be represented as bipartite graph comprising linked
reaction and metabolite nodes. Here it is shown how a network of conserved
fluxes can be assigned to the edges of such a graph by combining the reaction
fluxes with a conserved metabolite property such as molecular weight. A similar
flux network can be constructed by combining the primal and dual solutions to
the linear programming problem that typically arises in constraint-based
modelling. Such constructions may help with the visualisation of flux
distributions in complex metabolic networks. The analysis also explains the
strong correlation observed between metabolite shadow prices (the dual linear
programming variables) and conserved metabolite properties. The methods were
applied to recent metabolic models for Escherichia coli, Saccharomyces
cerevisiae, and Methanosarcina barkeri. Detailed results are reported for E.
coli; similar results were found for the other organisms.Comment: 9 pages, 4 figures, RevTeX 4.0, supplementary data available (excel
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
Method for finding metabolic properties based on the general growth law. Liver examples. A General framework for biological modeling
We propose a method for finding metabolic parameters of cells, organs and
whole organisms, which is based on the earlier discovered general growth law.
Based on the obtained results and analysis of available biological models, we
propose a general framework for modeling biological phenomena and discuss how
it can be used in Virtual Liver Network project. The foundational idea of the
study is that growth of cells, organs, systems and whole organisms, besides
biomolecular machinery, is influenced by biophysical mechanisms acting at
different scale levels. In particular, the general growth law uniquely defines
distribution of nutritional resources between maintenance needs and biomass
synthesis at each phase of growth and at each scale level. We exemplify the
approach considering metabolic properties of growing human and dog livers and
liver transplants. A procedure for verification of obtained results has been
introduced too. We found that two examined dogs have high metabolic rates
consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per
day, and verified this using the proposed verification procedure. We also
evaluated consumption rate of nutrients in human livers, determining it to be
about 0.088 gram of nutrients per cubic centimeter of liver per day for males,
and about 0.098 for females. This noticeable difference can be explained by
evolutionary development, which required females to have greater liver
processing capacity to support pregnancy. We also found how much nutrients go
to biomass synthesis and maintenance at each phase of liver and liver
transplant growth. Obtained results demonstrate that the proposed approach can
be used for finding metabolic characteristics of cells, organs, and whole
organisms, which can further serve as important inputs for many applications in
biology (protein expression), biotechnology (synthesis of substances), and
medicine.Comment: 20 pages, 6 figures, 4 table
MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models
Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads
The protein cost of metabolic fluxes: prediction from enzymatic rate laws and cost minimization
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 3.8 and 2.7,
respectively, for the two kinds of data. ECM can be used to predict enzyme
levels and protein cost in natural and engineered pathways, 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
Irreversible thermodynamics of open chemical networks I: Emergent cycles and broken conservation laws
In this and a companion paper we outline a general framework for the
thermodynamic description of open chemical reaction networks, with special
regard to metabolic networks regulating cellular physiology and biochemical
functions. We first introduce closed networks "in a box", whose thermodynamics
is subjected to strict physical constraints: the mass-action law, elementarity
of processes, and detailed balance. We further digress on the role of solvents
and on the seemingly unacknowledged property of network independence of free
energy landscapes. We then open the system by assuming that the concentrations
of certain substrate species (the chemostats) are fixed, whether because
promptly regulated by the environment via contact with reservoirs, or because
nearly constant in a time window. As a result, the system is driven out of
equilibrium. A rich algebraic and topological structure ensues in the network
of internal species: Emergent irreversible cycles are associated to
nonvanishing affinities, whose symmetries are dictated by the breakage of
conservation laws. These central results are resumed in the relation between the number of fundamental affinities , that of broken
conservation laws and the number of chemostats . We decompose the
steady state entropy production rate in terms of fundamental fluxes and
affinities in the spirit of Schnakenberg's theory of network thermodynamics,
paving the way for the forthcoming treatment of the linear regime, of
efficiency and tight coupling, of free energy transduction and of thermodynamic
constraints for network reconstruction.Comment: 18 page
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