66 research outputs found
Dynamic optimization of metabolic networks coupled with gene expression
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, 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.
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
Suboptimal triangular controller design methodology for full MIMO stable systems
This paper proposes a design methodology of triangular controllers for full MIMO stable plants. The procedure is based on an optimal design for a triangular model of the plant. Stability and appropriate performance can be
achieved by adjusting a set of design parameters involved in a weighting function. The resulting controller provides integration and can be computed analytically
Cascaded multilevel inverter with regeneration capability and reduced number of switches
Abstractâmultilevel converters are a very interesting alternative for medium and high power drives. One of the more flexible topologies of this type is the cascaded multicell converter. This paper proposes the use of a single-phase reduced cell suitable for cascaded multilevel converters. This cell uses a reduced singlephase active rectifier at the input and an H-bridge inverter at the output side. This topology presents a very good performance,
effectively controlling the waveform of the input current and of the output voltage and allowing operation in the motoring and regenerative mode. The results presented in this paper confirm that this medium voltage inverter effectively eliminates low frequency input current harmonics at the primary side of the transformer
and operates without problems in regenerative mod
Prediction of gene essentiality using machine learning and genome-scale metabolic models
The identification of essential genes, i.e. those that impair cell survival when deleted, requires large growth assays of knock-out strains. The complexity and cost of such experiments has triggered a growing interest in computational methods for prediction of gene essentiality. In the case of metabolic genes, Flux Balance Analysis (FBA) is widely employed to predict essentiality under the assumption that cells maximize their growth rate. However, this approach assumes that knock-out strains optimize the same objectives as the wild-type, which excludes cases in which deletions cause large physiological changes to meet other objectives for survival. Here, we resolve this limitation with a novel machine learning approach that predicts essentiality directly from wild-type flux distributions. We first project the wild-type FBA solution onto a mass flow graph, a digraph with reactions as nodes and edge weights proportional to the mass transfer between reactions, and then train binary classifiers on the connectivity of graph nodes. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli, achieving near state-of-the art prediction accuracy for essential genes. Our approach suggests that wild-type FBA solutions contain enough information to predict essentiality, without the need to assume optimality of deletion strains
Control structure and limitations of biochemical networks
Biochemical networks typically exhibit intricate
topologies that hinder their analysis with control-theoretic tools.
In this work we present a systematic methodology for the
identification of the control structure of a reaction network. The
method is based on a bandwidth reduction technique applied
to the incidence matrix of the networkâs graph. In addition,
in the case of mass-action and stable networks we show that
it is possible to identify linear algebraic dependencies between
the time-domain integrals of some speciesâ concentrations. We
consider the extrinsic apoptosis pathway and an activationâ
inhibition mechanism to illustrate the application of our result
Computation of Single-Cell Metabolite Distributions Using Mixture Models
Metabolic heterogeneity is widely recognised as the next challenge in our
understanding of non-genetic variation. A growing body of evidence suggests
that metabolic heterogeneity may result from the inherent stochasticity of
intracellular events. However, metabolism has been traditionally viewed as a
purely deterministic process, on the basis that highly abundant metabolites
tend to filter out stochastic phenomena. Here we bridge this gap with a general
method for prediction of metabolite distributions across single cells. By
exploiting the separation of time scales between enzyme expression and enzyme
kinetics, our method produces estimates for metabolite distributions without
the lengthy stochastic simulations that would be typically required for large
metabolic models. The metabolite distributions take the form of Gaussian
mixture models that are directly computable from single-cell expression data
and standard deterministic models for metabolic pathways. The proposed mixture
models provide a systematic method to predict the impact of biochemical
parameters on metabolite distributions. Our method lays the groundwork for
identifying the molecular processes that shape metabolic heterogeneity and its
functional implications in disease.Comment: 5 Figures, 3 Table
Opportunities at the interface of network science and metabolic modeling
Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology
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