Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network

<p>Abstract</p> <p>Background</p> <p>Constraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. However, metabolites are key players in the metabolic network and the current reaction-centric approach may not account for the effect of metabolite perturbation on the cellular physiology due to the inherent limitation in model formulation. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network.</p> <p>Results</p> <p>Presented herein is a metabolite-centric approach of analyzing the metabolic network by including the turnover rate of metabolite, known as flux-sum, as key descriptive variable within the model formulation. By doing so, the effect of varying metabolite flux-sum on physiological change can be simulated by resorting to mixed integer linear programming. From the results, we could classify various metabolite types based on the flux-sum profile. Using the <it>i</it>AF1260 <it>in silico </it>metabolic model of <it>Escherichia coli</it>, we demonstrated that this novel concept complements the conventional reaction-centric analysis.</p> <p>Conclusions</p> <p>Metabolite flux-sum analysis elucidates the roles of metabolites in the network. In addition, this metabolite perturbation analysis identifies the key metabolites, implicating practical application which is achievable through metabolite flux-sum manipulation in the areas of biotechnology and biomedical research.</p

Linear-T resistivity and change in Fermi surface at the pseudogap critical point of a high-Tc superconductor

A fundamental question of high-temperature superconductors is the nature of the pseudogap phase which lies between the Mott insulator at zero doping and the Fermi liquid at high doping p. Here we report on the behaviour of charge carriers near the zero-temperature onset of that phase, namely at the critical doping p* where the pseudogap temperature T* goes to zero, accessed by investigating a material in which superconductivity can be fully suppressed by a steady magnetic field. Just below p*, the normal-state resistivity and Hall coefficient of La1.6-xNd0.4SrxCuO4 are found to rise simultaneously as the temperature drops below T*, revealing a change in the Fermi surface with a large associated drop in conductivity. At p*, the resistivity shows a linear temperature dependence as T goes to zero, a typical signature of a quantum critical point. These findings impose new constraints on the mechanisms responsible for inelastic scattering and Fermi surface transformation in theories of the pseudogap phase.Comment: 24 pages, 6 figures. Published in Nature Physics. Online at http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys1109.htm

Signatures of arithmetic simplicity in metabolic network architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity

Incoherent non-Fermi liquid scattering in a Kondo lattice

One of the most notorious non-Fermi liquid properties of both archetypal heavy-fermion systems [1-4] and the high-Tc copper oxide superconductors [5] is an electrical resistivity that evolves linearly with temperature, T. In the heavy-fermion superconductor CeCoIn5 [5], this linear behaviour was one of the first indications of the presence of a zero-temperature instability, or quantum critical point. Here, we report the observation of a unique control parameter of T-linear scattering in CeCoIn5, found through systematic chemical substitutions of both magnetic and non-magnetic rare-earth, R, ions into the Ce sub-lattice. We find that the evolution of inelastic scattering in Ce1-xRxCoIn5 is strongly dependent on the f-electron configuration of the R ion, whereas two other key properties -- Cooper-pair breaking and Kondo-lattice coherence -- are not. Thus, T-linear resistivity in CeCoIn5 is intimately related to the nature of incoherent scattering centers in the Kondo lattice, which provides insight into the anomalous scattering rate synonymous with quantum criticality [7].Comment: 4 pages, 3 figures (published version

DNA content of a functioning chicken kinetochore

© The Author(s) 2014. In order to understand the three-dimensional structure of the functional kinetochore in vertebrates, we require a complete list and stoichiometry for the protein components of the kinetochore, which can be provided by genetic and proteomic experiments. We also need to know how the chromatin-containing CENP-A, which makes up the structural foundation for the kinetochore, is folded, and how much of that DNA is involved in assembling the kinetochore. In this MS, we demonstrate that functioning metaphase kinetochores in chicken DT40 cells contain roughly 50 kb of DNA, an amount that corresponds extremely closely to the length of chromosomal DNA associated with CENP-A in ChIP-seq experiments. Thus, during kinetochore assembly, CENP-A chromatin is compacted into the inner kinetochore plate without including significant amounts of flanking pericentromeric heterochromatin. © 2014 The Author(s).Wellcome Trust [grant number 073915]; Wellcome Trust Centre for Cell Biology (core grant numbers 077707 and 092076); Darwin Trust of Edinburg

Spin-orbit density wave induced hidden topological order in URu2Si2

The conventional order parameters in quantum matters are often characterized by 'spontaneous' broken symmetries. However, sometimes the broken symmetries may blend with the invariant symmetries to lead to mysterious emergent phases. The heavy fermion metal URu2Si2 is one such example, where the order parameter responsible for a second-order phase transition at Th = 17.5 K has remained a long-standing mystery. Here we propose via ab-initio calculation and effective model that a novel spin-orbit density wave in the f-states is responsible for the hidden-order phase in URu2Si2. The staggered spin-orbit order 'spontaneous' breaks rotational, and translational symmetries while time-reversal symmetry remains intact. Thus it is immune to pressure, but can be destroyed by magnetic field even at T = 0 K, that means at a quantum critical point. We compute topological index of the order parameter to show that the hidden order is topologically invariant. Finally, some verifiable predictions are presented.Comment: (v2) Substantially modified from v1, more calculation and comparison with experiments are include

Understanding the Adaptive Growth Strategy of Lactobacillus plantarum by In Silico Optimisation

In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether long-term adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy

Development of a framework for metabolic pathway analysis-driven strain optimization methods

Genome-scale metabolic models (GSMMs) have become important assets for rational design of compound overproduction using microbial cell factories. Most computational strain optimization methods (CSOM) using GSMMs, while useful in metabolic engineering, rely on the definition of questionable cell objectives, leading to some bias. Metabolic pathway analysis approaches do not require an objective function. Though their use brings immediate advantages, it has mostly been restricted to small scale models due to computational demands. Additionally, their complex parameterization and lack of intuitive tools pose an important challenge towards making these widely available to the community. Recently, MCSEnumerator has extended the scale of these methods, namely regarding enumeration of minimal cut sets, now able to handle GSMMs. This work proposes a tool implementing this method as a Java library and a plugin within the OptFlux metabolic engineering platform providing a friendly user interface. A standard enumeration problem and pipeline applicable to GSMMs is proposed, making use by the community simpler. To highlight the potential of these approaches, we devised a case study for overproduction of succinate, providing a phenotype analysis of a selected strategy and comparing robustness with a selected solution from a bi-level CSOM.The authors thank the project “DeYeastLibrary—Designer yeast strain library optimized for metabolic engineering applications”, Ref. ERA-IB-2/0003/2013, funded by national funds through “Fundação para a Ciência e Tecnologia / Ministério da Ciência, Tecnologia e Ensino Superior”.info:eu-repo/semantics/publishedVersio

Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Shewanella oneidensis MR-1 sequentially utilizes lactate and its waste products (pyruvate and acetate) during batch culture. To decipher MR-1 metabolism, we integrated genome-scale flux balance analysis (FBA) into a multiple-substrate Monod model to perform the dynamic flux balance analysis (dFBA). The dFBA employed a static optimization approach (SOA) by dividing the batch time into small intervals (i.e., ∼400 mini-FBAs), then the Monod model provided time-dependent inflow/outflow fluxes to constrain the mini-FBAs to profile the pseudo-steady-state fluxes in each time interval. The mini-FBAs used a dual-objective function (a weighted combination of “maximizing growth rate” and “minimizing overall flux”) to capture trade-offs between optimal growth and minimal enzyme usage. By fitting the experimental data, a bi-level optimization of dFBA revealed that the optimal weight in the dual-objective function was time-dependent: the objective function was constant in the early growth stage, while the functional weight of minimal enzyme usage increased significantly when lactate became scarce. The dFBA profiled biologically meaningful dynamic MR-1 metabolisms: 1. the oxidative TCA cycle fluxes increased initially and then decreased in the late growth stage; 2. fluxes in the pentose phosphate pathway and gluconeogenesis were stable in the exponential growth period; and 3. the glyoxylate shunt was up-regulated when acetate became the main carbon source for MR-1 growth