Metabolic Pathways and Activity-Dependent Modulation of Glutamate Concentration in the Human Brain

Glutamate is one of the most versatile molecules present in the human brain, involved in protein synthesis, energy production, ammonia detoxification, and transport of reducing equivalents. Aside from these critical metabolic roles, glutamate plays a major part in brain function, being not only the most abundant excitatory neurotransmitter, but also the precursor for γ-aminobutyric acid, the predominant inhibitory neurotransmitter. Regulation of glutamate levels is pivotal for normal brain function, as abnormal extracellular concentration of glutamate can lead to impaired neurotransmission, neurodegeneration and even neuronal death. Understanding how the neuron-astrocyte functional and metabolic interactions modulate glutamate concentration during different activation status and under physiological and pathological conditions is a challenging task, and can only be tentatively estimated from current literature. In this paper, we focus on describing the various metabolic pathways which potentially affect glutamate concentration in the brain, and emphasize which ones are likely to produce the variations in glutamate concentration observed during enhanced neuronal activity in human studies.

     

Metabolic Modeling and Simulation

Cover illustration Special issue: Metabolic Modeling and Simulation. Modeling of cellular metabolism has been a major area of research for bioengineers and biomedical researchers alike. This Special Issue collects a series of articles on methods of metabolic modeling, modeling of human metabolism, modeling of microbial metabolism and modeling of bioprocesses. This cover is a visual representation of the essence of metabolic engineering. Image: © rolffimages – Fotolia.com.     

Multi-timescale event-scheduling in multi-agent immune simulation models

Multi-agent (or MA) -based design approaches have received much research attention lately for modeling immunological systems due to their efficacy in representing non-heterogeneous behaviors in the population under dynamic environmental and topological conditions. The update scheme of a MA model refers to the frequency of agent state updates and how these are related in temporal order. In contrast to verifiable agent behavioral rules at the individual level, the update scheme is a design decision made by the model developer at the systems level that is subject to realism and computational efficiency issues that directly affect the credibility and the usefulness of the simulation results. Previous works have mainly focused on the issue of realism with respect to synchrony of updates but have often overlooked the necessary heterogeneity in update frequencies due to multi-timescales in immunological phenomena. To incorporate such multi-timescales for realism, the efficiency of the update scheme arises as a nontrivial issue. An event-scheduling based asynchronous update scheme has the advantage of allowing arbitrary smaller timescales for realism and avoiding unnecessary execution and delays to achieve efficiency. In this paper we present the application of the event-scheduling update scheme to realistically model the B cell life cycle, and empirically compare its simulation performance with the widely adopted uniform time-step update scheme. The simulation results show a significantly reduced execution time (40 times faster) and also reveal the conditions where the event-scheduling update scheme is superior.     

Ensemble Modeling for Aromatic Production in Escherichia coli

Ensemble Modeling (EM) is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning.     

Ensemble Modeling for Robustness Analysis in engineering non-native metabolic pathways

Metabolic pathways in cells must be sufficiently robust to tolerate fluctuations in expression levels and changes in environmental conditions. Perturbations in expression levels may lead to system failure due to the disappearance of a stable steady state. Increasing evidence has suggested that biological networks have evolved such that they are intrinsically robust in their network structure. In this article, we presented Ensemble Modeling for Robustness Analysis (EMRA), which combines a continuation method with the Ensemble Modeling approach, for investigating the robustness issue of non-native pathways. EMRA investigates a large ensemble of reference models with different parameters, and determines the effects of parameter drifting until a bifurcation point, beyond which a stable steady state disappears and system failure occurs. A pathway is considered to have high bifurcational robustness if the probability of system failure is low in the ensemble. To demonstrate the utility of EMRA, we investigate the bifurcational robustness of two synthetic central metabolic pathways that achieve carbon conservation: non-oxidative glycolysis and reverse glyoxylate cycle. With EMRA, we determined the probability of system failure of each design and demonstrated that alternative designs of these pathways indeed display varying degrees of bifurcational robustness. Furthermore, we demonstrated that target selection for flux improvement should consider the trade-offs between robustness and performance.     

Modeling of the Psychophysical Response Curves Using the Grand Canonical Ensemble in Statistical Physics

This paper aims to investigate the peripheral mechanism of taste perception by the use of the grand canonical ensemble in statistical physics. It allows a better understanding of this process and its interpretation at a microscopic level. The experimental part allowed us to obtain psychophysical curves relative to four nutritive sweeteners (sucrose, fructose, glucose, and maltitol). These curves represent the intensity of sweetness as a function of sugar concentration in water. A Sensory Measuring Unit for Recording Flux (SMURF) device is used to obtain intensity–time curves for each of the studied sweeteners. To model these curves we have established the expressions of some models using grand canonical ensemble formalism in statistical physics and applying some simplification approaches. The fitting of the psychophysical data with statistical models by numerical simulation demonstrates a good correlation between the models and the experimental curves. Some physicochemical parameters interfere in the expression of the established models. These parameters are classified in two categories: on one hand, the steric aspect, which is manifested by the density of taste receptor site, and the number of molecules per receptor site and on the other hand, an energetic aspect illustrated by the concentration at half saturation, which gives indirectly the sweet molecule-receptor site energy of interaction.     

Modeling Secondary Messenger Pathways in Neurovascular Coupling

Neurovascular coupling is the well-documented link between neural stimulation and constriction or dilation of the surrounding vasculature. Glial cells mediate this response via their unique anatomy, which connects neurons to arterioles. It is believed that calcium transients and the release of secondary messengers by these cells influence the vascular response. We present a model of intracellular calcium dynamics in an astrocyte (glial cell) and show that stable oscillatory behaviour is possible under certain conditions. We then couple this to a novel model for the relationship between calcium concentration and the production of vasoactive secondary messengers through a fatty-acid intermediate. The two secondary messengers modelled are epoxyeicosatrienoic and 20-hydroxyeicosatetraenoic acids (EET and 20-HETE, respectively). These secondary messengers are produced on different time scales, and we show how this supports the observation that the vasculature dilates rapidly in response to neural stimulation, before returning to baseline levels on a slower time scale.     

Modeling the Role of Negative Cooperativity in Metabolic Regulation and Homeostasis

A significant proportion of enzymes display cooperativity in binding ligand molecules, and such effects have an important impact on metabolic regulation. This is easiest to understand in the case of positive cooperativity. Sharp responses to changes in metabolite concentrations can allow organisms to better respond to environmental changes and maintain metabolic homeostasis. However, despite the fact that negative cooperativity is almost as common as positive, it has been harder to imagine what advantages it provides. Here we use computational models to explore the utility of negative cooperativity in one particular context: that of an inhibitor binding to an enzyme. We identify several factors which may contribute, and show that acting together they can make negative cooperativity advantageous.     

Multi-timescale dynamics study of FKBP12 along the rapamycin-mTOR binding coordinate

Drugs can affect function in proteins by modulating their flexibility. Despite this possibility, there are very few studies on how drug binding affects the dynamics of target macromolecules. FKBP12 (FK506 binding protein 12) is a prolyl cis-trans isomerase and a drug target. The immunosuppressant drug rapamycin exerts its therapeutic effect by serving as an adaptor molecule between FKBP12 and the cell proliferation regulator mTOR (mammalian target of rapamycin). To understand the role of dynamics in rapamycin-based immunosuppression and to gain insight into the role of dynamics in the assembly of supramolecular complexes, we used ¹⁵N, ¹³C, and ²H NMR spin relaxation to characterize FKBP12 along the binding coordinate that leads to cell cycle arrest. We show that sequential addition of rapamycin and mTOR leads to incremental rigidification of the FKBP12 backbone on the picosecond-nanosecond timescale. Both binding events lead to perturbation of main-chain and side-chain dynamics at sites distal to the binding interfaces, suggesting tight coupling interactions dispersed throughout the FKBP12-rapamycin interface. Binding of the first molecule, rapamycin, quenches microsecond-millisecond motions of the FKBP12 80's loop. This loop provides much of the surface buried at the protein-protein interface of the ternary complex, leading us to assert that preorganization upon rapamycin binding facilitates binding of the second molecule, mTOR. Widespread microsecond-millisecond motions of the backbone persist in the drug-bound enzyme, and we provide evidence that these slow motions represent coupled dynamics of the enzyme and isomerization of the bound drug. Finally, the pattern of microsecond-millisecond dynamics reported here in the rapamycin complex is dramatically different from the pattern in the complex with the structurally related drug FK506. This raises the important question of how two complexes that are highly isomorphic based on high-resolution static models have such different flexibilities in solution.     

Ensemble Modeling of the Likely Public Health Impact of a Pre-Erythrocytic Malaria Vaccine

Background

The RTS,S malaria vaccine may soon be licensed. Models of impact of such vaccines have mainly considered deployment via the World Health Organization''s Expanded Programme on Immunization (EPI) in areas of stable endemic transmission of Plasmodium falciparum, and have been calibrated for such settings. Their applicability to low transmission settings is unclear. Evaluations of the efficiency of different deployment strategies in diverse settings should consider uncertainties in model structure.

Methods and Findings

An ensemble of 14 individual-based stochastic simulation models of P. falciparum dynamics, with differing assumptions about immune decay, transmission heterogeneity, and treatment access, was constructed. After fitting to an extensive library of field data, each model was used to predict the likely health benefits of RTS,S deployment, via EPI (with or without catch-up vaccinations), supplementary vaccination of school-age children, or mass vaccination every 5 y. Settings with seasonally varying transmission, with overall pre-intervention entomological inoculation rates (EIRs) of two, 11, and 20 infectious bites per person per annum, were considered. Predicted benefits of EPI vaccination programs over the simulated 14-y time horizon were dependent on duration of protection. Nevertheless, EPI strategies (with an initial catch-up phase) averted the most deaths per dose at the higher EIRs, although model uncertainty increased with EIR. At two infectious bites per person per annum, mass vaccination strategies substantially reduced transmission, leading to much greater health effects per dose, even at modest coverage.

Conclusions

In higher transmission settings, EPI strategies will be most efficient, but vaccination additional to the EPI in targeted low transmission settings, even at modest coverage, might be more efficient than national-level vaccination of infants. The feasibility and economics of mass vaccination, and the circumstances under which vaccination will avert epidemics, remain unclear. The approach of using an ensemble of models provides more secure conclusions than a single-model approach, and suggests greater confidence in predictions of health effects for lower transmission settings than for higher ones. Please see later in the article for the Editors'' Summary     

Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI

Functional magnetic resonance imaging (fMRI) measures brain activity by detecting the blood-oxygen-level dependent (BOLD) response to neural activity. The BOLD response depends on the neurovascular coupling, which connects cerebral blood flow, cerebral blood volume, and deoxyhemoglobin level to neuronal activity. The exact mechanisms behind this neurovascular coupling are not yet fully investigated. There are at least three different ways in which these mechanisms are being discussed. Firstly, mathematical models involving the so-called Balloon model describes the relation between oxygen metabolism, cerebral blood volume, and cerebral blood flow. However, the Balloon model does not describe cellular and biochemical mechanisms. Secondly, the metabolic feedback hypothesis, which is based on experimental findings on metabolism associated with brain activation, and thirdly, the neurotransmitter feed-forward hypothesis which describes intracellular pathways leading to vasoactive substance release. Both the metabolic feedback and the neurotransmitter feed-forward hypotheses have been extensively studied, but only experimentally. These two hypotheses have never been implemented as mathematical models. Here we investigate these two hypotheses by mechanistic mathematical modeling using a systems biology approach; these methods have been used in biological research for many years but never been applied to the BOLD response in fMRI. In the current work, model structures describing the metabolic feedback and the neurotransmitter feed-forward hypotheses were applied to measured BOLD responses in the visual cortex of 12 healthy volunteers. Evaluating each hypothesis separately shows that neither hypothesis alone can describe the data in a biologically plausible way. However, by adding metabolism to the neurotransmitter feed-forward model structure, we obtained a new model structure which is able to fit the estimation data and successfully predict new, independent validation data. These results open the door to a new type of fMRI analysis that more accurately reflects the true neuronal activity.     

Modeling of Rhythmogenesis in an Ensemble of Cortical Neurons Connected with Electrical Synapses

Based on our own data on generation of spindle-like field electrical activity in neuronal barrels of the rat somatic cortex and also on the published data on the properties of voltage-dependent channels in the membranes of cortical cells, we developed a model of the ensemble (simple network) of neurons connected by electrical synapses. Such connections were found earlier in neurophysiological and ultramicroscopic studies. Model neurons with membranes having sodium, potassium, and calcium channels described in the literature were capable of generating bursting rhythmic impulse activity under conditions of switching off of synaptic connections between cells (isolation). With switching on of electrical synapses, spiking generated by separate neurons, which initially was nonsynchronous, became synchronized in time. Ipso facto, we demonstrated the ability of pacemaker oscillatory activity to be electrotonically synchronized in ensembles of neurons connected with electrical synapses.     

Mechanical Coupling between Myosin Molecules Causes Differences between Ensemble and Single-Molecule Measurements

In contracting muscle, individual myosin molecules function as part of a large ensemble, hydrolyzing ATP to power the relative sliding of actin filaments. The technological advances that have enabled direct observation and manipulation of single molecules, including recent experiments that have explored myosin's force-dependent properties, provide detailed insight into the kinetics of myosin's mechanochemical interaction with actin. However, it has been difficult to reconcile these single-molecule observations with the behavior of myosin in an ensemble. Here, using a combination of simulations and theory, we show that the kinetic mechanism derived from single-molecule experiments describes ensemble behavior; but the connection between single molecule and ensemble is complex. In particular, even in the absence of external force, internal forces generated between myosin molecules in a large ensemble accelerate ADP release and increase how far actin moves during a single myosin attachment. These myosin-induced changes in strong binding lifetime and attachment distance cause measurable properties, such as actin speed in the motility assay, to vary depending on the number of myosin molecules interacting with an actin filament. This ensemble-size effect challenges the simple detachment limited model of motility, because even when motility speed is limited by ADP release, increasing attachment rate can increase motility speed.     

Factors Governing Activity-Dependent Structural Plasticity of the Hypothalamoneurohypophysial System

1. The adult hypothalamoneurohypophysial system (HNS) undergoes reversible morphological changes in response to physiological stimulation.2. In the hypothalamus, stimulation of neurohormone secretion results in reducedastrocytic coverage of oxytocinergic somata and dendrites so that their surfaces becomedirectly juxtaposed. Concurrently, there is a significant increase in the number of GABAergic, glutamatergic, and noradrenergic synapses impinging on the neurons.3. In the neurohypophysis, stimulation induces retraction of pituicyte processes fromthe perivascular area and enlargement and multiplication of neurosecretory terminals.4. These neuronal-glial and synaptic changes are reversible with cessation of stimulation, thus rendering the HNS an excellent model to study physiologically linked structuralneuronal plasticity in the adult CNS.5. We still do not know the cellular mechanisms and factors underlying such plasticity.Recent studies indicate, however, that the adult HNS expresses molecular characteristicsnormally associated with histogenesis and/or tissue reorganization in developing or regenerating neural systems. They include expression of cell adhesion molecules such as the highlysialylated isoform of the neural cell adhesion molecule, PSA-NCAM, and the glycoproteins, F3 and tenascin-C.6. The expression of PSA-NCAM and tenascin-C does not show striking differencesin terms of age, sex or physiological condition but that of F3 varies considerably withneurohypophysial stimulation.7. We postulate that such molecular features allow magnocellular neurons and theirglia to undergo neuronal-glial and synaptic plasticity throughout life, provided the properstimulus intervenes.8. Thus, in the hypothalamic nuclei, centrally released oxytocin acting in synergy with steroids can induce such plasticity, while adrenaline, acting through -adrenergic mechanisms, does so in the neurohypophysis.     

All-Atom Ensemble Modeling to Analyze Small-Angle X-Ray Scattering of Glycosylated Proteins

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Highlights? MW determined from zero angle scattering is accurate for glycoproteins ? All-atom modeling with SAXS data can identify structural features of glycans ? SAXS ab initio reconstructions provide little structural information for glycans     

Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology.     

Tight Coupling of Metabolic Oscillations and Intracellular Water Dynamics in Saccharomyces cerevisiae

We detected very strong coupling between the oscillating concentration of ATP and the dynamics of intracellular water during glycolysis in Saccharomyces cerevisiae. Our results indicate that: i) dipolar relaxation of intracellular water is heterogeneous within the cell and different from dilute conditions, ii) water dipolar relaxation oscillates with glycolysis and in phase with ATP concentration, iii) this phenomenon is scale-invariant from the subcellular to the ensemble of synchronized cells and, iv) the periodicity of both glycolytic oscillations and dipolar relaxation are equally affected by D₂O in a dose-dependent manner. These results offer a new insight into the coupling of an emergent intensive physicochemical property of the cell, i.e. cell-wide water dipolar relaxation, and a central metabolite (ATP) produced by a robustly oscillating metabolic process.