Integrating Kinetic Model of <i>E</i>. <i>coli - Table 3 </i> with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism

<p>Integrating Kinetic Model of <i>E</i>. <i>coli - Table 3 </i> with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism</p

Kinetic model simulation trajectories stabilizing back onto steady state solution.

<p>This plot of the trajectories of 4 kinetic model metabolite concentrations over time shows that simulation initiated a small distance away from the steady state allowed the system to relax back onto the Keio steady state (dashed horizontal lines), also proving the state to be stable. Concentrations of all other metabolites also relaxed back onto their respective Keio steady state values, a metabolic profile referred to as the Keio phenotype.</p

Plots of the optimized parameters determined for ICDH reaction equation.

<p>[A] Plot of the experimental measurements of Ogawa <i>et al</i>, (2007) (hollow circles), for different fixed concentrations of inhibitor pep ([pep] = 0mM, 0.2mM, 1mM and 5mM are given by blue, black, red and green lines respectively). The solution of the reaction equation with optimized parameters (solid respectively coloured curves) is superimposed onto the data points. The optimization resulted in a very close fit with the measured data. [B] Similar plot, but of only the fitting of the kinetics of the uninhibited reaction. [C] Similar plot, but of only the inhibiting term of the reaction equation, equation given in plot.</p

Plots and schematic showing metabolic profile of alternative steady state found.

<p>Plots along left illustrate the convergence of the model metabolite concentrations (solid lines) onto a steady state different from the Keio phenotype (dotted lines). Simulations initiated randomly in the metabolic phase space always and only ever converged onto these two stable metabolic steady states. The kinetic model schematic illustrates the percentage difference in metabolite concentrations (coloured rectangles next to metabolite names) and fluxes (black horizontal bars) between the achieved alternative steady state and the Keio phenotype.</p

Schematic of the kinetic model reaction network with metabolite regulation.

<p>A schematic of the network of reactions and metabolites of the kinetic model, including the metabolite regulation of the respective reactions. The red and blue lines represent enzyme kinetic regulation by metabolite inhibition (red) and non-essential metabolite activation (blue), respectively. Grey dotted lines represent the account of net flux from the reactions connecting metabolites to the rest of the genome-scale metabolic network (connecting reactions).</p

Percentage Increase in Steady State Metabolite Concentrations, from the Keio Phenotype to the Alternative Phenotype.

<p>Percentage Increase in Steady State Metabolite Concentrations, from the Keio Phenotype to the Alternative Phenotype.</p

Eigenvalues of the Kinetic Model System at the Keio Steady State (aex = a x 10^x).

<p>Eigenvalues of the Kinetic Model System at the Keio Steady State (aex = a x 10^x).</p

Comparison of relative flux ranges predicted by the model and those reported in Keio database.

<p>Both plots [A] and [B] compare upper and lower flux values (relative to GLCptspp reaction) predicted from a flux variability analysis of the genome-scale model (solid lines) and those reported from the carbon-13 metabolic flux analysis in Keio database (dotted lines). Lower flux values are darker lines and upper flux values are lines lighter in shade. The range of flux based on the Keio database was calculated as the variance of 4 replicates; upper (grey dotted line) and lower bound (black dotted line) is +/- 2 standard deviations from the mean, respectively. [A] The comparison is made using the original <i>iAF1260 E</i>. <i>coli</i> genome-scale model, before reparameterization. [B] The comparison is made using the optimally reparameterized version of the <i>iAF1260 E</i>. <i>coli</i> genome-scale model.</p

MOESM7 of Elucidation of the co-metabolism of glycerol and glucose in Escherichia coli by genetic engineering, transcription profiling, and 13C metabolic flux analysis

Additional file 7. Metabolic pathways involved in glycerol and glucose dissimilations and biosynthesis of 1,2-propanediol in E. coli. Broken lines illustrate multiple steps. aceE, aceF pyruvate dehydrogenase genes; adhE aldehyde-alcohol dehydrogenase gene; aldA lactaldehyde dehydrogenase gene; dhaKLM dihydroxyacetone kinase genes; fbaA, fbaB fructose bisphosphate aldolase genes; fucO 1,2-propanediol reductase gene; gldA glycerol dehydrogenase gene; glk glucokinase gene; gloA glyoxylase type I gene; gloB glyoxylase type II gene; glpD glycerol-3-phosphate dehydrogenase gene; ldhA lactate dehydrogenase gene; lpdA lipoamide dehydrogenase gene; mgsA methylglyoxal synthase gene; pflB pyruvate formate-lyase gene; pykA, pykF pyruvate kinase gens; tpiA triosephosphate isomerase gene

The role of the non-profit sector in the mental health care

This diploma thesis called "The role of the nonprofit sector in the mental health care" deals with problems expressed in the context of nongovernmental organizations that are regarded as a significant stakeholder very important actors in the issue of mental disease. Besides the state that creates predominantly political conception concerning mental health and also mostly ensures some constituents of mental health care (i.e. in the psychiatric asylums) there is also nonprofit sector playing an important role. We can observe its importance on the level of creation of the new mental health conceptions as well as on the level of response to the continuing process of deinstitutionalization of psychiatric care manifested in wider approach to mental disease and moreover its services with which the nonprofit sector completes and sometimes substitutes the role of the state. This thesis firstly deals with the mental disease as the integral part of the complex state of health of each of us, which importance is often underestimated and consequently insufficiently reflected in mental health policy in the ČR (and not only here) which should follow the world wide trend of finding out the negative consequences of mental diseases. After grounding the nonprofit sector in social and legal frame I continue in analyzing and..