Metabolic states

Substances which promote the elimination of water by the kidney without major losses of salts (e.g. con-ivaptan, tolvaptan, SR121463A/B). They are particularly useful in situations where excess water needs to be eliminated without affecting the salt metabolism, like eu- or hypervolemic hyponatraemia, congestive heart failure, some stages of hypertension and some metabolic states. 

Two of the most widely used and detected UV filters in the environment and WWTPs are BP3 and 4-MBC. Thus, they were the selected compounds to study individually their degradation by fungi [44, 49]. Studies with BP1, not only a BP3 metabolite but also an industrial UV filter (but its use in cosmetics is not allowed) itself have also been performed. Studies in liquid media allow a better analysis and monitoring of many parameters, both the contaminant concentration and the fungal metabolic state such as glucose consumption and enzyme production. In these studies, the degradation process was performed with the fungus in form of pellets. 

The PHSS method of real-time H2S measurement allows for investigating the potentially complex H2S kinetic responses of organs, tissues, cells, and mitochondria as levels of 02 and NO as well as metabolic state are adjusted within physiological limits. Kinetic changes in H2S concentration continuously reported by the PHSS, which are not seen with other H2S measurement techniques, suggest potentially complex interactions of H2S production and consumption mechanisms. H2S may likely exist as a cellular pool of free and labile persulfides able to rapidly respond to redox challenges with production and consumption pathways that operate to maintain the pool. This possible scenario reinforces the need for the PHSS as a valuable tool to provide a continual report of H2S throughout the course of an experimental treatment or to accurately determine H2S levels in situ. 

We ll distinguish five basic metabolic states. There are three major metabolic signals that we ll consider—insulin, glucagon, and epinephrine. 

Metabolic state of mitochondria, cells or tissues respiration and glycolysis... 

For the quantitative description of the metabolic state of a cell, and likewise which is of particular interest within this review as input for metabolic models, experimental information about the level of metabolites is pivotal. Over the last decades, a variety of experimental methods for metabolite quantification have been developed, each with specific scopes and limits. While some methods aim at an exact quantification of single metabolites, other methods aim to capture relative levels of as many metabolites as possible. However, before providing an overview about the different methods for metabolite measurements, it is essential to recall that the time scales of metabolism are very fast Accordingly, for invasive methods samples have to be taken quickly and metabolism has to be stopped, usually by quick-freezing, for example, in liquid nitrogen. Subsequently, all further processing has to be performed in a way that prevents enzymatic reactions to proceed, either by separating enzymes and metabolites or by suspension in a nonpolar solvent. 

From a theoretical perspective, and provided that the network structure and some information about input and output fluxes are available, the intracellular steady-state fluxes can be estimated utilizing flux balance analysis. In conjunction with large-scale concentrations measurements, as described in Section IV, this allows, at least in principle, to specify the metabolic state of the system. 

The established tools of nonlinear dynamics provide an elaborate and versatile mathematical framework to examine the dynamic properties of metabolic systems. In this context, the metabolic balance equation (Eq. 5) constitutes a deterministic nonlinear dynamic system, amenable to systematic formal analysis. We are interested in the asymptotic, the linear stability of metabolic states, and transitions between different dynamic regimes (bifurcations). For a more detailed account, see also the monographs of Strogatz [290], Kaplan and Glass [18], as well as several related works on the topic [291 293],... 

Besides the two most well-known cases, the local bifurcations of the saddle-node and Hopf type, biochemical systems may show a variety of transitions between qualitatively different dynamic behavior [13, 17, 293, 294, 297 301]. Transitions between different regimes, induced by variation of kinetic parameters, are usually depicted in a bifurcation diagram. Within the chemical literature, a substantial number of articles seek to identify the possible bifurcation of a chemical system. Two prominent frameworks are Chemical Reaction Network Theory (CRNT), developed mainly by M. Feinberg [79, 80], and Stoichiometric Network Analysis (SNA), developed by B. L. Clarke [81 83]. An analysis of the (local) bifurcations of metabolic networks, as determinants of the dynamic behavior of metabolic states, constitutes the main topic of Section VIII. In addition to the scenarios discussed above, more complicated quasiperiodic or chaotic dynamics is sometimes reported for models of metabolic pathways [302 304]. However, apart from few special cases, the possible relevance of such complicated dynamics is, at best, unclear. Quite on the contrary, at least for central metabolism, we observe a striking absence of complicated dynamic phenomena. To what extent this might be an inherent feature of (bio)chemical systems, or brought about by evolutionary adaption, will be briefly discussed in Section IX. 

Specifically, SKM seeks to overcome several known deficiencies of stoichiometric analysis While stoichiometric analysis has proven immensely effective to address the functional capabilities of large metabolic networks, it fails for the most part to incorporate dynamic aspects into the description of the system. As one of its most profound shortcomings, the steady-state balance equation allows no conclusions about the stability or possible instability of a metabolic state, see also the brief discussion in Section V.C. The objectives and main requirements in devising an intermediate approach to metabolic modeling are as follows, a schematic summary is depicted in Fig. 25 ... 

Figure 26. The proposed workflow of structural kinetic modeling Rather than constructing a single kinetic model, an ensemble of possible models is evaluated, such that the ensemble is consistent with available biological information and additional constraints of interest. The analysis is based upon a (thermodynamically consistent) metabolic state, characterized by a vector S° and the associated flux v° v(S°). Since based only on the an evaluation of the eigenvalues of the Jacobian matrix are evaluated, the approach is (computationally) applicable to large scale system. Redrawn and adapted from Ref. 296.

The elements of the matrix A are fully specified by the stoichiometry matrix N and the metabolic state of the system. Usually, though not necessarily, the metabolic state corresponds to an experimentally observed state of the system and is characterized by steady-state concentrations S° and flux values v(S°). 

The interpretation of the elements of the matrix 0 is slightly more subtle, as they represent the derivatives of unknown functions fi(x) with respect to the variables x at the point x° = 1. Nevertheless, an interpretation of these parameters is possible and does not rely on the explicit knowledge of the detailed functional form of the rate equations. Note that the definition corresponds to the scaled elasticity coefficients of Metabolic Control Analysis, and the interpretation is reminiscent to the interpretation of the power-law coefficients of Section VII.C Each element 6% of the matrix measures the normalized degree of saturation, or likewise, the effective kinetic order, of a reaction v, with respect to a substrate Si at the metabolic state S°. Importantly, the interpretation of the elements of does again not hinge upon any specific mathematical representation of specific... 

In contrast, SKM does not assume knowledge of thespecific functional form of the rate equations. Rather, the system is evaluated in terms of generalized parameters, specified by the elements of the matrices A and 0X. In this sense, the matrices A and 0 x are bona fide parameters of the system The pathway is described in terms ofan average metabolite concentration S°, and a steady-state flux vector v°, together defining the metabolic state of the pathway. Additionally, we assume that the substrate only affects reaction v2, the saturation matrix is thus fully specified by a single parameter Of 6 [0,1], Note that the number of parameters is identical to the number used within the explicit equation. The structure of the parameter matrices is... 

The generalized parameters can be straightforwardly converted back into to the original kinetic parameters. Note that while this transformation is usually straightforward and almost always has a unique solution, the opposite does not hold The estimation of the metabolic state from explicit kinetic parameters is often computationally demanding and must not give rise to a unique solution. 

The metabolic state of the minimal model is specified by one independent flux value v° and 3 steady-state metabolite concentrations,... 

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