Metabolic modeling stoichiometric analysis

In this section, we describe a recently proposed approach that aims overcome some of the difficulties [23, 84, 296, 325] Structural Kinetic Modeling (SKM) seeks to provide a bridge between stoichiometric analysis and explicit kinetic models of metabolism and represents an intermediate step on the way from topological analysis to detailed kinetic models of metabolic pathways. Different from approximative kinetics described above, SKM is based on those properties that are a priori independent of the functional form of the rate equation. 

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 25. Structural Kinetic Modeling seeks to keep the advantages of stoichiometric analysis, while incorporating dynamic properties into the description of the system. Specifically, SKM aims to give a quantitative account of the possible dynamics of a metabolic network.

We emphasize that these results have several implications for the modeling and analysis of metabolic systems. First, stoichiometric properties alone are not... 

The catabolism of 2 includes 118 reactions many of which are reversible (Fig. 3.5). Since kinetic data on micro-metabolites are difficult to determine experimentally, and in order to obtain an overall view of the xenobiotic metabolism, a stoichiometric model of the full network of degradation pathways of 2 was set up in addition to the network shown in Fig. 3.5. This network was then analyzed by means of elementary flux mode analysis [78]. 

A more popular form of stoichiometric analysis is the analysis of flux distributions that are consistent with system steady state (Note that in the terminology of metabolic modelling, the rate of a reaction at system steady state is referred to as a flux.) This type of analysis can be done directly on the N matrix because of its central role in the description of the mass balances of all the variable intermediates in a network. 

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. 

FIGURE 23.6 Stoichiometric modeling of metabolic networks (Patil et al., 2004). TABLE 23.2 Techniques Available for Data Generation and Analysis (Lee et al., 2005)... 

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