Metabolic modeling analysis

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 ... 

Within this contribution, our aim was to summarize and describe the ways and means of modelmaking. In our opinion, and reflecting the structure of this contribution, the construction and analysis of metabolic models rests upon five main pillars (i) The expertise and knowledge of classic biochemistry, defining the building blocks and their interactions (as described in Section III). 

P. J. Mulquiney and P. W. Kuchel, Model of 2,3 bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations Computer simulation and metabolic control analysis. Biochem. J. 342 (3), 597 604 (1999). 

METABOLIC CONTROL ANALYSIS METABOLONS META-MODEL FMN,... 

Lion Biosciences is the supplier of the iDEA Metabolism software package as well as other ADME/T services (289). The iDEA software simulates metabolism and predicts a compound s metabolic behavior in humans. The Metabolism Module consists of a data expert module to perform data fitting and analysis of collected in vitro data and the physiological metabolism model. The physiological metabolism model is constructed from proprietary database of 64 clinically tested compounds. Additionally, the metabolism module automatically calculates the Michaelis-Menten constants Km and VjIiax for the kinetic analysis of metabolism turnover (289). 

Metabolic flux analysis Cellular metabolites and metabolic fluxes can be combined into a series of balance equations, not unlike a series of (bio)chemical reactions in a kinetic model. Metabolic flux analysis is the description of the components and their connections in a metabolic network. 

Bottom-up systems biology does not rely that heavily on Omics. It predates top-down systems biology and it developed out of the endeavors associated with the construction of the first mathematical models of metabolism in the 1960s [10, 11], the development of enzyme kinetics [12-15], metabolic control analysis [16, 17], biochemical systems theory [18], nonequilibrium thermodynamics [6, 19, 20], and the pioneering work on emergent aspects of networks by researchers such as Jacob, Monod, and Koshland [21-23]. 

Many methods have been developed for model analysis for instance, bifurcation and stability analysis [88, 89], parameter sensitivity analysis [90], metabolic control analysis [16, 17, 91] and biochemical systems analysis [18]. One highly important method for model analysis and especially for large models, such as many silicon cell models, is model reduction. Model reduction has a long history in the analysis of biochemical reaction networks and in the analysis of nonlinear dynamics (slow and fast manifolds) [92-104]. In all cases, the aim of model reduction is to derive a simplified model from a larger ancestral model that satisfies a number of criteria. In the following sections we describe a relatively new form of model reduction for biochemical reaction networks, such as metabolic, signaling, or genetic networks. 

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