Robustness, metabolic states

Figure 46. The scenario shown in Fig. 45 has profound consequences for the robustness of the metabolic state. Note that, even though the (nearest) distance to the bifurcation is similar in both cases, the probability that a perturbation of a given size will result in a loss of stability is markedly different.

Figure 47. The robustness of metabolic states. Shown is the probability that a randomly chosen state is unstable. Starting with initially 100% stable models, the parameters are subject to increasing perturbations of strength p, corresponding to a random walk in parameter space. (A) The initial states are chosen randomly from the parameter space. (B) The initial states are confined to a small region with 0.01 < < 0. Note that the state Catp exhibits a rapid decay in stability. The data...

S. Grimbs, J. Selbig, S. Bulik, H. G. Holzhiitter, and R. Steuer, The stability and robustness of metabolic states identifying stabilizing sites in metabolic networks. Mol. Syst. Biol. 3, 146 (2007). 

Landrum et al. (1992) developed a kinetic bioaccumulation model for PAHs in the amphipod Diporeia, employing first-order kinetic rate constants for uptake of dissolved chemical from the overlying water, uptake by ingestion of sediment, and elimination of chemical via the gills and feces. In this model, diet is restricted to sediment, and chemical metabolism is considered negligable. The model and its parameters, as Table 9.3 summarizes, treat steady-state and time-variable conditions. Empirically derived regression equations (Landrum and Poore, 1988 and Landrum, 1989) are used to estimate the uptake and elimination rate constants. A field study in Lake Michigan revealed substantial differences between predicted and observed concentrations of PAHs in the amphipod Diporeia. Until more robust kinetic rate constant data are available for a variety of benthic invertebrates and chemicals, this model is unlikely to provide accurate estimates of chemical concentrations in benthic invertebrates under field conditions. 

In what many consider to be a landmark publication on metabolomics, Fiehn et al. (2000) state it is crucial to perform unbiased (metabolite) analyses in order to define precisely the biochemical function of plant metabolism. The authors argue that for metabolomics/metabolite profiling to become a robust and sensitive method suited to automation, a mature technology such as gas chromatography-mass spectrometry (GC-MS) is required as an analytical technique. The authors go on to describe a simple sample preparation and analysis regime that allowed for the detection and quantification of more than 300 compounds from a single-leaf sample extract. 

Kurata H, Zhao Q, Okuda R, Shimizu K (2007) Integration of enzyme activities into metabolic flux distributions by elementary mode analysis. BMC Syst Biol 1 31 Larhlimi A, Bockmayr A (2009) A New constraint-based description of the steady-state flux cone of metabolic networks. Discret Appl Math 157 2257-2266 Larhlimi A, Blachon S, Selbig J, Nikoloski Z (2011) Robustness of metabolic networks a review of existing definitions. Biosystems 106 1-8... 

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