0×104 (J/mol), , AEn = −5 866729×104, The factor [CB]/[CB0] co

0×104 (J/mol), , AEn = −5.866729×104, . The factor [CB]/[CB0] corrects LEnmax for changes of [CB]; the second factor is introduced to damp changes of [CB]. KBref is not constant, but depends on [Ca2+]. The stroke generating fluxes are given by: (A15a) (A15b) Both fluxes are identical as long as uncoupling is absent. The first factor corrects LStrmax = 4.6 × 10−4 (µM/ms)×(mol/J) for changes of [CBt] – [CB]. The second factor introduces [Ca2+] dependence of LStr. The third factor is responsible for the hyperbolic character

of the flux equation at constant [Ca2+] with KICB = −1.8 × 104 J/mol, which learn more represents the inhibition constant, KRref = 1.310889 × 10−4. Inhibitors,research,lifescience,medical λ values are not independent; this interdependency is given in Results. Uncoupling is formulated to occur in two steps, expressed by λStr1P = 0.15 and λStr2P = 0.85. SIMGLYgen (A16) . . . . . . . . . . . . . The above set of differential equations without a variable [Mg2+] ([Mg2+] = 0.8 mM = const.) is used to calculate the various Inhibitors,research,lifescience,medical points of figures (Figure 1B, Figure 2, Figure 3, and Figure 4) for a given [Ca2+] and various loads. As already mentioned, [Mg2+] is introduced as a variable only for conditions of very high power output leading to fatigue. From the output of the simulation many more variables, as shown here, can be obtained as functions

of time, Inhibitors,research,lifescience,medical which may often be helpful in understanding underlying mechanisms.
How topology shapes dynamics is a long-standing question in the field of network theory [1,2]. Many attempts have been formulated Inhibitors,research,lifescience,medical to understand the functional structure of metabolic networks from first principles using evolutionary, biochemical, or graph theoretical arguments [3,4,5,6,7,8]. Several works have argued that the network topology of metabolic systems is markedly optimized for robustness. For example, Marr et al. [9] used binary dynamic probes to demonstrate that on average fluctuations are dampened out in real metabolic networks.

Also, there seems to be a selection for minimal metabolic pathways, given the experimental conditions [10]. The accessible nutrients for a species may thus be inferred by analyzing the network topologies. Inhibitors,research,lifescience,medical Furthermore, robustness Dipeptidyl peptidase of metabolism against gene or reaction deletions has been explored using flux-balance analysis (FBA) [11]. Particularly, its capacity to predict gene essentiality with high accuracy for E. coli and Saccharomyces cerevisiae has turned FBA into a widely accepted method for in silico studies of metabolic states [12,13]. More recent refinements of FBA focus on the redistribution of fluxes due to gene deletions [14,15]. Along similar lines of research, metabolic reactions have been classified in several ways based on topological information [3,16,17,18]. Here we will focus on two recent examples providing such classifications: UPUC (uniquely producing/consuming) and SA (synthetic accessibility) reactions. UPUC metabolites have been introduced by Samal et al. [19].

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