Additional file 10.

Alternate optima. To identify alternate flux solutions that can equally satisfy the problem, i.e. yield the same optimal solution, we performed additional simulations. The MILP was re-solved after adding a constraint (z*) for a single flux of the original flux distribution which was set to either 1.01-fold ( <a onClick="popup('http://www.biomedcentral.com/1752-0509/6/114/mathml/M29','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1752-0509/6/114/mathml/M29">View MathML</a>) or 0.99-fold ( <a onClick="popup('http://www.biomedcentral.com/1752-0509/6/114/mathml/M30','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1752-0509/6/114/mathml/M30">View MathML</a>) of its original calculated flux value (v0). The optimization problem was repeated with substrate combinations which were identified with the highest or lowest efficiency value while satisfying (1) a baseline ATP consumption rate and (2) a target function of the cardiomyocyte. This table includes all calculated flux solutions yielding the same optimal solution as with the original optimization problem. Furthermore, an overview is given of alternate flux solutions for fluxes representing external substrate and oxygen uptake. Statistical significance between flux solutions for the analysis of alternate flux solutions was determined by use of 1-way ANOVA.

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Karlst├Ądt et al. BMC Systems Biology 2012 6:114   doi:10.1186/1752-0509-6-114