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. 2010 Jun 1;26(12):i255–i260. doi: 10.1093/bioinformatics/btq183

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© The Author(s) 2010. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fig. 1. — The figure illustrates the associations between variables in IOMA's optimization problem imposed by the various constraints. Rows represent enzymes and columns represent growth conditions—i.e. the j-th column representing the flux distribution for the j-th condition (denoted by . Mass-balance [Equation (6)] and reaction directionality [Equation (7)] create dependencies between fluxes through different enzymes in one condition, irrespectively of all others conditions (i.e. associating fluxes in one column). The enzyme-kinetic constraint [Equation (8)] associates between fluxes through one enzyme in different growth conditions (via the enzyme's parameters v_max⁺ and v_max⁻, which are condition-invariant), irrespectively of all other enzymes (i.e. associating fluxes in one row). The latter constraint is defined only for a core set of enzymes for which metabolomic and proteomic data is available (marked in blue). The union of both types of row and column constraints in IOMA's optimization indirectly associates between many additional fluxes through various enzymes in different conditions.

Inline graphic — The figure illustrates the associations between variables in IOMA's optimization problem imposed by the various constraints. Rows represent enzymes and columns represent growth conditions—i.e. the j-th column representing the flux distribution for the j-th condition (denoted by . Mass-balance [Equation (6)] and reaction directionality [Equation (7)] create dependencies between fluxes through different enzymes in one condition, irrespectively of all others conditions (i.e. associating fluxes in one column). The enzyme-kinetic constraint [Equation (8)] associates between fluxes through one enzyme in different growth conditions (via the enzyme's parameters v_max⁺ and v_max⁻, which are condition-invariant), irrespectively of all other enzymes (i.e. associating fluxes in one row). The latter constraint is defined only for a core set of enzymes for which metabolomic and proteomic data is available (marked in blue). The union of both types of row and column constraints in IOMA's optimization indirectly associates between many additional fluxes through various enzymes in different conditions.