Self-adaptive evolutionary algorithm based methods for quantification in metabolic systems

Metabolic fluxes have been regarded as an important quantity for metabolic engineering as they reveal cause-effect relationships between genetic modifications and resulting changes in metabolic activity and are used as a prerequisite for the design of optimal whole cell biocatalysts. The intracellular fluxes must be estimated due to the inability to measure them directly. A particular useful technique involves the use of ¹³C-enriched substrates and the measurement of label distribution generated for each intermediate to uncover all unmeasured fluxes by solving the label balance equations, e.g. isotopomer balances, at steady state. However, the formation of these equations typically requires tedious algebraic manipulation and in many cases the resulting equations must be solved numerically, due to the nonlinearity and high dimensionality. Here we present three different evolutionary algorithm (EA) based approaches in combination with the least squares algorithm to show the applicability of EAs in metabolic flux quantification. The performance of the algorithms are illustrated and discussed through the simulation of the cyclic pentose phosphate network in a noisy environment and the identifiability problem is also considered.