The Application And Design For ¹³ C Metabolic Flux Analysis And Optimization

Metabolic fluxes analysis has been regarded as an important quantity for metabolic engineering, they reveal cause-effect relationships between genetic modifications and resulting changes in metabolic activity. They are also 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-enricbed 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.The purpose of our work is to find an intelligent algorithm which suit for the metabolic fluxes analysis and parameter estimation of the dynamic biochemical systems.First, we introduce the main methods of metabolic flux analysis: stoichiometry matrix and carbon labeling experiments and isotope labeling experiments focus on theory and the transfer of carbon atoms in the basic operation and construction of a mathematical model. In this paper, flux estimation is formulated as a global optimization problem by carbon enrichment balances. Intelligent algorithms had been widely used in global optimization programming problems for the efficiency, convergency and robustness of the algorithmsSecond, a case study considering the estimation of cyclic pentose phosphate pathway and 5 parameters of a nonlinear biochemical dynamic model have been taken as a benchmark. Several intelligent optimization algorithms have been explored to the problem. Experiments show that quantum-behaved particle swarm optimization algorithm can estimate the flux with high accuracy and successfully reconstruct the metabolic networks.Third, the results show that a single optimization algorithm is ineffective. Combined with a variety of optimization algorithms to solve ¹³CMFA will be a good method. The Quantum-behaved PSO algorithm performances are illustrated and compared with ordinary least squares estimation through simulation of the E. coli metabolic network in a noisy environment. The LS-QPSO algorithm has a better performance as showed by the comparative experiments. By the simulation shows, for different optimization problems, often in a single optimization algorithm optimization of execution speed or precision can not get satisfactory results, so considering the advantage of a variety of optimization algorithms to form a hybrid algorithm will bring metabolic flux estimation rapid development...