Dynamic Flux Balance Analysis of a Genetic Engineered Cyanobacterium for Ethanol Production. Parameter Estimation

Abstract

In this work, we present a parameter estimation problem for Dynamic Flux Balance Analysis to study the production of ethanol by a mutant strain of the cyanobacterium Synechocystis sp. PCC 6803, as well as experimental data. This modified strain harbors the genes pdc and adhB from Zymomonas mobilis. The model includes two major components: (1) a dynamic model with mass balances for biomass, ethanol, nitrate, phosphate, internal nitrogen and phosphorus, and (2) a steady state genome-scale metabolic Linear Problem (LP) model. The biomass equation includes limiting functions for temperature and kinetics of growth inhibition by ethanol toxicity. Limitation of light by biomass accumulation is also taken into account. We formulate the dynamic parameter estimation problem with a weighted least-squares objective function, subject to dynamic mass balance equations at the bioreactor level and the intracellular LP model. The problem is solved in GAMS through a simultaneous optimization approach. The data sets for parameter estimation were obtained in experiments performed over 73 hours in batch liquid cultures. Numerical results provide useful insights on the ethanol production by the genetically modified strain within the context of genomic-scale cyanobacterial metabolism.