Development of a two-step segregated model for the optimization of plant cell growth

Abstract

A two-step approach was used to develop a segregated model for the growth of Eschscholtzia californica plant cell cultures. In the first step, a non-segregated model was developed. Its parameters were estimated using conventional methods. The exponential phase of biomass growth was adequately estimated using a first set of parameters obtained using 14 bioreactor cultures. The exponential growth phase was well predicted. On the other hand, the model was unable to predict the decline phase. The model predicted the biomass growth with an overall error of 17±9%. In the second step, a segregated model was developed to improve the understanding of the biomass state in a bioreactor. The biomass was divided into three components based on the hypothesis of different activities: small cell type a biomass, large cell type b biomass and large inactive cell type c biomass. To represent cell death, a mortality constant was associated with each type of biomass in the equation system. The model predicted the biomass growth phase with an overall error of 14±7%. The estimation of total biomass growth was improved over the non-segregated model since the former model predicted better all growth phases. The non-segregated model was successfully used to optimize the growth of E. californica cell suspension culture in bioreactor resulting in a 100% increase in dried biomass concentration and a 300% increase in biomass growth rate over a conventional batch culture.