A systems approach to plant bioprocess optimization

Abstract

SummaryA dynamic model for plant cell metabolism was used as a basis for a rational analysis of plant production potential in in vitro cultures. The model was calibrated with data from 3-L bioreactor cultures. A dynamic sensitivity analysis framework was developed to analyse the response curves of secondary metabolite production to metabolic and medium perturbations. Simulation results suggest that a straightforward engineering of cell metabolism or medium composition might only have a limited effect on productivity. To circumvent the problem of the dynamic allocation of resources between growth and production pathways, the sensitivity analysis framework was used to assess the effect of stabilizing intracellular nutrient concentrations. Simulations showed that a stabilization of intracellular glucose and nitrogen reserves could lead to a 116% increase in the specific production of secondary metabolites compared with standard culture protocol. This culture strategy was implemented experimentally using a perfusion bioreactor. To stabilize intracellular concentrations, adaptive medium feeding was performed using model mass balances and estimations. This allowed for a completely automated culture, with controlled conditions and predefined decision making algorithm. The proposed culture strategy leads to a 73% increase in specific production and a 129% increase in total production, as compared with a standard batch culture protocol. The sensitivity analysis on a mathematical model of plant metabolism thus allowed producing new insights on the links between intracellular nutritional management and cell productivity. The experimental implementation was also a significant improvement on current plant bioprocess strategies.

Analyzing the effect of decreasing cytosolic triosephosphate isomerase onSolanum tuberosumhairy root cells using a kinetic–metabolic model

“…However, yields still have to be enhanced to meet economical viability. The key nutrients affecting culture performance have been identified (Cloutier et al, 2008;Lamboursain and Jolicoeur, 2005), and it has been demonstrated that culture management can be improved using an in silico model describing cell behavior (Cloutier et al, 2009). Nevertheless, developments in metabolic engineering allowing to deeply modify a plant cell catalytic capacity may clearly benefit from having a better description of the regulation of the metabolic network.…”

Integrative Modeling of Gene Expression and Metabolic Networks of Arabidopsis Embryos for Identification of Seed Oil Causal Genes

“…In a previous metabolic modeling study on plant cells (Cloutier et al, 2009), a similar problem of under-determination did not hinder the analysis and predictive capacity of the model. An even larger model for cell signaling (Chen et al, 2009), with hundreds of states and parameters, was shown to be insightful, as long as it is trained against experimental data, as our model is.…”

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

“…In addition to the metabolic fluxes, dynamic models can reflect intracellular metabolites concentration not only at steady state but also in the transient time [10,11,149]. It consequently makes dynamic models a better candidate in comparison with their constraint-based counterparts for the implementation of monitoring and control strategies in bioprocess management [18,19,85,86,148]. However, the hindering obstacles for all kinetic models to reach genome-scale are limited available data for intracellular concentrations and the complex procedure of selecting kinetic rate laws with identifiable mechanistic parameters.…”

“…However, the extent of progress in the model application to accomplish each of these goals depends on several factors, including the industrial importance and the scientific significance of sought-after results. Based on this premise, metabolic modelling has gained substantial importance to enhance bioprocess optimization [18,19], where the increased efficiency leads to cost reduction in million-dollar order of magnitude, and to develop metabolic therapies [11,14], i.e., to push forward cancer treatment. In a broad classification, metabolic models are divided into four categories based on the capability of the model to distinguish between sub-populations of biomass (unsegregated/segregated) and the extent of recognizing the intracellular biochemical components (unstructured/structured) [17,20], (Figure 1).…”