Modelling of E.coli fermentations: comparison of multicompartment and variable structure models

Abstract

Two novel approaches for modelling processes that can be described by a sequence of phases (metabolic states) are suggested and applied to Escherichia Coli fermentations. The first approach uses a multi-compartment model framework, coupled with knowledge-based logic. In the second approach the multi-compartment model is reduced into the Variable Structure Model consisting of a battery of alternative submodels, each of which qualitatively represents one of the process steps. Furthermore, simulated intracellular process variables are compared with the output of a multi-wavelength fluorosensor and excitation-emission pairs that predict best these variables are identified.

A structured model for the simulation of bioreactors under transient conditions

“…Depending on the complexity of the biological systems and on our level of knowledge, the choice of the internal variables can be more or less biologically sound. 28 This modeling approach has produced a large number of submodels the classification of which is not easy to establish. Historically, one can distinguish multiple types of models such as: (i) Adaptation or time delay structured models 14,19,21 (ii) Compartment models 28,29 (iii) Cybernetic model 23,28 (iv) Metabolic models.…”

“…28 This modeling approach has produced a large number of submodels the classification of which is not easy to establish. Historically, one can distinguish multiple types of models such as: (i) Adaptation or time delay structured models 14,19,21 (ii) Compartment models 28,29 (iii) Cybernetic model 23,28 (iv) Metabolic models. [30][31][32] Whereas compartment and cybernetic models are well suited for the simulation of biological systems under transient conditions, metabolic models in general are based on two characteristics: the use of a metabolic network and the assumption of a pseudosteady state 24,33 which limits their applicability in the domain of transient simulations.…”

Recurrent neural network model of a fed-batch Saccharomyces cerevisiae fermentation process

“…Due to the particular characteristics of each microorganism, the application of both types of models is restricted to the conditions considered for its proposal. There are a great variety of models that let us correlate biomass with several fermentation parameters [ 6 ] . However, since those models had been established under specific conditions, the application to a wide range of microorganisms is very complex.…”

Multiple model approach to modelling of Escherichia coli fed-batch cultivation extracellular production of bacterial phytase

“…In many cases, the globally valid conventional numeric models, which describe the overall process behavior cannot be used in on-line monitoring and control, either because they do not describe the process well enough or contain too many poorly known parameters. Simple unstructured models, which account for key process variables (biomass, substrate and product concentrations) do not reflect metabolic changes and are unsuitable for many tasks (Zhang et al 1994;Tartakovsky et al 1997;Feng and Glassey, 2000;Venkat et al 2003). Model predictions could be improved using structured models, but these models incorporate too many equations and unknown parameters and provide a qualitative, rather than quantitative description of the process.…”

“…The state of the approaches to modelling and control problems arising working with systems of ever-increasing complexity and associated nonlinearity is presented by (Tartakovsky et al 1997). In this work the authors describe an approach which embraces a wide range of methods by developing complex models and controllers based on multiple submodels.…”