Abstract

Hybrid kinetic models, linking structured cell metabolic processes to the dynamics of macroscopic variables of the bioreactor, are more and more used in engineering evaluations to derive more precise predictions of the process dynamics under variable operating conditions. Depending on the cell model complexity, such a math tool can be used to evaluate the metabolic fluxes in relation to the bioreactor operating conditions, thus suggesting ways to genetically modify the microorganism for certain purposes. Even if development of such an extended dynamic model requires more experimental and computational efforts, its use is advantageous. The approached probative example refers to a model simulating the dynamics of nanoscale variables from several pathways of the central carbon metabolism (CCM) of Escherichia coli cells, linked to the macroscopic state variables of a fed-batch bioreactor (FBR) used for the tryptophan (TRP) production. The used E. coli strain was modified to replace the PTS system for glucose (GLC) uptake with a more efficient one. The study presents multiple elements of novelty: (i) the experimentally validated modular model itself, and (ii) its efficiency in computationally deriving an optimal operation policy of the FBR.

Highlights

  • Over the last few decades, there has been a continuous trend to develop more and more effective bioreactors [1,2] “to industrialize important biosyntheses for producing fine chemicals used in the food, pharmaceutical, or detergent industry, by using freesuspended or immobilized cell cultures in suitable bioreactors”, as reviewed by Maria [3]

  • The present study presents multiple elements of novelty: (i) production of TRP by engineered E. coli has been extensively studied, “the need of multiple precursors for its synthesis and the complex regulations of the biosynthetic pathways make the achievement of a high product yield still very challenging” [35]

  • To support further engineering calculations, a reasonable extended hybrid modular approach was adapted from literature [35], by expressing the macroscopic main state variable species dynamics governing the fed-batch bioreactor (FBR) performance, as a function of intracellular species dynamics related to the cell central carbon metabolism (CCM) metabolic fluxes responsible for the TRP synthesis

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Summary

Introduction

Over the last few decades, there has been a continuous trend to develop more and more effective bioreactors [1,2] “to industrialize important biosyntheses for producing fine chemicals used in the food, pharmaceutical, or detergent industry, by using freesuspended or immobilized cell cultures (or enzymes) in suitable bioreactors (or enzymatic reactors)”, as reviewed by Maria [3]. The present study presents multiple elements of novelty: (i) production of TRP by engineered E. coli has been extensively studied, “the need of multiple precursors for its synthesis and the complex regulations of the biosynthetic pathways make the achievement of a high product yield still very challenging” [35] This engineering problem was solved here by using a model-based (in silico) approach, completed with a biological improvement of the used E. coli cell culture; (ii) the derived optimal operating policy of the FBR is given in time intervals (the so-called “time-arcs”) of equal length, with a reduced number, to be implemented. (iv) the results reveal the close link between the cell key metabolites and the FBR operating conditions; (v) the used hybrid bilevel kinetic model is complex enough to adequately represent the dynamics of the FBR state variables (i.e., the biomass growth, the GLC depletion, and the excreted TRP and PYR in the bulk phase), as well as the dynamics of the cell key species involved in the concerned reaction pathway modules, i.e., (a) glycolysis,. Zeng [76], Chen [74], Li et al [78], Niu et al [79], and Carmona et al [80]

Comparison the from reduced schemes
Experimental Bioreactor and theusing
The Structured Hybrid Kinetic Model of Maria
The FBR Dynamic Model
Ways to Intensify the TRP Production in the FBR
Preliminary Considerations
Selection of the FBR Control Variables
Optimization Problem Constraints
Ndiv and Operating Alternatives Choice
The Used Numerical Solvers
The Problem Solution Particularities
Optimization Results and Discussion
Conclusions
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