###### Abstract  
Hybrid kinetic models, linking structured models of cell (nano-scale) metabolic processes to the dynamics of macroscopic variables of the bioreactor, are proven to lead to more precise predictions of all key-species dynamics under variable operating conditions, being of an exceptional importance in engineering evaluations to in-silico (math-model-based) determine the optimal operating mode of a fed-batch bioreactor (FBR). Even if such extended dynamic models require more experimental and computational efforts, their use has proven to be advantageous. The approached probative example refers to the simulation of the dynamics of some key species of the central carbon metabolism (CCM) of a modified _E. coli_ cell, linked to the state variables of a FBR used for the tryptophan (TRP) production. By using several optimization algorithms, and an original application of the Pareto-optimal front technique, this paper compares various operating alternatives by using multiple control variables, aiming to maximize TRP production, with minimum substrate consumption. The used _E. coli_ strain was modified to drastically amplify the glucose (GLC) uptake into the cell. g pacs: +
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(2) The in-silico evaluation of optimal operating policies of a given FBR based on an available bioprocess kinetic model. This step can be applied in two ways: (2a) off-line (or 'run-to-run'), by using an (extended/deterministic) adequate kinetic model previously identified from the collected experimental data (this paper, and other reports [10; 11; 12; 13]); (2b) on-line, by using a simplified, often empirical mathematical model to obtain a state-parameter estimator based on the on-line recorded data (such as the classical Kalman filter) [14; 15; 16; 17; 18; 19]. To be consistent, the in-silico derived optimal operation mode of a bioreactor should be based on a simulation model that must include most of the process key variables. The use of a deterministic dynamic model, based on the process mechanism, and on macroscopic (measurable) state-variables, as the case here, is preferred due to the physical significance of the terms/parameters, which make possible their validation vs. experimental and literature data, even if repeated model updating is often necessary due to the high variability of the bioprocess. Typical optimization objective functions were reviewed, as stated by [PERSON] [20]. In spite of its low productivity, BRs are commonly used for slow bioprocesses, because they are highly flexible and easy to operate in various alternative forms [5], detailed as follows: (i) simple BR, when substrate(s), a biocatalyst, and additives are initially loaded in the recommended amounts (concentrations) (Figure 1) [5; 21; 22]. The BR (single- or multi-objective) optimization determines the batch time and the substrate/biocatalyst initial load [14; 23]; (ii) a BR-to-BR optimization, which includes a model-updating intermediate step ('tendency modelling' [24]) to evaluate the optimal load of the next BR [4; 9; 12; 14; 25; 26; 27; 28]; (iii) a serial sequence of BRs (SeqBR) [28]. The SeqBR includes a series of BRs of quasi-equal volumes. The content of every BR is transferred to the next, by adjusting the reactants and/or biocatalyst concentrations of the latter. Each BR initial load adjustment is determined in silico to ensure the optimal operation of the whole ensemble [4; 25]. (iv) BRP, that is, a BR with reactants and/or a biocatalyst added during the batch process in a pulse-like addition of equal/uneven solution volumes, with the frequency determined correspondingly [5]; (v) a semi-batch (that is, fed-batch) reactor (SBR or FBR) (Figure 1), with an optimally varied feeding strategy of the biocatalyst/substrate(s) [5; 10; 15; 27; 29; 30; 31]. Usually, FBRs report better performances compared to other batch operating alternatives. However, they are more difficult to operate.