In the first part of this work, the general Chemical and Biochemical Engineering (CBE) concepts and rules are briefly reviewed, together with the rules of the control theory of Nonlinear Systems (NSCT), all in the context of (i) deriving deterministic Modular Structured Kinetic Models (MSDKM) to describe the dynamics of metabolic processes in living cells, and (ii) of Hybrid Structured Modular Dynamic Models (HSMDM) (with continuous variables, linking the cell-nano-scale MSDKM state variables to the macro-scale state variables of the bioreactor dynamic model). Thus, in the HSMDM model, both prediction quality and its validity range are improved. By contrast, the current (classical/default) approach in bioengineering practice for solving design, optimization, and control problems based on the math models of industrial biological reactors is to use unstructured Monod (for cell culture reactor) or simple Michaelis-Menten (if only enzymatic reactions are retained) global kinetic models by ignoring detailed representations of metabolic cellular processes.
By contrast, as reviewed, and exemplified in the second part of this work, an accurate and realistic math modelling of the dynamic individual GERMs (gene expression regulatory module), or genetic regulatory circuits (GRC), and cell-scale CCM (central carbon metabolism) key-modules can be done by only using the novel holistic ’Whole-Cell Of Variable-Volume’ (WCVV) modelling framework, under isotonic/homeostatic conditions/constraints introduced and promoted by the author. An example was given in the same Part 2 for the case study of a dynamic model for the oscillating glycolysis coupled with the Tryptophan (TRP) oscillating synthesis in the E. coli cells.
As exemplified in the present paper, the use of an HSMDM (WCVV) model can successfully simulate the dynamic of cell individual GERMs, and of GRC-s (i.e. operon expression here), simultaneously with the dynamics of the bioreactor. Among multiple advantages - state-variables prediction, of a higher accuracy, and detailing degree, over a wider time-range for the bioreactor dynamic parameters (at both macro- and nano-scale level);
As exemplified here, the immediate applications of such an HSMDM model are related to solving difficult bioengineering problems, such as (i) in-silico off-line optimization of the operating policy of the bioreactor, and (ii) in-silico design/checking some GMOs of industrial use and able to improve the performances of the target bioprocess.
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Published on: Jan 19, 2024 Pages: 1-34
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DOI: 10.17352/asb.000021
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