**5. Problems and bottlenecks in** *E. coli* **media optimisation**

Medium optimisation involves many experiments irrespective of the media chosen, which accounts for labour cost and is an open-ended experiment. Many experiments are carried out at shake flask, even after generation of large amount of data using single experiments. The results obtained at piolet scale batch fermentation are not reproducible. During shake flask experiments, the precise control over pH, oxygen transfer and evaporation is not controlled. The experiments carried out at shake flask may or may replicate during fermentation. Also, soluble proteins expression may lead to inclusion bodies formation. Optimisation of media is time consuming due to the requirement of rigorous experimental planning. Moreover, the media utilised in the production of recombinant products faces challenges due to variability in different batches, media availability, cost of media, bulk storage, transport time. For Biotherapeutics, Enzymes and Probiotics, the cost of media needs to lower in Probiotics compared to Enzymes and Biotherapeutics, respectively. The choice of fermentation depends on the solubility of protein from batch to fed-batch. *E. coli* cells are dynamic, and every product requires different media compared to the earlier optimised process. Optimisation of media depends on considering dynamic internal control mechanisms. For the production of metabolites after engineering of bacterial strains, metabolic pathways needed to be optimised to regulate the desired product by choice of media. The influence of using different strains for the production is dependent on toxicity, complexity (Disulphide bonds in the sequence), AT-rich sequences. In our previous study, *E. coli* cell Shuffle 3030H for production of *Plasmodium falciparum* MSP-3 and the MSP-119 fusion protein was successfully optimised to generate protective antibodies [91].

Improvement of production of recombinant products is also guided by downstream processing of protein. Therefore, series of experiments designed for correct folding and confirmation are most important. Significant protein amounts can be achieved using pH, time for fermentation, oxygen transfer and temperature for fermentation. Also, inducer and harvest time are critical for increasing output. The critical factors for fermentation in batch and fed-batch are different. Therefore, the choice of media defined, semi-defined or complex media with vitamins, minerals and trace elements needed to be considered for evaluation in DOE experiments. To evaluate the considerable amount of output and variables combination and application of various algorithms is done to achieve desired output. In all the optimisation process and advanced algorithms such as Artificial neural networks and Genetic Algorithm are applied to achieve the desired output efficiently. The need for innovation as per sustainable development goals (SDGs) for United Nations 2030 plan is needed to increase the reach of technologies to low income countries. The application of DOE can improve the yield and cost leading to improved access to Biomolecules, Biopharmaceuticals, enzymes and metabolites.

#### **6. Future strategies**

The selection of host cells for industrial application has some technical difficulties despite the availability of many gene manipulations theoretically in various organisms. The availability of a genetic map, gene exchange system, useful vector and transformation procedures, and metabolic pathways leading from raw material to the desired product are essential criteria for selecting a suitable host strain. The most popular organisms used to date for the expression of the recombinant proteins are *E. coli*, *Bacillus subtilis, Bacillus stearothermophillus, Streptomyces spp, Corynebacterium, Saccharomyces cerevisiae, Pichia pastoris, Hansenula polymorpha* and various animal/plant cells. *E. coli* remains an important host system for the industrial protein production from cloned genes as one of the main applications of genetic engineering in biotechnology. Various efficient expression vector systems have been developed, and a variety of mutants are available as host strains for different purposes [92, 93]. Overexpression of a heterologous protein is possible in *E. coli,* making it suitable for industrial production*.* Fermentation DOE is an essential tool for basic research that greatly facilitates efficient purification and analysis of such proteins [94].

For the successful production of the recombinant protein-based vaccine, producing biologically active protein is an essential requirement that can be further scaled up. Production of a biologically active recombinant protein depends on the host cell's microenvironment for expression and compatibility of codon usage. *E. coli* has been a widely used expression host for the high-level production of heterologous protein. Differences in usage of codons in prokaryotes (*E. coli*) and eukaryotes Chinese hamster ovary cells (CHO) can substantially impact heterologous protein production. The compatibility of codon usage can significantly increase protein expression [95, 96]. Moreover, the presence of rare codons in cloned genes affects protein expression level and mRNA & plasmid stability. The excessive presence of rare codons may result in ribosome stalling, slow translation errors [96, 97]. In some cases, rare codons inhibit protein synthesis and cell growth [98]. Earlier studies of codon usage patterns in *E. coli* have established that a clear codon bias exists in the mRNA. The level of each cognate tRNA seems to be directly proportional to the codon frequency [99, 100]. The strategy widely used is to change rare codons in the target gene to the favoured codons of *E. coli* without affecting the encoded amino acid sequence [101, 102]. The second approach is to expand the intracellular tRNA pool by introducing a plasmid encoding additional copies of tRNAs for codons rarely used in *E. coli* [103]. The co-presence of the RIG plasmid encoding three tRNAsAG(A/G), ATA, GGA in the host cells significantly increases the expression level of Dihydropteroate synthase, Aldolase, Phosphatase, and Orotidine-5<sup>0</sup> -monophosphate decarboxylase of *P. falciparum* [104–106]. Codon optimisation for maximum expression of foreign proteins by changing host cell favourable codons is beneficial and crucial for large-scale proteins [107].

The recombinant plasmid carrying cloned gene would behave differently compared to the original vector plasmid. It can be easily understood, as it is preserved under a delicate quasi-equilibrium state in the host cell. There are several reasons for the instability of recombinants. The higher the plasmid gene expression, the more segregants (plasmid free cells) tend to appear. The recombinant plasmid is relatively unstable when the cloned gene products are inhibitory to the host cells. Phenotypic instability of plasmid is due to the disappearance of the entire plasmid or the deletion of a specific region [108]. Both plasmid copy number and plasmid loss rate are features affected by factors such as media composition growth rate and culture strategy [109] and other factors such as temperature, agitation rate, and pH [110].

Therefore, future strategies for optimisation of cultivations needed to be shift to conclusions evaluated during experimental phases before actual fermentation to identify role of batch, fed-batch or different media components. The utilisation of carbon, nitrogen and other minimally required nutrients during batch and fedbatch is critical for delivering output and achieve sustainable development goals (SDGs) for technological innovation. The method design and modelling approaches are future strategies for increasing output during a process development. Utilisation *Strategies for Enhancing Product Yield: Design of Experiments (DOE) for* Escherichia coli*… DOI: http://dx.doi.org/10.5772/intechopen.99288*

of one factor and carrying out experiments by statistical media optimisation can be improved by combining several algorithms such as Plakett Burman, Box–Behnken, Taguchi design, Central composite design, partial least squares modelling in determining optimal factors. Response surface methodology with Artificial neural network (ANNs) can be applied to difficult model kind of fermentation processes. A free artificial neural network is applicable for carrying out nonlinear regression models to optimise metabolic processes. These algorithms are combined and applied to increase productivity and optimise the product output by reducing the cost of fermentation and product development.
