Application of simplex‐based experimental optimization to challenging bioprocess development problems: Case studies in downstream processing

The identification of feasible operating conditions during the early stages of bioprocess development is implemented frequently through High Throughput (HT) studies. These typically employ techniques based on regression analysis, such as Design of Experiments. In this work, an alternative approach, based on a previously developed variant of the Simplex algorithm, is compared to the conventional regression‐based method for three experimental systems involving polishing chromatography and protein refolding. This Simplex algorithm variant was found to be more effective in identifying superior operating conditions, and in fact it reached the global optimum in most cases involving multiple optima. By contrast, the regression‐based method often failed to reach the global optimum, and in many cases reached poor operating conditions. The Simplex‐based method is further shown to be robust in dealing with noisy experimental data, and requires fewer experiments than regression‐based methods to reach favorable operating conditions. The Simplex‐variant also lends itself to the use of HT analytical methods, when they are available, which can assist in avoiding analytical bottlenecks. It is suggested that this Simplex‐variant is ideally suited to rapid optimization in early‐phase process development. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:404–419, 2016     

Optimization of media for β-fructofuranosidase production by Aspergillus niger in submerged and solid state fermentation

The kinetics of β-fructofuranosidase (Ffase) production by Aspergillus niger in submerged (SmF) and solid-state fermentation (SSF) systems was investigated. The maximum productivity of Ffase (81.8 U/l per h) was obtained in SSF for 72 h while it was 18.3 U/l per h in SmF for 120 h. The productivity of extra cellular Ffase produced in SSF was 5-fold higher than in SmF. Optimization of fermentation medium for Ffase production was carried out using De Meo's fractional factorial design with seven components such as (NH₄)₂SO₄, KH₂PO₄, FeSO₄, MgSO₄ · 7H₂O, sucrose, urea and yeast extract. The media designed for SmF after two steps of optimization supported the growth of A. niger and higher productivity of Ffase (58.3 U/l per h) than with the medium before optimization. The optimized medium of SmF when used in SSF, did not improve the Ffase productivity and therefore medium for SSF was optimized independent of SmF. After two optimization steps, the media was defined for SSF which supported the growth and high level of Ffase productivity (149.1 U/l per h) in SSF compared to the medium before optimization (81.8 U/l per h) and optimized medium for SmF (58.3 U/l per h). Our results suggested that the optimized media for SmF and SSF for the production of Ffase have to be different.     

Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets

Microbial production strains are currently improved using a combination of random and targeted approaches. In the case of a targeted approach, potential bottlenecks, feed-back inhibition, and side-routes are removed, and other processes of interest are targeted by overexpressing or knocking-out the gene(s) of interest. To date, the selection of these targets has been based at its best on expert knowledge, but to a large extent also on educated guesses and gut feeling. Therefore, time and thus money is wasted on targets that later prove to be irrelevant or only result in a very minor improvement. Moreover, in current approaches, biological processes that are not known to be involved in the formation of a specific product are overlooked and it is impossible to rank the relative importance of the different targets postulated. Metabolomics, a technology that involves the non-targeted, holistic analysis of the changes in the complete set of metabolites in the cell in response to environmental or cellular changes, in combination with multivariate data analysis (MVDA) tools like principal component discriminant analysis and partial least squares, allow the replacement of current empirical approaches by a scientific approach towards the selection and ranking of targets. In this review, we describe the technological challenges in setting up the novel metabolomics technology and the principle of MVDA algorithms in analyzing biomolecular data sets. In addition to strain improvement, the combined metabolomics and MVDA approach can also be applied to growth medium optimization, predicting the effect of quality differences of different batches of complex media on productivity, the identification of bioactives in complex mixtures, the characterization of mutant strains, the exploration of the production potential of strains, the assignment of functions to orphan genes, the identification of metabolite-dependent regulatory interactions, and many more microbiological issues.