Identification of key bioprocess variables using global sensitivity analysis

The selection of appropriate operating conditions for bioprocessing is complex due to the large number of interacting stages and variables. Models for each processing stage require significant numbers of variables and thus the whole process model will consist of a large number of variables overall. Interactions typically exist within a bioprocess meaning that the operation of one unit or the value of a variable may adversely effect the operation of subsequent units. It is therefore necessary to consider the process as a whole – selecting the optimal conditions for individual units will not yield optimal performance for the process. In addition to these complexities, bioprocesses also operate under tight regulation and it is necessary to demonstrate that performance is satisfactory over the likely operating range. Therefore, tools to analyse the sensitivities of the variables and to identify the key variables will assist bioprocess design and be of significant utility. Conventional approaches for the analysis of variable sensitivities are inadequate. Since they only consider one variable at a time and are unable to consider interactions between variables. We propose the use of global sensitivity analysis to determine the level of importance of each variable and their interactions. Global sensitivity analyses enable the effects of variable and parameter changes to be determined over the range of likely operation, but more importantly it determines the impact of all variables simultaneously rather than individually, which is necessary where interactions between variables are expected. Quantitative sensitivity indices for each variable and their interactions can be quantified which are used to determine the relative importance of each variable. Once key variables have been determined, the designer may focus on the most significant variable subset and investigate the effects of any process interactions. In this paper a case study is presented which illustrates the utility of the application of global sensitivity analysis to bioprocess operation. The case study investigates a two-stage sequence of a fermentation and subsequent centrifugal harvest and studies how the variable sensitivities change as the fermentation yield increases. The impact of such changes on the operation of the process is then considered. It is found as the cell density increases the importance of the fermentation stage increases. Thus at higher cell densities control of the fermentation will have a larger impact on the overall performance than the subsequent harvest centrifuge.