Estimation of biomass concentrations in fermentation processes for recombinant protein production

Abstract

Online biomass estimation for bioprocess supervision and control purposes is addressed. As the biomass concentration cannot be measured online during the production to sufficient accuracy, indirect measurement techniques are required. Here we compare several possibilities for the concrete case of recombinant protein production with genetically modified Escherichia coli bacteria and perform a ranking. At normal process operation, the best estimates can be obtained with artificial neural networks (ANNs). When they cannot be employed, statistical correlation techniques can be used such as multivariate regression techniques. Simple model-based techniques, e.g., those based on the Luedeking/Piret-type are not as accurate as the ANN approach; however, they are very robust. Techniques based on principal component analysis can be used to recognize abnormal cultivation behavior. For the cases investigated, a complete ranking list of the methods is given in terms of the root-mean-square error of the estimates. All techniques examined are in line with the recommendations expressed in the process analytical technology (PAT)-initiative of the FDA.

Real-time monitoring and control of microbial bioprocesses with focus on the specific growth rate: current state and perspectives

“…The ability to maintain cells in a particular metabolic or physiological state related to μ, promoting a particular metabolism for the production of secondary metabolites, such as prodigiosins (Chang et al 2011), is of particular relevance to biotechnology and pharmaceutical industries. Generally speaking, the specific productivity, recombinant protein production rate, and secretory expression in carbon-limited fed-batch cultures is often inversely correlated to the specific growth rate of the microorganism (Jenzsch et al 2006)-hence the importance of maintaining low specific growth rates, at least during protein expression phase, to maximize product yield (Panda et al 1999;Hellwig et al 2001;Priego-Jimenez et al 2005;Ren and Yuan 2005;Jenzsch et al 2006;Babaeipour et al 2007;Min et al 2010;Puertas et al 2010).…”

“…ANN have a tremendous application potential, especially in industrial processes where an enormous amount of data is available for model training and validation. ANN have been extensively applied to estimate biomass (Desai et al 2005;Won and Yoon-Keun 2006;da Costa Albuquerque et al 2008) and substrate concentrations (Hocalar et al 2011) or oxygen uptake rate (Jenzsch et al 2006); however, probably due to the lack of offline values in sufficient number for training and validation, examples of real-time estimations of μ by ANN or similar methods are rare.…”

Biomass measurement online: the performance of in situ measurements and software sensors

“…PCA models are also useful, particularly because they provide a simple variable for fault detection and quality control [72]. Neural networks have been used for biomass estimation on their own [2,14,27,30,47] or in combination with other modeling techniques [9].…”

Application of Artificial Neural Networks to Food and Fermentation Technology

“…The best prediction is obtained using a neural network with two hidden layers consisting of 15 and 16 neurons, respectively. Online biomass estimation for bioprocess supervision and control purposes is addressed by Jenzsch et al (2006), for the concrete case of recombinant protein production with genetically modified Escherichia coli bacteria and perform a ranking. As the biomass concentration cannot be measured online during the production to sufficient accuracy, indirect measurement techniques are required.…”