(7b) Integration of Living Analytics into Biomanufacturing Processes | AIChE

(7b) Integration of Living Analytics into Biomanufacturing Processes


Polizzi, K. - Presenter, Imperial College London
Moya Ramirez, I., Imperial College London
Kotidis, P., Imperial College London
Marbiah, M., Imperial College London
Kontoravdi, C., Imperial College London
Kim, J., Imperial College London
Like any other industrial process, biomanufacturing strongly relies on analytical technologies to monitor key parameters and ensure optimal operational conditions to maintain the desired yield, product quality or safety conditions. Currently in bioprocesses this is done by means of physicochemical detection techniques based on chromatography or mass spectrometry or through the use of electrochemical biosensors, many of which are off-line and require destructive sampling.

However, in the past few years, there is a growing interest in harnessing the ability of living systems to sense their environment and respond to changes to develop genetically-encoded biosensors. These leverage the naturally evolved ability of cells to detect a specific target molecule and activate genetic programs in response. Genetically-encoded biosensors often have better sensitivity and specificity than the traditional physicochemical methods and our work, among others, has demonstrated that they can be used for quantitative measurements (Goers et al, 2017, Biotechnology and Bioengineering, 114: 1290-1300). Using synthetic biology methods, it is now possible to design genetically-encoded biosensors for a wide array of metabolites that are important in biomanufacturing.

One of the remaining challenges for industrial implementation of genetically-encoded biosensors is whether the sensing elements should be placed inside the producing cells (which would divert resources from bioproduction towards sensing) or whether dedicated biosensor cell populations could be created and cultured alongside the producer cells. In the latter case, the growth and division of the biosensor cells must be limited or they will overgrow the producer cells, leading to an overall loss of productivity from the culture. To tackle this challenge, we have been exploring strategies involving encapsulation of biosensor cells in materials that allow free diffusion of metabolites to the biosensor cells, but limit their growth within the culture.

In this talk, we will introduce LAMPS (Living Analytics in a Multilayer Polymer Shell) and demonstrate their application to the co-culture of a bacterial whole-cell biosensor for L-lactate with suspension-adapted mammalian cell lines producing a monoclonal antibody. LAMPS are composed of an inner layer of alginate containing the biosensor population, surrounded by one or more additional polymeric layers that prevent bacterial escape. Our results show that the biosensor population within LAMPS is able to sense L-lactate production by the mammalian producer cell lines and activate expression of a reporter gene in response. The response of the biosensor cells is proportional to the L-lactate acid concentration and the bacterial whole-cell biosensor does not escape the LAMPS to contaminate the cell culture. Moreover, LAMPS have no effect on the viability of the mammalian cell line and antibody productivity levels were not significantly different between producer cell lines grown in the presence and absence of LAMPS. Finally, we demonstrate that LAMPS can be produced, frozen, and stored at -20 degrees C for several weeks without loss of performance, thereby paving the way for application of this new analytical technology in bioprocesses. Overall, our results show that materials-based solutions can facilitate the implementation of genetically-encoded biosensors in biomanufacturing, opening up the possibility of exploiting natural sensing mechanisms to develop better process analytics.