Building models is a key element of today’s science behind bioprocessing. The value of such models, however, depends on the information being produced. To improve models of bioprocessing, Mariana Monteiro, a doctoral student in Cleo Kontoravdi’s lab in the department of chemical engineering at Imperial in London, and her colleagues created multiscale models that incorporate the metabolism taking place in a reactor.
“We call it multiscale due to the differences in dimension and time included in the bioreactor model,” Monteiro says. “Dimension-wise, we describe the reactions occurring inside each cell and use these to track the profile of metabolites in the reactor, which contains millions of cells per milliliter of culture.” For the timing element, Monteiro notes that cells in an actual bioreactor “grow and duplicate within a day whereas the reactor model can make projections for longer time intervals.”
That output of a model can be used to control an ongoing bioprocess, but the benefit of that depends on the accuracy of the information. “A controller with access to a more mechanistic understanding of the bioprocess outputs better and more interpretable actions,” Monteiro says. “It also means that the model structure should generally hold true when we move across cell lines or even products.”
So, multiscale modeling of bioprocessing that includes the metabolism of cells in a bioreactor provides several benefits. One that Monteiro mentions is that it is less reliant on condition-specific parameters. “We have also discovered that a multiscale perspective allows controllers to yield resource-intelligent control decisions,” she says. “This is mostly due to the embedded knowledge of how cells handle different metabolites.”
