After completing a PhD thesis on developing a bone marrow biomimicry at the University of Rochester, Sakis Mantalaris, PhD, took the Governor’s lecturer position (assistant professor) in the department of chemical engineering at Imperial College London. There, he turned from wet-lab work to in silico modeling. In particular, he says, “I got interested in modeling bioprocess systems.”

Most important, he wants to apply these models. “We don’t do models for the sake of modeling,” as he emphasizes it. “We’ve always developed models that have experimental applicability.”

Now a professor of biomedical engineering at the Georgia Institute of Technology in Atlanta, Mantalaris recently described a model of cells that produce monoclonal antibodies.1

In talking about the challenges in modeling biological systems, Mantalaris says that the top one is the complexity. “If you start making a model very complex—too many parameters—you come across problems of how do you get the values of the parameters,” he explains. “Then, how do you validate such a model?”

But anyone modeling bioprocessing of any sort faces a balance issue. If the model, for example, captures lots of intracellular processes, the model cannot be validated, which leaves little reason to build the model in the first place. On the other hand, Mantalaris points out, “Models that are overly simplistic do not capture biology.”

To keep a model simple enough that it can be validated but complex enough to provide useful information, a scientist needs to know which parameters of a system matter the most. For that, Mantalaris uses global sensitivity analysis. “It allows you to determine the significant parameters whereby changing their values is so important that it will affect the output of the model.”

In describing the model of the cells that produce monoclonal antibodies, Mantalaris says that it “allows you to look at the most important cellular functions: growth, cell cycle and productivity.” He adds, “You can run different scenarios and look at what would be the optimal operating conditions.”

Mantalaris and his colleagues are setting up a company that will use this model to develop optimal culture media for specific cells. Eventually, the model will also be linked to those cells growing in a particular bioreactor. “Then, we can identify stresses within the bioreactors due to mixing, oxygenation, etcetera,” he says. “We’ll be able to design the whole process for different bioreactors on different scales—from plates and wells to a 10,000-liter fermenter.” That, he thinks, is only a year or so away.

 

Reference
1. Grilo, A.L., Mantalaris, A. A predictive mathematical model of cell cycle, metabolism, and apoptosis of monoclonal antibody‐producing GS–NS0 cells. Biotechnology Journal. 2019. Jul 22:e1800573. doi: 10.1002/biot.201800573. [Epub ahead of print]

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