Experimentally integrated dynamic modelling for intuitive optimisation of cell based processes and manufacture
journal contributionposted on 13.03.2018, 09:12 by Adrian J. Stacey, Elizabeth Cheeseman, Katie Glen, Rebecca L.L. Moore, Rob Thomas
Dynamic mechanistic modelling of cell culture is a key tool in bioprocess development to support optimisation and risk assessment. However, the approach is underutilised in the bioprocess industry due to challenges including lack of accessible tools to support a structured approach, the difficulty of realising computationally tractable (low parameter) yet realistic models, and the specialised skill sets required. We have proposed that these issues could be partly addressed by developing a parsimonious framework comprising a set of model building blocks, based on the ordinary differential equation modelling paradigm, representing common cell culture dynamics and modulation thereof. The framework is designed to avoid obvious pathological behaviours. Further, specific model instances within the framework can be simply visualised as a directed graph with vertices representing system species, dynamics and modulations, and arcs representing the interactions between them. The directed graph can be extended to describe the timing and nature of experimental interventions. A visual and intuitive route to describing models with an associated mathematical framework enables realisation in a software interface and integration with standard mathematical tools such as those for sensitivity analysis and parameter estimation. Such a framework is sufficient to represent some of the simple mechanisms underpinning bioprocesses that nonetheless lead to highly non-linear and counterintuitive outcomes. It also has a relatively low learning burden for users without formal mathematical training. The concept could be extended to include, for example, partial differential equation-based approaches to stochastic or spatially complex systems built up from a small number of parametrically parsimonious and well-behaved building blocks.
This work was supported by an Engineering and Physical Sciences Research Council Fellowship grant (EP/K00705X/1)
- Mechanical, Electrical and Manufacturing Engineering