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Experimentally integrated dynamic modelling for intuitive optimisation of cell based processes and manufacture

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journal contribution
posted on 13.03.2018 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.

Funding

This work was supported by an Engineering and Physical Sciences Research Council Fellowship grant (EP/K00705X/1)

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Biochemical Engineering Journal

Volume

132

Pages

130 - 138

Citation

STACEY, A.J. ... et al, 2018. Experimentally integrated dynamic modelling for intuitive optimisation of cell based processes and manufacture. Biochemical Engineering Journal, 132, pp.130-138

Publisher

© Elsevier

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

10/01/2018

Publication date

2018

Notes

This paper was published as Open Access by Elsevier and distributed under a Creative Commons Attribution (CC BY) 4.0 licence.

ISSN

1369-703X

eISSN

1873-295X

Language

en

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