Development of a combined mathematical and experimental framework for the control and optimisation of mammalian cell culture systems
Author(s)
Kyparissidis, Alexandros-Dimitrios
Type
Thesis
Abstract
Even relatively simple microorganisms, which have been extensively studied, are hosts to a complex
network of interconnected processes occurring on diverse time scales. The multilevel nature of the
regulatory network of cells and the interactions occurring at the intra cellular level further augment this
complexity (Yokobayashi et al., 2003). Attempts to wholly model the function of even a single cell are
currently non trivial, if not impossible, as the amount of delicate intracellular measurements required to
validate such a model is exhaustive both in terms of labour and cost. Uncertainties introduced both on
the parameter identifiability and on the mechanistic level further complicate this task.
The large number of biological data generated with the advancement of a variety of high-throughput
experimental technologies demand for the development of comprehensive mathematical model
building methods able to capture the complex phenomena occurring within a cell (Covert et al., 2001).
Borrowing the fundamental research principles from the Process Systems Engineering paradigm,
mathematical modelling of biological systems can provide a systematic means to quantitatively study
the characteristics of the multilevel interactions that occur in cell culture. In the present thesis, an
integrated modelling framework is established that can ensure the seamless interaction of experimental
biology with the development of quantitative mathematical descriptions of biological systems. The use
of model-based techniques can facilitate the reduction of unnecessary experimentation and reduce
labour and operating costs by identifying the most informative experiments and providing strategies to
optimise and automate the bio-process at hand.
Paving the way towards a ‘closed-loop’ approach for bio-process automation (Kiparissides et al.,
2011), the work herein presents a biological model development framework following a step by step
approach, highlighting challenges and “real life” problems associated with each stage of model
development. By organising available information in a systematic way, unnecessary experimentation is
avoided and models with a priori objectives can be established to guide the in vivo process through the
in silico representation. The proposed methodology combines macroscopic and subcellular model
development, parameter estimation, global sensitivity analysis, model based design of experiments and
selection of optimal feeding policies via dynamic optimisation methods in a fromalised structure. The
combined mathematical and experimental framework for the control and optimisation of mammalian
cell culture systems, presented herein, is experimentally validated via the succesfull model based
optimisation of antibody secreting GS-NS0 cell cultures.
network of interconnected processes occurring on diverse time scales. The multilevel nature of the
regulatory network of cells and the interactions occurring at the intra cellular level further augment this
complexity (Yokobayashi et al., 2003). Attempts to wholly model the function of even a single cell are
currently non trivial, if not impossible, as the amount of delicate intracellular measurements required to
validate such a model is exhaustive both in terms of labour and cost. Uncertainties introduced both on
the parameter identifiability and on the mechanistic level further complicate this task.
The large number of biological data generated with the advancement of a variety of high-throughput
experimental technologies demand for the development of comprehensive mathematical model
building methods able to capture the complex phenomena occurring within a cell (Covert et al., 2001).
Borrowing the fundamental research principles from the Process Systems Engineering paradigm,
mathematical modelling of biological systems can provide a systematic means to quantitatively study
the characteristics of the multilevel interactions that occur in cell culture. In the present thesis, an
integrated modelling framework is established that can ensure the seamless interaction of experimental
biology with the development of quantitative mathematical descriptions of biological systems. The use
of model-based techniques can facilitate the reduction of unnecessary experimentation and reduce
labour and operating costs by identifying the most informative experiments and providing strategies to
optimise and automate the bio-process at hand.
Paving the way towards a ‘closed-loop’ approach for bio-process automation (Kiparissides et al.,
2011), the work herein presents a biological model development framework following a step by step
approach, highlighting challenges and “real life” problems associated with each stage of model
development. By organising available information in a systematic way, unnecessary experimentation is
avoided and models with a priori objectives can be established to guide the in vivo process through the
in silico representation. The proposed methodology combines macroscopic and subcellular model
development, parameter estimation, global sensitivity analysis, model based design of experiments and
selection of optimal feeding policies via dynamic optimisation methods in a fromalised structure. The
combined mathematical and experimental framework for the control and optimisation of mammalian
cell culture systems, presented herein, is experimentally validated via the succesfull model based
optimisation of antibody secreting GS-NS0 cell cultures.
Date Issued
2012
Date Awarded
2012-05
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
License URL
Advisor
Mantalaris, Sakis
Pistikopoulos, Stratos
Sponsor
European Union
Publisher Department
Chemical Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)