Abstract
This contribution moves in the direction of answering some general questions about the most effective and useful ways of modelling bioprocesses. We investigate the characteristics of models that are good at extrapolating. We trained three fully predictive models with different representational structures (differential equations, differential equations with inheritance of rates and a network of reactions) on Saccharopolyspora erythraea shake flask fermentation data using genetic programming. The models were then tested on unseen data outside the range of the training data and the resulting performances were compared. It was found that constrained models with mathematical forms analogous to internal mass balancing and stoichiometric relations were superior to flexible unconstrained models, even though no a priori knowledge of this fermentation was used.
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Abbreviations
- N vars :
-
Number of variables
- N batches :
-
Number of batches
- N nodes :
-
Number of nodes making up an individual
- t F :
-
Time of last sample
- C i , C j , C k :
-
Hidden variables used internally by models
- CGluc, CNitrate, CBiomass, CRed:
-
Predicted values of measured variables, glucose, nitrate, biomass, red pigment, respectively (g/l)
- MGluc, MNitrate, MBiomass, MRed:
-
Measured values of glucose, nitrate, biomass, red pigment, respectively (g/l)
- r batch :
-
Pearson correlation coefficient between measured and predicted values at a given time point varying with respect to initial conditions
- r time :
-
Pearson correlation coefficient between measured and predicted values with respect to time
- R 2 :
-
Root mean squared error
- a, b, c, d:
-
Floating point weights
- ɛ s :
-
Scaled error of model on batch
- ɛ av :
-
Error between the average profiles of training data and the actual value on that batch
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Acknowledgements
Adaptive Biosystems Ltd. and the EPSRC for sponsoring this work. Support for the Innovative Manufacturing Research Centre for Bioprocessing housed in the ACBE by the Engineering and Physical Sciences Research Council is also gratefully acknowledged.
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Hodgson, B.J., Taylor, C.N., Ushio, M. et al. Intelligent modelling of bioprocesses: a comparison of structured and unstructured approaches. Bioprocess Biosyst Eng 26, 353–359 (2004). https://doi.org/10.1007/s00449-004-0382-0
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DOI: https://doi.org/10.1007/s00449-004-0382-0