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Intelligent modelling of bioprocesses: a comparison of structured and unstructured approaches

Bioprocess and Biosystems Engineering volume 26, pages 353–359 (2004)Cite this article

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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Affiliations

  1. The Advanced Centre for Biochemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK

    Benjamin J. Hodgson, Misti Ushio & Frank Baganz

  2. Lilly Systems Biology Pte Ltd, Singapore

    Christopher N. Taylor

  3. Control Engineering Centre, Department of Electrical Engineering and Electronics, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK

    J. R. Leigh

  4. Electrical and Computing Engineering Department, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK

    Tatiana Kalganova

Authors
  1. Benjamin J. Hodgson
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  2. Christopher N. Taylor
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  3. Misti Ushio
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  4. J. R. Leigh
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  5. Tatiana Kalganova
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  6. Frank Baganz
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Corresponding author

Correspondence to Frank Baganz.

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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

Keywords

  • Genetic programming
  • Predictive
  • Modelling
  • Fermentation
  • Development