Bioprocess and Biosystems Engineering

, Volume 26, Issue 6, pp 353–359

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

  • Benjamin J. Hodgson
  • Christopher N. Taylor
  • Misti Ushio
  • J. R. Leigh
  • Tatiana Kalganova
  • Frank Baganz
Original paper


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.


Genetic programming Predictive Modelling Fermentation Development 

List of symbols


Number of variables


Number of batches


Number of nodes making up an individual


Time of last sample

Ci, Cj, Ck

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)


Pearson correlation coefficient between measured and predicted values at a given time point varying with respect to initial conditions


Pearson correlation coefficient between measured and predicted values with respect to time


Root mean squared error

a, b, c, d

Floating point weights


Scaled error of model on batch


Error between the average profiles of training data and the actual value on that batch


  1. 1.
    Marenbach P, Betterhausen KD, Freyer S, Nieken U, Retttenmaier H (1997) Data-driven structured modelling of a biotechnological fed-batch fermentation by means of genetic programming. Proc Inst Mech Eng 211:325–332Google Scholar
  2. 2.
    Chen L, Bernard O, Bastin G, Angelov P (2000) Hybrid modelling of biotechnological processes using neural networks. Control Eng Pract 8:821–827CrossRefGoogle Scholar
  3. 3.
    Kennedy MJ, Spooner NR (1996) Using fuzzy logic to design fermentation media: a comparison to neural networks and factorial design. Biotechnol Tech 10(1):47–52Google Scholar
  4. 4.
    Asprey SP, Mantalaris A (2001) Global parametric identifiability of a dynamic unstructured model of hybridoma cell culture. In: Proceedings of the 8th international conference on computer applications in biotechnology (CAB8), Quebec, Canada, June 2001Google Scholar
  5. 5.
    Roubos JA, Krabben P, Luiten R, Babuska R, Heijnen JJ (2001) A semi-stoichiometric model for a Streptomyces fed-batch cultivation with multiple feeds. In: Proceedings of the 8th international conference on computer applications in biotechnology (CAB8), Quebec, Canada, June 2001, pp 299–304Google Scholar
  6. 6.
    Krabben P, Roubos JA, Bruins ME, Babuska R, Verbruggen HB, Heijnen JJ (2000) Metabolic flux analysis of the growth of S.clavuligerus in batch-cultivations with different N-sources. In: Proceedings of the workshop on metabolic engineering, vol 2, Elmau, GermanyGoogle Scholar
  7. 7.
    Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, Massachusetts, ISBN 0262111705Google Scholar
  8. 8.
    Nelder JA, Mead R (1965) Downhill simplex method. Comput J 7:308–313Google Scholar
  9. 9.
    Cao H, Kang L, Chen Y, Yu J (2000) Evolutionary modeling of systems of ordinary differential equations with genetic programming. J Genet Programming Evolvable Machines 1:309–337CrossRefGoogle Scholar
  10. 10.
    Sakamoto E, Iba H (2001) Inferring a system of differential equations for a gene regulatory network by using genetic programming. In: Proceedings of the congress on evolutionary computation (CEC2001), Seoul, Korea, May 2001, 1:720–726Google Scholar
  11. 11.
    Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in C, 2nd edn. Cambridge University Press, Cambridge, UK, ISBN 0-521-75033-4Google Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Benjamin J. Hodgson
    • 1
  • Christopher N. Taylor
    • 2
  • Misti Ushio
    • 1
  • J. R. Leigh
    • 3
  • Tatiana Kalganova
    • 4
  • Frank Baganz
    • 1
  1. 1.The Advanced Centre for Biochemical EngineeringUniversity College LondonLondonUK
  2. 2.Lilly Systems Biology Pte LtdSingapore
  3. 3.Control Engineering Centre, Department of Electrical Engineering and ElectronicsBrunel UniversityUxbridgeUK
  4. 4.Electrical and Computing Engineering DepartmentBrunel UniversityUxbridgeUK

Personalised recommendations