On-line prediction of the feeding phase in high-cell density cultivation of rE. coli using constructive neural networks

Comput Methods Programs Biomed. 2013 Jul;111(1):228-48. doi: 10.1016/j.cmpb.2013.03.005. Epub 2013 Apr 6.

Abstract

Streptococcus pneumoniae (pneumococcus) is a bacterium responsible for a wide spectrum of illnesses. The surface of the bacterium consists of three distinctive membranes: plasmatic, cellular and the polysaccharide (PS) capsule. PS capsules may mediate several biological processes, particularly invasive infections of human beings. Prevention against pneumococcal related illnesses can be provided by vaccines. There is a sound investment worldwide in the investigation of a proteic antigen as a possible alternative to pneumococcal vaccines based exclusively on PS. A few proteins which are part of the membrane of the pneumococcus seem to have antigen potential to be part of a vaccine, particularly the PspA. A vital aspect in the production of the intended conjugate pneumococcal vaccine is the efficient production (in industrial scale) of both, the chosen PS serotypes as well as the PspA protein. Growing recombinant Escherichia coli (rE. coli) in high-cell density cultures (HCDC) under a fed-batch regime requires a refined continuous control over various process variables where the on-line prediction of the feeding phase is of particular relevance and one of the focuses of this paper. The viability of an on-line monitoring software system, based on constructive neural networks (CoNN), for automatically detecting the time to start the fed-phase of a HCDC of rE. coli that contains a plasmid used for PspA expression is investigated. The paper describes the data and methodology used for training five different types of CoNNs, four of them suitable for classification tasks and one suitable for regression tasks, aiming at comparatively investigate both approaches. Results of software simulations implementing five CoNN algorithms as well as conventional neural networks (FFNN), decision trees (DT) and support vector machines (SVM) are also presented and discussed. A modified CasCor algorithm, implementing a data softening process, has shown to be an efficient candidate to be part of an on-line HCDC monitoring system for detecting the feeding phase of the HCDC process.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Bacterial Load
  • Bacterial Proteins / genetics
  • Bacterial Proteins / immunology
  • Bacteriological Techniques
  • Bioreactors / microbiology*
  • Bioreactors / statistics & numerical data*
  • Computer Simulation
  • Epoetin Alfa
  • Erythropoietin / genetics
  • Erythropoietin / immunology
  • Escherichia coli / genetics*
  • Escherichia coli / growth & development
  • Escherichia coli / immunology*
  • Humans
  • Neural Networks, Computer*
  • Pneumococcal Vaccines / genetics*
  • Pneumococcal Vaccines / immunology*
  • Recombinant Proteins / genetics
  • Recombinant Proteins / immunology
  • Software
  • Streptococcus pneumoniae / genetics
  • Streptococcus pneumoniae / immunology
  • Vaccines, Conjugate / genetics
  • Vaccines, Conjugate / immunology

Substances

  • Bacterial Proteins
  • Pneumococcal Vaccines
  • Recombinant Proteins
  • Vaccines, Conjugate
  • pneumococcal surface protein A
  • Erythropoietin
  • Epoetin Alfa