19. Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation

  1. Michael P. H. Stumpf4,
  2. David J. Balding5 and
  3. Mark Girolami6
  1. Neil Lawrence1,
  2. Magnus Rattray1,
  3. Antti Honkela2 and
  4. Michalis Titsias3

Published Online: 12 SEP 2011

DOI: 10.1002/9781119970606.ch19

Handbook of Statistical Systems Biology

Handbook of Statistical Systems Biology

How to Cite

Lawrence, N., Rattray, M., Honkela, A. and Titsias, M. (2011) Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation, in Handbook of Statistical Systems Biology (eds M. P. H. Stumpf, D. J. Balding and M. Girolami), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781119970606.ch19

Editor Information

  1. 4

    Division of Molecular Biosciences, Imperial College London, UK

  2. 5

    Institute of Genetics, University College London, UK

  3. 6

    Department of Statistical Science, University College London, UK

Author Information

  1. 1

    Department of Computer Science and Sheffield Institute for Translational Neuroscience, University of Sheffield, UK

  2. 2

    Helsinki Institute for Information Technology, University of Helsinki, Finland

  3. 3

    School of Computer Science, University of Manchester, UK

Publication History

  1. Published Online: 12 SEP 2011
  2. Published Print: 21 OCT 2011

ISBN Information

Print ISBN: 9780470710869

Online ISBN: 9781119970606

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Keywords:

  • Gaussian process inference;
  • Systems Biology models;
  • Gaussian processes in biological modeling;
  • generalized linear model;
  • transcription factor concentration;
  • model based target ranking;
  • model fit to data;
  • correlation-based approach for Twist;
  • biological time series

Summary

This chapter contains sections titled:

  • Introduction

  • Generalized linear model

  • Model based target ranking

  • Multiple tanscription factors

  • Conclusion

  • References