Integrative Information Management for Systems Biology

  • Neil Swainston
  • Daniel Jameson
  • Peter Li
  • Irena Spasic
  • Pedro Mendes
  • Norman W. Paton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6254)


Systems biology develops mathematical models of biological systems that seek to explain, or better still predict, how the system behaves. In bottom-up systems biology, systematic quantitative experimentation is carried out to obtain the data required to parameterize models, which can then be analyzed and simulated. This paper describes an approach to integrated information management that supports bottom-up systems biology, with a view to automating, or at least minimizing the manual effort required during, creation of quantitative models from qualitative models and experimental data. Automating the process makes model construction more systematic, supports good practice at all stages in the pipeline, and allows timely integration of high throughput experimental results into models.


computationalsystems biology workflow 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Neil Swainston
    • 1
  • Daniel Jameson
    • 1
  • Peter Li
    • 1
  • Irena Spasic
    • 1
  • Pedro Mendes
    • 1
  • Norman W. Paton
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK

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