Guidance for data collection and computational modelling of regulatory networks.
Date
2009
Authors
Palmer, A.
Shearwin, K.
Editors
McDermott, J.
Samudrala, R.
Bumgarner, R.
Montgomery, K.
Ireton, R.
Samudrala, R.
Bumgarner, R.
Montgomery, K.
Ireton, R.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Book chapter
Citation
Computational Systems Biology, 2009 / McDermott, J., Samudrala, R., Bumgarner, R., Montgomery, K., Ireton, R. (ed./s), vol.541, pp.337-354
Statement of Responsibility
Conference Name
Abstract
Many model regulatory networks are approaching the depth of characterisation of bacteriophage lambda, wherein the vast majority of individual components and interactions are identified, and research can focus on understanding whole network function and the role of interactions within that broader context. In recent years, the study of the system-wide behaviour of phage lambda's genetic regulatory network has been greatly assisted by the combination of quantitative measurements with theoretical and computational analyses. Such research has demonstrated the value of a number of general principles and guidelines for making use of the interplay between experiments and modelling. In this chapter we discuss these guidelines and provide illustration through reference to case studies from phage lambda biology.In our experience, computational modelling is best facilitated with a large and diverse set of quantitative, in vivo data, preferably obtained from standardised measurements and expressed as absolute units rather than relative units. Isolation of subsets of regulatory networks may render a system amenable to 'bottom-up' modelling, providing a valuable tool to the experimental molecular biologist. Decoupling key components and rendering their concentration or activity an independent experimental variable provide excellent information for model building, though conclusions drawn from isolated and/or decoupled systems should be checked against studies in the full physiological context; discrepancies are informative. The construction of a model makes possible in silico experiments, which are valuable tools for both the data analysis and the design of wet experiments.