# Towards Computational Systems Biology with a Statistical Analysis Pipeline for Microarray Data

[edit]

**Department of Molecular Biology and Biotechnology, University of Sheffield, U.K.**on Oct 31, 2007 [pdf]

#### Abstract

Since the human genome project began mathematical models have become an integral part of biological data analysis. The growth in data availability has necessitated their use in summarization of the data (e.g. *statistical* approaches such as hierarchical clustering). Simultaneously, as more has become understood about the mechanisms underpinning particular pathways *mechanistic* models of interactions have become more widespread.\
\
The data-driven statistical approach and the mechanistic model approach each have their advantages. Data-driven models can be used in genome wide analyses to ’fish’ for genes that were not known to be relevant but provide a critical role in a pathway. Mechanistic models make real predictions about how systems will respond given particular interventions. The two approaches have interacted only loosely, often not through interaction between the ‘mathematicians’ but through indirect interaction via the biologists.\
\
In this talk we will follow describe a statistical analysis ‘pipeline’ for microarray data which handles the noise in the data. As we proceed down the pipeline we will come closer to mechanistic models of systems. We will finish with some general thoughts about the contribution that a combined statistical/mechanistic modelling approach can make.