Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data.
The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.
Dipartimento di Ingegneria e Scienza dell'Informazione, University of Trento, Italy.
Nicola
N
Segata
Enrico
E
Blanzieri
Corrado
C
Priami
D019295:Computational Biology; D003198:Computer Simulation; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D046228:Microarray Analysis; D008954:Models, Biological; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010710:Phosphates / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000235:genetics; D049490:Systems Biology / Q000379:methods*
D010710:Phosphates