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 introduc...
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Autor*in:	Segata Nicola [verfasserIn] Blanzieri Enrico [verfasserIn] Priami Corrado [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Übergeordnetes Werk:	In: Journal of Integrative Bioinformatics - De Gruyter, 2018, 5(2008), 1, Seite 57-71
Übergeordnetes Werk:	volume:5 ; year:2008 ; number:1 ; pages:57-71

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1515/jib-2008-87

Katalog-ID:	DOAJ07075327X

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topic_title	TP248.13-248.65 Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data
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title	Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data
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title_full	Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data
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title_sort	towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data
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title_auth	Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data
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