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Bridging biological scales by state-space analysis and modeling using molecular, tissue cytometric and physiological data
Journal article   Open access   Peer reviewed

Bridging biological scales by state-space analysis and modeling using molecular, tissue cytometric and physiological data

Andres Kriete
Cytometry. Part A, v 69(3)
Mar 2006
PMID: 16479594
url
https://doi.org/10.1002/cyto.a.20226View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze) Open

Abstract

Data Interpretation, Statistical Eukaryotic Cells - cytology Eukaryotic Cells - metabolism Eukaryotic Cells - physiology Gene Expression Profiling Humans Image Cytometry Models, Biological Systems Biology - methods Computational Biology Molecular Biology
Combining data streams across different levels of biological organization such as molecular, cellular, and physiological responses support to a system-wide view in biology. Recently, an unbiased analysis of tissues that provides data-rich descriptors of tissue architecture, cell types, and cell states has become available. As tissues are centrally located in the biological hierarchy, these advancements give rise to a new class of state variables that are critical to elucidate both underlying cellular, molecular and emergent physiological properties. Concepts to statistically identify, correlate, and model relationships across scales are introduced, which rely on a state-space matrix derived by multi-omics data aggregation.

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4 citations in Scopus

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Web of Science research areas
Biochemical Research Methods
Cell Biology
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