Bridging biological scales by state-space analysis and modeling using molecular, tissue cytometric and physiological data.
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.
Kriete
Andres
A
School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, Pennsylvania 19104, USA. andres.kriete@drexel.edu
D019295:Computational Biology; D003627:Data Interpretation, Statistical; D005057:Eukaryotic Cells / Q000166:cytology / Q000378:metabolism / Q000502:physiology; D020869:Gene Expression Profiling; D006801:Humans; D019044:Image Cytometry; D008954:Models, Biological; D008967:Molecular Biology; D049490:Systems Biology / Q000379:methods*