title: Computational and experimental single cell biology techniques for the definition of cell type heterogeneity, interplay and intracellular dynamics.
abstract: Novel technological developments enable single cell population profiling with respect to their spatial and molecular setup. These include single cell sequencing, flow cytometry and multiparametric imaging approaches and open unprecedented possibilities to learn about the heterogeneity, dynamics and interplay of the different cell types which constitute tissues and multicellular organisms. Statistical and dynamic systems theory approaches have been applied to quantitatively describe a variety of cellular processes, such as transcription and cell signaling. Machine learning approaches have been developed to define cell types, their mutual relationships, and differentiation hierarchies shaping heterogeneous cell populations, yielding insights into topics such as, for example, immune cell differentiation and tumor cell type composition. This combination of experimental and computational advances has opened perspectives towards learning predictive multi-scale models of heterogeneous cell populations.
authors.affiliation: Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland; Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland.
authors.forename: Laura
authors.initials: L
authors.lastname: de Vargas Roditi
authors.affiliation: Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland; Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland. Electronic address: claassen@imsb.biol.ethz.ch.
authors.forename: Manfred
authors.initials: M
authors.lastname: Claassen
mesh_terms: D003585:Cell Biology; D002468:Cell Physiological Phenomena; D003201:Computers; D003584:Cytological Techniques; D006801:Humans; D042541:Intracellular Space; D015398:Signal Transduction