Wiley Interdiscip Rev Syst Biol Med - Integrating omics into the cardiac differentiation of human pluripotent stem cells.

Tópicos

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Resumo

Time-dependent extracellular manipulations of human pluripotent stem cells can yield as much as 90% pure populations of cardiomyocytes. While the extracellular control of differentiation generally entails dynamic regulation of well-known pathways such as Wnt, BMP, and Nodal signaling, the underlying genetic networks are far more complex and are poorly understood. Notably, the identification of these networks holds promise for understanding heart disease and regeneration. The availability of genome-wide experimentation, such as RNA and DNA sequencing, as well as high throughput surveying with small molecule and small interfering RNA libraries, now enables us to map the genetic interactions underlying cardiac differentiation on a global scale. Initial studies demonstrate the complexity of the genetic regulation of cardiac differentiation, exposing unanticipated novel mechanisms. However, the large datasets generated tend to be overwhelming and systematic approaches are needed to process the vast amount of data to improve our mechanistic understanding of the complex biology. Systems biology methods spur high hopes for parsing vast amounts of data into genetic interaction models that can be verified experimentally and ultimately yield functional networks that expose the genetic connections underlying biological processes.

Resumo Limpo

timedepend extracellular manipul human pluripot stem cell can yield much pure popul cardiomyocyt extracellular control differenti general entail dynam regul wellknown pathway wnt bmp nodal signal under genet network far complex poor understood notabl identif network hold promis understand heart diseas regener avail genomewid experiment rna dna sequenc well high throughput survey small molecul small interf rna librari now enabl us map genet interact under cardiac differenti global scale initi studi demonstr complex genet regul cardiac differenti expos unanticip novel mechan howev larg dataset generat tend overwhelm systemat approach need process vast amount data improv mechanist understand complex biolog system biolog method spur high hope pars vast amount data genet interact model can verifi experiment ultim yield function network expos genet connect under biolog process

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