Computational Tools for Stem Cell Biology. Issue 12 (December 2016)

• Record Type: Journal Article
• Authors: Bian, Qin; Cahan, Patrick
• Is Part Of: Trends in biotechnology. Volume 34:Issue 12(2016)
• Page Start: 993
• Page End: 1009
• Publication Date: 2016-12
• Subjects: computational biology -- stem cell biology -- cell fate engineering -- single cell transcriptomics -- network biology; Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Genetic engineering -- Periodicals; Industrial microbiology -- Periodicals; 660.605
• DOI: 10.1016/j.tibtech.2016.05.010
• Languages: English
• ISSNs: 0167-7799

Abstract

Abstract : For over half a century, the field of developmental biology has leveraged computation to explore mechanisms of developmental processes. More recently, computational approaches have been critical in the translation of high throughput data into knowledge of both developmental and stem cell biology. In the past several years, a new subdiscipline of computational stem cell biology has emerged that synthesizes the modeling of systems-level aspects of stem cells with high-throughput molecular data. In this review, we provide an overview of this new field and pay particular attention to the impact that single cell transcriptomics is expected to have on our understanding of development and our ability to engineer cell fate. Trends: High-throughput data molecular profiling, mainly based on nucleic acid sequencing (e.g., RNA-Seq), but increasingly other modalities such as metabolomics and proteomics, has necessitated the development of sophisticated analysis algorithms. The combination of OMICs and targeted analytics has enabled seminal observations in stem cell biology. Computational stem cell biology has emerged as its own subdiscipline that is concerned with synthesizing the modeling of systems-level aspects of stem cells with large-scale molecular data. Single cell genomics is poised to transform stem cell biology by identifying new cell types; by clarifying the relationship between transcriptional noise, lineage priming, and lineage potential; and by enabling a higher resolution dissection of genetic circuits underlying commitment and differentiation.