Titlebook: Computational Stem Cell Biology; Methods and Protocol Patrick Cahan Book 2019 Springer Science+Business Media, LLC, part of Springer Nature

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Methods in Molecular Biologyhttp://image.papertrans.cn/c/image/233147.jpg

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Computational Stem Cell Biology978-1-4939-9224-9Series ISSN 1064-3745 Series E-ISSN 1940-6029

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https://doi.org/10.1007/978-981-10-5062-6nization. However, these models have been previously inaccessible to many systems biologists due to the difficulties with formulating and simulating multi-scale behavior. In this chapter, a review of the Compucell3D framework is presented along with a general workflow for transitioning from a well-m

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https://doi.org/10.1007/978-981-19-4847-3o coordinate and maintain the overall gene expression profile observed in a cell is a key question in cellular biology. However, the immense complexity arising due to the scale and the nature of gene-gene interactions often hinders obtaining a global understanding of gene regulation. In this regard,

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https://doi.org/10.1007/978-981-19-4847-3bryonic stem cells or induced pluripotent stem cells can be achieved by exposing them to a succession of signaling conditions meant to mimic developmental milieus. However, achieving a quantitative understanding of the relationship between proliferation, cell death, and commitment has been difficult

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https://doi.org/10.1007/978-981-19-4847-3ly multicellular data of cell clones can be obtained. In this situation, experimental data?alone is not sufficient to validate biological models because the hypotheses and the data?cannot be directly compared and thus standard statistical tests cannot be leveraged. On the other hand, mathematical mo

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Avinash S. Welankiwar,Sagar Kudkelwaractors control cell fate is fundamental to many biological experiments. However, due to transcriptional heterogeneity or microenvironmental fluctuations, cell fates appear to be random. Individual cells within well-defined subpopulations vary with respect to their proliferative potential, survival,