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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Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisionselationships. We provide guidelines on how best to combine this analysis with other complementary methods designed to explore single cell data, and how to interpret the resulting gene regulatory network models to gain insight into the processes regulating cell differentiation.

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Lineage Inference and Stem Cell Identity Prediction Using Single-Cell RNA-Sequencing Datamajor interest. Therefore, here we present an in-house state-of-the-art scRNA-seq data analyses workflow for de novo lineage tree inference and stem cell identity prediction applicable to many biological processes under current investigation.

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1064-3745 ation advice from the expertsThis volume details methods and protocols to further the study of stem cells within the computational stem cell biology (CSCB) field. Chapters are divided into four sections covering the theory and practice of modeling of stem cell behavior, analyzing single cell genome-

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Sustainable Tertiary Education in Asia strategies for building deterministic and stochastic mathematical models of gene expression and demonstrate how analysis of these models can benefit our understanding of complex observed behaviors. Developing a mathematical understanding of biological processes is of utmost importance in understanding and controlling stem cell behavior.

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https://doi.org/10.1007/978-981-10-5062-6ion (PFLI) gene circuit in the intestinal crypts. Specifically, techniques for gene circuit-driven hypothesis formation, geometry construction, selection of simulation parameters, and simulation quantification are presented.

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