Computational Stem Cell Biology [electronic resource] : Methods and Protocols / edited by Patrick Cahan.

New York, NY : Springer New York : Imprint: Humana, 2019.
1 online resource (XI, 456 pages) : 107 illustrations, 95 illustrations in color.
1st ed. 2019.
Springer Protocols (Springer-12345)
Methods in molecular biology 1064-3745 ; 1975
Methods in Molecular Biology, 1064-3745 ; 1975
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Stem cells.
Biology -- Data processing.
Local subjects:
Stem Cells. (search)
Computer Appl. in Life Sciences. (search)
Computational Biology/Bioinformatics. (search)
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This 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-scale measurements, reconstructing gene regulatory networks, and metabolomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Stem Cell Biology: Methods and Protocols will be an invaluable guide to researchers as they explore stem cells from the perspective of computational biology.
Agent-Based Modeling to Delineate Spatiotemporal Control Mechanisms of the Stem Cell Niche
Modeling Cellular Differentiation and Reprogramming with Gene Regulatory Networks
Cell Population Model to Track Stochastic Cellular Decision-Making during Differentiation
Automated Formal Reasoning to Uncover Molecular Programs of Self-Renewal
Mathematical Modeling of Clonal Stem Cell Dynamics
Computational Tools for Quantifying Concordance in Single Cell Fate
Quantitative Modeling of the Waddington Epigenetic Landscape
Modeling Gene Networks to Understand Multistability in Stem Cells
Trajectory Algorithms to Infer Stem Cell Fate Decisions
Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions
Reconstructing Gene Regulatory Networks that Control Hematopoietic Commitment
Investigating Cell Fate Decisions with ICGS Analysis of Single Cells
Lineage Inference and Stem Cell Identity Prediction using Single Cell RNA-Sequencing Data
Dynamic Network Modeling of Stem Cell Metabolism
Metabolomics in Stem Cell Biology Research
Molecular Interaction Networks to Select Factors for Cell Conversion
A Computational Approach for Cell Fate Reprogramming
Computational Analysis of Aneuploidy in Pluripotent Stem Cells
Cell Fate Engineering Tools for iPSC Disease Modeling.
Cahan, Patrick, editor., Editor,
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10.1007/978-1-4939-9224-9 doi
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