Cahan Computational Stem Cell Biology

Methods and Protocols
Erscheinungsjahr 2019
ISBN: 978-1-4939-9224-9
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 1975, 456 Seiten, eBook

Reihe: Methods in Molecular Biology

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Cahan, Patrick

Inhaltsverzeichnis

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.