This detailed volume is devoted to the recent development of quantitative experiments and computational methods driving new transforming growth factor beta (TGF-) and other cell signaling knowledge. Many chapters cover quantitative assays for TGF- signaling studies, with others exploring the increasing role of both modeling and computational methods. Written for the highly successful Methods in Molecular Biology series, 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 up-to-date, TGF-Beta Signaling: Methods and Protocols serves as a vital resource for researchers seeking to move the TGF- field into the quantitative realm.

Includes cutting-edge techniques Provides step-by-step detail essential for reproducible results Contains key implementation advice from the experts

Inhalt

Absolute Quantification of TGF- Signaling Proteins Using Quantitative Western Blot.- Fast Quantitation of TGF- Signaling Using Adenoviral Reporter.- Complex Formation among TGF-b Receptors in Live Cell Membranes Measured by Patch-FRAP.- Branched Proximity Hybridization Assay for the Quantification of Nanoscale Protein-Protein Proximity.- Visualizing Dynamic Changes during TGF--Induced Epithelial to Mesenchymal Transition.- Establishment of Embryonic Zebrafish Xenograft Assays to Investigate TGF- Family Signaling in Human Breast Cancer Progression.- Generating Somatic Knockout Cell Lines with CRISPR-Cas9 Technology to Investigate SMAD Signaling.- CRISPR-Based Screening in Three-Dimensional Organoid Cultures to Identify TGF- Pathway Regulators.- Optogenetic Control of TGF- Signaling.- Using Microfluidics and Live Cell Reporters to Dissect the Dynamics of TGF- Signaling in Mouse Embryonic Stem Cells.- Energy Landscape Analysis of the Epithelial-Mesenchymal Transition Network.- Discrete Logic Modeling of Cell Signaling Pathways.- Mining of Single-Cell Signaling Time-Series for Dynamic Phenotypes with Clustering.- Automated Classification of Cellular Phenotypes Using Machine Learning in Cellprofiler and CellProfiler Analyst.- Live Cell Imaging of Spatiotemporal Ca 2+ Fluctuation Responses to Anticancer Drugs.