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This volume focuses on the computational modeling of cell signaling networks and the application of these models and model-based analysis to systems and personalized medicine. Chapters guide readers through various modeling approaches for signaling networks, new methods and techniques that facilitate model development and analysis, and new applications of signaling network modeling towards systems and personalized treatment of cancer. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and methods, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols.
Authoritative and cutting-edge, Computational Modeling of Signaling Networks aims to benefit a wide spectrum of readers including researchers from the biological as well as computational systems biology communities.
List of contents
Design Principles Underlying Robust Adaptation of Complex Biochemical Networks.- High-dimensional Dynamic Analysis of Biochemical Network Dynamics using pyDYVIPAC.- A Practical Guide for the Efficient Formulation and Calibration of Large, Energy Rule-Based Models of Cellular Signal Transduction.- Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks.- A Practical Guide to Reproducible Modeling for Biochemical Networks.- Integrating Multi-omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory and Metabolic Pathways.- Efficient Quantification of Extrinsic Fluctuations via Stochastic Simulations.- Meta-Dynamic Network Modelling for Biochemical Networks.- Rapid Particle-based Cell Signalling Simulations with the FLAME-accelerated Signalling Tool (FaST) and GPUs.- Modelling Cellular Signalling Variability Based on Single-cell Data: the TGFbeta-SMAD Signaling Pathway.- Quantitative Imaging Analysis of NF- B for Mathematical Modelling Applications.- Resolving Crosstalk between Signaling Pathways using Mathematical Modeling and Time-resolved Single-cell Data.- Live-cell Sender-Receiver Co-cultures for Quantitative Measurement of Paracrine Signaling Dynamics, Gene Expression, and Drug Response.- Application of Optogenetics to Probe the Signaling Dynamics of Cell Fate Decision Making.- Computational Random Mutagenesis to Investigate RAS Mutant Signaling.- Mathematically Modeling the Effect of Endocrine and CDK4/6 Inhibitor Therapies on Breast Cancer Cells.- SynDISCO: a mechanistic modelling-based framework for predictive prioritisation of synergistic drug combinations directed at cell signalling networks.
