Computational Modeling of Signaling Networks

Chapter 1 Design Principles Underlying Robust Adaptation of Complex Biochemical Networks

Chapter 2 Multi-Dimensional Analysis of Biochemical Network Dynamics Using pyDYVIPAC

Chapter 3 A Practical Guide for the Efficient Formulation and Calibration of Large, Energy- and Rule-Based Models of Cellular Signal Transduction

Chapter 4 Systems Biology: Identifiability Analysis and Parameter Identification via Systems-Biology-Informed Neural Networks

Chapter 5 A Practical Guide to Reproducible Modeling for Biochemical Networks

Chapter 6 Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways

Chapter 7 Efficient Quantification of Extrinsic Fluctuations via Stochastic Simulations

Chapter 8 Meta-Dynamic Network Modelling for Biochemical Networks

Chapter 9 Rapid Particle-Based Simulations of Cellular Signalling with the FLAME-Accelerated Signalling Tool (FaST) and GPUs

Chapter 10 Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Chapter 11 Quantitative Imaging Analysis of NF-κB for Mathematical Modeling Applications

Chapter 12 Resolving Crosstalk Between Signaling Pathways Using Mathematical Modeling and Time-Resolved Single Cell Data

Chapter 13 Live-Cell Sender-Receiver Co-cultures for Quantitative Measurement of Paracrine Signaling Dynamics, Gene Expression, and Drug Response.

Chapter 14 Application of Optogenetics to Probe the Signaling Dynamics of Cell Fate Decision-Making

Chapter 15 Computational Random Mutagenesis to Investigate RAS Mutant Signaling

Chapter 16 Mathematically Modeling the Effect of Endocrine and Cdk4/6 Inhibitor Therapies on Breast Cancer Cells

Chapter 17 SynDISCO: A Mechanistic Modeling-Based Framework for Predictive Prioritization of Synergistic Drug Combinations Targeting Cell Signalling Networks