Computational Modeling of Signaling Networks

​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 TGFβ-SMAD Signaling Pathway.- Quantitative Imaging Analysis of NF-κB 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.