Nguyen Computational Modeling of Signaling Networks

1. Auflage 2023
ISBN: 978-1-0716-3008-2
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2634, 386 Seiten, eBook

Reihe: Methods in Molecular Biology

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Nguyen, Lan K.

Inhaltsverzeichnis

PART I. ADVANCES IN COMPUTATIONAL MODELLING OF SIGNALLING NETWORKS

1.     Design Principles Underlying Robust Adaptation of Complex Biochemical Networks

Robyn P. Araujo and Lance A. Liotta

2.     High-dimensional Dynamic Analysis of Biochemical Network Dynamics using pyDYVIPAC

Yunduo Lan and Lan K Nguyen

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

Fabian Fröhlich

4.     Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks

Mitchell Daneker, Zhen Zhang, George Em Karniadakis, and Lu Lu

5.     A Practical Guide to Reproducible Modeling for Biochemical Networks

Veronica L. Porubsky, Herbert M. Sauro

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

Krishna Kumar, Debaleena Bhowmik, Sapan Mandloi, Anupam Gautam, Abhishake Lahiri, Nupur Biswas, Sandip Paul and Saikat Chakrabarti

7.     Efficient Quantification of Extrinsic Fluctuations via Stochastic Simulations

Tagari Samanta and Sandip Kar

8.     Meta-Dynamic Network Modelling for Biochemical Networks

Anthony Hart and Lan K. Nguyen

9.     Rapid Particle-based Cell Signalling Simulations with the FLAME-accelerated Signalling Tool (FaST) and GPUs

Gavin Fullstone

PART II. ADVANCES IN INTEGRATIVE ANALYSIS OF SIGNALLING NETWORKS

10.  Modelling Cellular Signalling Variability Based on Single-cell Data: the TGFß-SMAD Signaling Pathway

Uddipan Sarma, Lorenz Ripka, Uchenna Alex Anyaegbunam, Stefan Legewie

11.  Quantitative Imaging Analysis of NF-?B for Mathematical Modelling Applications

Johannes Nicolaus Wibisana, Takehiko Inaba, Yasushi Sako, Mariko Okada

12.  Resolving Crosstalk between Signaling Pathways using Mathematical Modeling and Time-resolved Single-cell Data

Fabian Konrath, Alexander Loewer, Jana Wolf

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

Michael Pargett, Abhineet R. Ram, Vaibhav Murthy, Alexander E. Davies

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

Heath E. Johnson

PART III. APPLICATION OF INTEGRATIVE MODELLING AND ANALYSIS OF SIGNALLING NETWORKS IN DISEASES

15.  Computational Random Mutagenesis to Investigate RAS Mutant Signaling

Edward C. Stites

16.  Mathematically Modeling the Effect of Endocrine and CDK4/6 Inhibitor Therapies on Breast Cancer Cells

Wei He, Ayesha N. Shajahan-Haq and William T. Baumann

17.  SynDISCO: a mechanistic modelling-based framework for predictive prioritisation of synergistic drug combinations directed at cell signalling networks.

Sung-Young Shin and Lan K. Nguyen