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Updated: Jun 9, 2026
11:13
Identification of Kinase-substrate Pairs Using High Throughput Screening
Published on: August 29, 2015
Computational modelling of kinase signalling cascades
David Gilbert1, Monika Heiner, Rainer Breitling
1School of Information Science, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UK. david.gilbert@brunel.ac.uk
Methods in Molecular Biology (Clifton, N.J.)
|September 3, 2010
Summary
This chapter details methods for building dynamic computational models of kinase signaling pathways using ordinary differential equations. These models are part of a systematic approach for analyzing biological systems.
Area of Science:
- Systems Biology
- Computational Biology
- Biochemistry
Background:
- Kinase signaling cascades are crucial for cellular processes.
- Dynamic computational models are essential for understanding complex biological systems.
- Existing modeling approaches may lack a unified framework.
Purpose of the Study:
- To describe general methods for creating dynamic computational models of kinase signaling cascades.
- To introduce tools that support the development of these models.
- To integrate ordinary differential equation models within a broader modeling framework.
Main Methods:
- Focus on ordinary differential equation (ODE) models for kinase signaling.
- Present a general framework encompassing qualitative, quantitative, stochastic, and continuous models.
- Utilize BioModel engineering principles for systematic model design and analysis.
Main Results:
- Demonstration of how ODE models fit into a comprehensive modeling framework.
- Provision of methods and tools for constructing dynamic kinase signaling models.
- Establishment of a systematic approach for analyzing biological system models.
Conclusions:
- Dynamic computational models, particularly ODE-based ones, are valuable for studying kinase signaling.
- A unified framework enhances the design, construction, and analysis of biological models.
- BioModel engineering offers a systematic approach to computational biology.
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