Modeling and analysis of the dynamics of signaling transduction networks can be powerful tools to understand and predict how cells will respond to native signals and artificial perturbations. This is of special interest for analyzing disease processes associated with signal transduction malfunctioning and to contribute to the development of efficient drug treatment strategies. In this work we examine the advantages of a kinetics-based framework as compared with purely topological approaches to identify input sets and disruption strategies that preserve desired cellular functions while blocking undesired disease states in signaling networks. These differences are highlighted through two examples where the mechanistic-based approach captures information that the topological-based analysis is unable to reveal.
|Original language||English (US)|
|Number of pages||7|
|Journal||Computers and Chemical Engineering|
|State||Published - Sep 26 2008|
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)
- Computer Science Applications