.png){kind=icon}
.png){kind=icon}
Return
pathPSA: A Dynamical Pathway-Based Parametric Sensitivity Analysis
.png){kind=icon}
PRE
AI
DOI：10.1021/ie403277d.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Normal functioning of biological systems relies on coordinated activities of biomolecules participating in a complex network of biological interactions. As human intuition is often incapable in analyzing how biological functions emerge from such complex interactions, the use of mathematical models and systems analysis tools has become critical in understanding biological system behavior. While biological functions have been associated with the connectivity (structure) and pathways of the network and the kinetics of the processes involved, most model analyses address each of these aspects separately. In contrast, we present a novel sensitivity analysis, called pathway-based parametric sensitivity analysis (pathPSA), which combines the analysis of both network structure and kinetics. Unlike existing sensitivity analyses, pathPSA relies on perturbing the kinetics of pathways in the network, using persistent perturbations or impulse perturbations at varying time. Consequently, the sensitivity coefficients can give insights on the dominant pathways in a network and any transient shift in the rate controlling mechanism. The efficacy of pathPSA is demonstrated through an application to understand competing signaling pathways in programmed cell death network.
Keywords:
SYSTEMS BIOLOGY
MODEL
ROBUSTNESS
PHASE
IDENTIFIABILITY
REDUCTION
NETWORKS
TOOLBOX
DESIGN
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.9
Papers:
4.0W
Citations:
9.6W