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Title: Mechanistic and data-driven models of cell signaling: Tools for fundamental discovery and rational design of therapy
A full understanding of cell signaling processes requires knowledge of protein structure–function relationships, protein–protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.  more » « less
Award ID(s):
1716537
PAR ID:
10474520
Author(s) / Creator(s):
; ;
Editor(s):
Finley, Stacey Deleria; Hatzimanikatis, Vassily
Publisher / Repository:
Elsevier Ltd.
Date Published:
Journal Name:
Current Opinion in Systems Biology
Volume:
28
Issue:
C
ISSN:
2452-3100
Page Range / eLocation ID:
100349
Subject(s) / Keyword(s):
Uncertainty, Sensitivity, Parameter sampling, Parameter estimation, Regression, Clustering, Classification, Cancer, Immunology.
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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