| Autor: |
Myers PJ; Department of Chemical Engineering, Charlottesville, VA 22904., Lee SH; Department of Chemical Engineering, Charlottesville, VA 22904., Lazzara MJ; Department of Chemical Engineering, Charlottesville, VA 22904.; Department of Biomedical Engineering University of Virginia, Charlottesville, VA 22904. |
| Jazyk: |
angličtina |
| Zdroj: |
Current opinion in systems biology [Curr Opin Syst Biol] 2021 Dec; Vol. 28. Date of Electronic Publication: 2021 Jun 09. |
| DOI: |
10.1016/j.coisb.2021.05.010 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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