Modeling of protein signaling networks in clinical proteomics

Geho, David H., Petricoin III, Emanuel F., Liotta, Lance A., & Araujo, Robyn (2005) Modeling of protein signaling networks in clinical proteomics. Cold Spring Harbor Symposia on Quantitative Biology, 70, pp. 517-524.

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Abstract

Molecular interactions that underlie pathophysiological states are being elucidated using techniques that profile proteomicend points in cellular systems. Within the field of cancer research, protein interaction networks play pivotal roles in the establishment and maintenance of the hallmarks of malignancy, including cell division, invasion, and migration. Multiple complementary tools enable a multifaceted view of how signal protein pathway alterations contribute to pathophysiological states.One pivotal technique is signal pathway profiling of patient tissue specimens. This microanalysis technology provides a proteomic snapshot at one point in time of cells directly procured from the native context of a tumor micro environment. To study the adaptive patterns of signal pathway events over time, before and after experimental therapy, it is necessary to obtain biopsies from patients before, during, and after therapy. A complementary approach is the profiling of cultured cell lines with and without treatment. Cultured cell models provide the opportunity to study short-term signal changes occurring over minutes to hours. Through this type of system, the effects of particular pharmacological agents may be used to test the effects of signal pathway inhibition or activation on multiple endpoints within a pathway. The complexity of the data generated has necessitated the development of mathematical models for optimal interpretation of interrelated signaling pathways. In combination,clinical proteomic biopsy profiling, tissue culture proteomic profiling, and mathematical modeling synergistically enable a deeper understanding of how protein associations lead to disease states and present new insights into the design of therapeutic regimens.

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ID Code: 74323
Item Type: Journal Article
Refereed: Yes
DOI: 10.1101/sqb.2005.70.022
ISSN: 1943-4456
Subjects: Australian and New Zealand Standard Research Classification > BIOLOGICAL SCIENCES (060000)
Divisions: Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 23 Jul 2014 23:37
Last Modified: 25 Jul 2014 00:18

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