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• Villaamil, V
• Gallego, G
• Cainzos, I
• Valladares-Ayerbes, M
• Aparicio, L
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• Villaamil, V
• Gallego, G
• Cainzos, I
• Valladares-Ayerbes, M
• Aparicio, L
• [+] on PubMed
• Villaamil, V
• Gallego, G
• Cainzos, I
• Valladares-Ayerbes, M
• Aparicio, L

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Int. J. Mol. Sci. 2012, 13(6), 6561-6581; doi:10.3390/ijms13066561

Review

State of the Art in Silico Tools for the Study of Signaling Pathways in Cancer

Vanessa Medina Villaamil ^1,* , Guadalupe Aparicio Gallego ¹ , Isabel Santamarina Cainzos ¹ , Manuel Valladares-Ayerbes ^1,3  and Luis M. Antón Aparicio ^2,3

¹ Biomedical Research Institute (INIBIC), CHU A Coruña, Xubias de Abaixo s/n, PC 15006, A Coruña, Spain ² University of A Coruña (UDC), PC 15006, A Coruña, Spain ³ Medical Oncology Department, CHU A Coruña, PC 15006, A Coruña, Spain

* Author to whom correspondence should be addressed.

Received: 27 March 2012; in revised form: 3 May 2012 / Accepted: 10 May 2012 / Published: 29 May 2012

Download PDF Full-Text [672 KB, uploaded 29 May 2012 13:43 CEST]

Abstract: In the last several years, researchers have exhibited an intense interest in the evolutionarily conserved signaling pathways that have crucial roles during embryonic development. Interestingly, the malfunctioning of these signaling pathways leads to several human diseases, including cancer. The chemical and biophysical events that occur during cellular signaling, as well as the number of interactions within a signaling pathway, make these systems complex to study. In silico resources are tools used to aid the understanding of cellular signaling pathways. Systems approaches have provided a deeper knowledge of diverse biochemical processes, including individual metabolic pathways, signaling networks and genome-scale metabolic networks. In the future, these tools will be enormously valuable, if they continue to be developed in parallel with growing biological knowledge. In this study, an overview of the bioinformatics resources that are currently available for the analysis of biological networks is provided.

Keywords: bioinformatics; cancer; networks; pathways; systems biology

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