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Towards finding the linkage between metabolic and age-related disorders using semantic gene data network analysis

A metabolic disorder (MD) occurs when the metabolic process is disturbed. This process is carried out by thousands of enzymes participating in numerous inter-dependent metabolic pathways. Critical biochemical reactions that involve the processing and transportation of carbohydrates, proteins and lip...

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Detalles Bibliográficos
Autores principales: Uzzal Hossain, Mohammad, Zaffar Shibly, Abu, Md. Omar, Taimur, Tous Zohora, Fatama, Sara Santona, Umme, Hossain, Md. Jakir, Hosen Khoka, Md. Sadek, Ara Keya, Chaman, Salimullah, Md.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857462/
https://www.ncbi.nlm.nih.gov/pubmed/27212841
http://dx.doi.org/10.6026/97320630012022
Descripción
Sumario:A metabolic disorder (MD) occurs when the metabolic process is disturbed. This process is carried out by thousands of enzymes participating in numerous inter-dependent metabolic pathways. Critical biochemical reactions that involve the processing and transportation of carbohydrates, proteins and lipids are affected in metabolic diseases. Therefore, it is of interest to identify the common pathways of metabolic disorders by building protein-protein interactions (PPI) for network analysis. The molecular network linkages between MD and age related diseases (ARD) are intriguing. Hence, we created networks of protein-protein interactions that are related with MD and ARD using relevant known data in the public domain. The network analysis identified known MD associated proteins and predicted genes and or its products of ARD in common pathways. The genes in the common pathways were isolated from the network and further analyzed for their co-localization and shared domains. Thus, a model hypothesis is proposed using interaction networks that are linked between MD and ARD. This data even if less conclusive finds application in understanding the molecular mechanism of known diseases in relation to observed molecular events