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Network Analysis Methods for Interpreting Complex Phenotypes in Biological Networks
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H Zali   , M Rezaee tavirani * 1, K Haidarbegi , M Shahriari noor |
| 1- , rezaei.tavirani@ibb.ut.ac.ir |
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Abstract: (10250 Views) |
| Gene network analysis is an important part of systems biology studies. Compared with traditional genotype/phenotype studies that focused on establishing the relationships between single genes and interested traits, network analysis give us a global view of how all the genes work together properly, which in turn leads to the correct biological functions. Network analysis also helps to derive useful information from the network and also helps the discovery of biological processes from a network. In this study, the main methods and applications in network analysis to interpret complex phenotypes basically explain three aspects. The first aspect is to identify the importance of each node in the network which determine more important or crucial genes, or less important or dispensable one. Another aspect is to identify which genes are more functionally related through the whole network view by measuring the direct gene connections and also by considering the connections through the whole network. Identifying the paths or flows through the networks with known input and output genes is the last aspect discussed in network analysis. Although these methods have many advantages, network biology still faces many challenges so more methods have emerged, which provide important tools for network analysis. Mastering these methods is necessary, but far from sufficient for understanding biology. More important things to do are to ask the right questions, to choose proper network analysis tools, and to validate analysis results by solid experimentation. Finally can express that the fundamental goal is the same for network biology and molecular biology – to better understand biological processes and the mechanisms of human diseases. |
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| Keywords: Node, Edge, Gene expression networks, Network analysis |
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Full-Text [PDF 450 kb]
(4019 Downloads)
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Type of Study: Research |
Subject:
Medical microbiology
Received: 2013/03/18 | Accepted: 2013/06/16 | Published: 2013/06/16
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