Chapter 5: Network Biology Approach to Complex Diseases
Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals mig...
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Public Library of Science
2012
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531284/ |
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pubmed-35312842013-01-08 Chapter 5: Network Biology Approach to Complex Diseases Cho, Dong-Yeon Kim, Yoo-Ah Przytycka, Teresa M. Education Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations. Public Library of Science 2012-12-27 /pmc/articles/PMC3531284/ /pubmed/23300411 http://dx.doi.org/10.1371/journal.pcbi.1002820 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Cho, Dong-Yeon Kim, Yoo-Ah Przytycka, Teresa M. |
| spellingShingle |
Cho, Dong-Yeon Kim, Yoo-Ah Przytycka, Teresa M. Chapter 5: Network Biology Approach to Complex Diseases |
| author_facet |
Cho, Dong-Yeon Kim, Yoo-Ah Przytycka, Teresa M. |
| author_sort |
Cho, Dong-Yeon |
| title |
Chapter 5: Network Biology Approach to Complex Diseases |
| title_short |
Chapter 5: Network Biology Approach to Complex Diseases |
| title_full |
Chapter 5: Network Biology Approach to Complex Diseases |
| title_fullStr |
Chapter 5: Network Biology Approach to Complex Diseases |
| title_full_unstemmed |
Chapter 5: Network Biology Approach to Complex Diseases |
| title_sort |
chapter 5: network biology approach to complex diseases |
| description |
Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations. |
| publisher |
Public Library of Science |
| publishDate |
2012 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531284/ |
| _version_ |
1611943050095886336 |