Graph-Theoretical Analysis of Biological Networks: A Survey
Biological networks such as protein interaction networks, gene regulation networks, and metabolic pathways are examples of complex networks that are large graphs with small-world and scale-free properties. An analysis of these networks has a profound effect on our understanding the origins of life,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/11/10/188 |
| _version_ | 1797574212345397248 |
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| author | Kayhan Erciyes |
| author_facet | Kayhan Erciyes |
| author_sort | Kayhan Erciyes |
| collection | DOAJ |
| description | Biological networks such as protein interaction networks, gene regulation networks, and metabolic pathways are examples of complex networks that are large graphs with small-world and scale-free properties. An analysis of these networks has a profound effect on our understanding the origins of life, health, and the disease states of organisms, and it allows for the diagnosis of diseases to aid in the search for remedial processes. In this review, we describe the main analysis methods of biological networks using graph theory, by first defining the main parameters, such as clustering coefficient, modularity, and centrality. We then survey fundamental graph clustering methods and algorithms, followed by the network motif search algorithms, with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function that is carried out by that small network, and discovering such a function provides an insight into the overall function of the organism. Lastly, we review network alignment algorithms that find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor, and finding such a relationship may help researchers to derive ancestral relationships and to predict the future evolution of organisms to enable the design of new drugs. We provide a review of the research studies in all of these methods, and conclude using the current challenging areas of biological network analysis, and by using graph theory and parallel processing for high performance analysis. |
| first_indexed | 2024-03-10T21:20:41Z |
| format | Article |
| id | doaj.art-768f0fb3b0c44e8bb4b580deb827724c |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-10T21:20:41Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-768f0fb3b0c44e8bb4b580deb827724c2023-11-19T16:07:32ZengMDPI AGComputation2079-31972023-09-01111018810.3390/computation11100188Graph-Theoretical Analysis of Biological Networks: A SurveyKayhan Erciyes0Sofware Engineering Deptartment, Faculty of Engineering and Natural Sciences, Maltepe University, Maltepe, 34857 İstanbul, TürkiyeBiological networks such as protein interaction networks, gene regulation networks, and metabolic pathways are examples of complex networks that are large graphs with small-world and scale-free properties. An analysis of these networks has a profound effect on our understanding the origins of life, health, and the disease states of organisms, and it allows for the diagnosis of diseases to aid in the search for remedial processes. In this review, we describe the main analysis methods of biological networks using graph theory, by first defining the main parameters, such as clustering coefficient, modularity, and centrality. We then survey fundamental graph clustering methods and algorithms, followed by the network motif search algorithms, with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function that is carried out by that small network, and discovering such a function provides an insight into the overall function of the organism. Lastly, we review network alignment algorithms that find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor, and finding such a relationship may help researchers to derive ancestral relationships and to predict the future evolution of organisms to enable the design of new drugs. We provide a review of the research studies in all of these methods, and conclude using the current challenging areas of biological network analysis, and by using graph theory and parallel processing for high performance analysis.https://www.mdpi.com/2079-3197/11/10/188biological networkgraph analysisclusteringnetwork motifnetwork alignment |
| spellingShingle | Kayhan Erciyes Graph-Theoretical Analysis of Biological Networks: A Survey Computation biological network graph analysis clustering network motif network alignment |
| title | Graph-Theoretical Analysis of Biological Networks: A Survey |
| title_full | Graph-Theoretical Analysis of Biological Networks: A Survey |
| title_fullStr | Graph-Theoretical Analysis of Biological Networks: A Survey |
| title_full_unstemmed | Graph-Theoretical Analysis of Biological Networks: A Survey |
| title_short | Graph-Theoretical Analysis of Biological Networks: A Survey |
| title_sort | graph theoretical analysis of biological networks a survey |
| topic | biological network graph analysis clustering network motif network alignment |
| url | https://www.mdpi.com/2079-3197/11/10/188 |
| work_keys_str_mv | AT kayhanerciyes graphtheoreticalanalysisofbiologicalnetworksasurvey |