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McLean C, Sorokin A, Simpson TI, Armstrong JD, Sorokina O. BioNAR: an integrated biological network analysis package in bioconductor. BIOINFORMATICS ADVANCES 2023; 3:vbad137. [PMID: 37860105 PMCID: PMC10582516 DOI: 10.1093/bioadv/vbad137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/25/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023]
Abstract
Motivation Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different sub-complexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analysing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone. Results Here, we present the Bioconductor package BioNAR, which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the network's vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a protein's impact within multiple complexes, and enables estimation of the co-occurrence of metadata, i.e. diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms. Availability and implementation The package is available from Bioconductor release 3.17: https://bioconductor.org/packages/release/bioc/html/BioNAR.html.
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Affiliation(s)
- Colin McLean
- Edinburgh Cancer Research Centre, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Anatoly Sorokin
- Biological Systems Unit, Okinawa Institute of Science and Technology, Onna, Okinawa 904-0495, Japan
| | - Thomas Ian Simpson
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
| | - James Douglas Armstrong
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
- Computational Biomedicine Institute (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany
| | - Oksana Sorokina
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
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Takemoto K, Kajihara K. Human Impacts and Climate Change Influence Nestedness and Modularity in Food-Web and Mutualistic Networks. PLoS One 2016; 11:e0157929. [PMID: 27322185 PMCID: PMC4913940 DOI: 10.1371/journal.pone.0157929] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Accepted: 06/07/2016] [Indexed: 11/18/2022] Open
Abstract
Theoretical studies have indicated that nestedness and modularity—non-random structural patterns of ecological networks—influence the stability of ecosystems against perturbations; as such, climate change and human activity, as well as other sources of environmental perturbations, affect the nestedness and modularity of ecological networks. However, the effects of climate change and human activities on ecological networks are poorly understood. Here, we used a spatial analysis approach to examine the effects of climate change and human activities on the structural patterns of food webs and mutualistic networks, and found that ecological network structure is globally affected by climate change and human impacts, in addition to current climate. In pollination networks, for instance, nestedness increased and modularity decreased in response to increased human impacts. Modularity in seed-dispersal networks decreased with temperature change (i.e., warming), whereas food web nestedness increased and modularity declined in response to global warming. Although our findings are preliminary owing to data-analysis limitations, they enhance our understanding of the effects of environmental change on ecological communities.
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Affiliation(s)
- Kazuhiro Takemoto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka Fukuoka, Japan
| | - Kosuke Kajihara
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka Fukuoka, Japan
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Takemoto K. Habitat variability does not generally promote metabolic network modularity in flies and mammals. Biosystems 2015; 139:46-54. [PMID: 26723229 DOI: 10.1016/j.biosystems.2015.12.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 12/06/2015] [Accepted: 12/09/2015] [Indexed: 11/24/2022]
Abstract
The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered.
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Affiliation(s)
- Kazuhiro Takemoto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan.
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Takemoto K. Metabolic networks are almost nonfractal: a comprehensive evaluation. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 90:022802. [PMID: 25215776 DOI: 10.1103/physreve.90.022802] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Indexed: 06/03/2023]
Abstract
Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.
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Affiliation(s)
- Kazuhiro Takemoto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
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Climatic seasonality may affect ecological network structure: food webs and mutualistic networks. Biosystems 2014; 121:29-37. [PMID: 24907523 DOI: 10.1016/j.biosystems.2014.06.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Revised: 05/30/2014] [Accepted: 06/02/2014] [Indexed: 11/23/2022]
Abstract
Ecological networks exhibit non-random structural patterns, such as modularity and nestedness, which determine ecosystem stability with species diversity and connectance. Such structure-stability relationships are well known. However, another important perspective is less well understood: the relationship between the environment and structure. Inspired by theoretical studies that suggest that network structure can change due to environmental variability, we collected data on a number of empirical food webs and mutualistic networks and evaluated the effect of climatic seasonality on ecological network structure. As expected, we found that climatic seasonality affects ecological network structure. In particular, an increase in modularity due to climatic seasonality was observed in food webs; however, it is debatable whether this occurs in mutualistic networks. Interestingly, the type of climatic seasonality that affects network structure differs with ecosystem type. Rainfall and temperature seasonality influence freshwater food webs and mutualistic networks, respectively; food webs are smaller, and more modular, with increasing rainfall seasonality. Mutualistic networks exhibit a higher diversity (particularly of animals) with increasing temperature seasonality. These results confirm the theoretical prediction that the stability increases with greater perturbation. Although these results are still debatable because of several limitations in the data analysis, they may enhance our understanding of environment-structure relationships.
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Winterbach W, Mieghem PV, Reinders M, Wang H, Ridder DD. Topology of molecular interaction networks. BMC SYSTEMS BIOLOGY 2013; 7:90. [PMID: 24041013 PMCID: PMC4231395 DOI: 10.1186/1752-0509-7-90] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Accepted: 08/01/2013] [Indexed: 12/23/2022]
Abstract
Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.
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Affiliation(s)
- Wynand Winterbach
- Network Architectures and Services, Department of Intelligent Systems, Faculty of
Electrical Engineering, Mathematics and Computer Science, Delft University of
Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
- Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical
Engineering, Mathematics and Computer Science, Delft University of Technology,
P.O. Box 5031, 2600 GA Delft, The Netherlands
| | - Piet Van Mieghem
- Network Architectures and Services, Department of Intelligent Systems, Faculty of
Electrical Engineering, Mathematics and Computer Science, Delft University of
Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
| | - Marcel Reinders
- Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical
Engineering, Mathematics and Computer Science, Delft University of Technology,
P.O. Box 5031, 2600 GA Delft, The Netherlands
- Netherlands Bioinformatics Center, 6500 HB Nijmegen, The Netherlands
- Kluyver Centre for Genomics of Industrial Fermentation, 2600 GA Delft, The
Netherlands
| | - Huijuan Wang
- Network Architectures and Services, Department of Intelligent Systems, Faculty of
Electrical Engineering, Mathematics and Computer Science, Delft University of
Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
| | - Dick de Ridder
- Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical
Engineering, Mathematics and Computer Science, Delft University of Technology,
P.O. Box 5031, 2600 GA Delft, The Netherlands
- Netherlands Bioinformatics Center, 6500 HB Nijmegen, The Netherlands
- Kluyver Centre for Genomics of Industrial Fermentation, 2600 GA Delft, The
Netherlands
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Modular organization of cancer signaling networks is associated with patient survivability. Biosystems 2013; 113:149-54. [DOI: 10.1016/j.biosystems.2013.06.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2013] [Revised: 06/13/2013] [Accepted: 06/16/2013] [Indexed: 01/28/2023]
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Friedlander T, Mayo AE, Tlusty T, Alon U. Mutation rules and the evolution of sparseness and modularity in biological systems. PLoS One 2013; 8:e70444. [PMID: 23936433 PMCID: PMC3735639 DOI: 10.1371/journal.pone.0070444] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2013] [Accepted: 06/18/2013] [Indexed: 11/21/2022] Open
Abstract
Biological systems exhibit two structural features on many levels of organization: sparseness, in which only a small fraction of possible interactions between components actually occur; and modularity – the near decomposability of the system into modules with distinct functionality. Recent work suggests that modularity can evolve in a variety of circumstances, including goals that vary in time such that they share the same subgoals (modularly varying goals), or when connections are costly. Here, we studied the origin of modularity and sparseness focusing on the nature of the mutation process, rather than on connection cost or variations in the goal. We use simulations of evolution with different mutation rules. We found that commonly used sum-rule mutations, in which interactions are mutated by adding random numbers, do not lead to modularity or sparseness except for in special situations. In contrast, product-rule mutations in which interactions are mutated by multiplying by random numbers – a better model for the effects of biological mutations – led to sparseness naturally. When the goals of evolution are modular, in the sense that specific groups of inputs affect specific groups of outputs, product-rule mutations also lead to modular structure; sum-rule mutations do not. Product-rule mutations generate sparseness and modularity because they tend to reduce interactions, and to keep small interaction terms small.
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Affiliation(s)
- Tamar Friedlander
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Avraham E. Mayo
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Tsvi Tlusty
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel
- Simons Center for Systems Biology, Institute for Advanced Study, Princeton, New Jersey, United States of America
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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Does habitat variability really promote metabolic network modularity? PLoS One 2013; 8:e61348. [PMID: 23593470 PMCID: PMC3625173 DOI: 10.1371/journal.pone.0061348] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Accepted: 03/06/2013] [Indexed: 11/19/2022] Open
Abstract
The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.
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