Using Topology of the Metabolic Network to Predict Viability of Mutant Strains
Background: Understanding the relationships between the structure (topology) and function of biological networks is a central question of systems biology. The idea that topology is a major determinant of systems function has become an attractive and highly-disputed hypothesis. While the structural a...
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| Format: | Article |
| Language: | English |
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BioMed Central Ltd
2010
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| Online Access: | http://hdl.handle.net/1721.1/59195 https://orcid.org/0000-0002-0785-5410 |
| _version_ | 1826199495905378304 |
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| author | Mirny, Leonid A. Wunderlich, Zeba |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Mirny, Leonid A. Wunderlich, Zeba |
| author_sort | Mirny, Leonid A. |
| collection | MIT |
| description | Background: Understanding the relationships between the structure (topology) and function of biological networks is a central question of systems biology. The idea that topology is a major determinant of systems function has become an attractive and highly-disputed hypothesis. While the structural analysis of interaction networks demonstrates a correlation between the topological properties of a node (protein, gene) in the network and its functional essentiality, the analysis of metabolic networks fails to find such correlations. In contrast, approaches utilizing both the topology and biochemical parameters of metabolic networks, e.g. flux balance analysis (FBA), are more successful in predicting phenotypes of knock-out strains. Results: We reconcile these seemingly conflicting results by showing that the topology of E. coli's metabolic network is, in fact, sufficient to predict the viability of knock-out strains with accuracy comparable to FBA on a large, unbiased dataset of mutants. This surprising result is obtained by introducing a novel topology-based measure of network transport: synthetic accessibility. We also show that other popular topology-based characteristics like node degree, graph diameter, and node usage (betweenness) fail to predict the viability of mutant strains. The success of synthetic accessibility demonstrates its ability to capture the essential properties of the metabolic network, such as the branching of chemical reactions and the directed transport of material from inputs to outputs. Conclusions: Our results (1) strongly support a link between the topology and function of biological networks; (2) in agreement with recent genetic studies, emphasize the minimal role of flux re-routing in providing robustness of mutant strains. |
| first_indexed | 2024-09-23T11:21:10Z |
| format | Article |
| id | mit-1721.1/59195 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:21:10Z |
| publishDate | 2010 |
| publisher | BioMed Central Ltd |
| record_format | dspace |
| spelling | mit-1721.1/591952022-09-27T18:56:28Z Using Topology of the Metabolic Network to Predict Viability of Mutant Strains Mirny, Leonid A. Wunderlich, Zeba Harvard University--MIT Division of Health Sciences and Technology Mirny, Leonid A. Background: Understanding the relationships between the structure (topology) and function of biological networks is a central question of systems biology. The idea that topology is a major determinant of systems function has become an attractive and highly-disputed hypothesis. While the structural analysis of interaction networks demonstrates a correlation between the topological properties of a node (protein, gene) in the network and its functional essentiality, the analysis of metabolic networks fails to find such correlations. In contrast, approaches utilizing both the topology and biochemical parameters of metabolic networks, e.g. flux balance analysis (FBA), are more successful in predicting phenotypes of knock-out strains. Results: We reconcile these seemingly conflicting results by showing that the topology of E. coli's metabolic network is, in fact, sufficient to predict the viability of knock-out strains with accuracy comparable to FBA on a large, unbiased dataset of mutants. This surprising result is obtained by introducing a novel topology-based measure of network transport: synthetic accessibility. We also show that other popular topology-based characteristics like node degree, graph diameter, and node usage (betweenness) fail to predict the viability of mutant strains. The success of synthetic accessibility demonstrates its ability to capture the essential properties of the metabolic network, such as the branching of chemical reactions and the directed transport of material from inputs to outputs. Conclusions: Our results (1) strongly support a link between the topology and function of biological networks; (2) in agreement with recent genetic studies, emphasize the minimal role of flux re-routing in providing robustness of mutant strains. 2010-10-12T18:17:50Z 2010-10-12T18:17:50Z 2005-12 2005-12 2010-09-03T16:13:26Z Article http://purl.org/eprint/type/JournalArticle 1465-6906 http://hdl.handle.net/1721.1/59195 Genome Biology. 2005 Dec 28;6(13):P15 https://orcid.org/0000-0002-0785-5410 en http://dx.doi.org/10.1186/gb-2005-6-13-p15 Genome Biology Creative Commons Attribution et al.; licensee BioMed Central Ltd. application/pdf BioMed Central Ltd BioMed Central Ltd |
| spellingShingle | Mirny, Leonid A. Wunderlich, Zeba Using Topology of the Metabolic Network to Predict Viability of Mutant Strains |
| title | Using Topology of the Metabolic Network to Predict Viability of Mutant Strains |
| title_full | Using Topology of the Metabolic Network to Predict Viability of Mutant Strains |
| title_fullStr | Using Topology of the Metabolic Network to Predict Viability of Mutant Strains |
| title_full_unstemmed | Using Topology of the Metabolic Network to Predict Viability of Mutant Strains |
| title_short | Using Topology of the Metabolic Network to Predict Viability of Mutant Strains |
| title_sort | using topology of the metabolic network to predict viability of mutant strains |
| url | http://hdl.handle.net/1721.1/59195 https://orcid.org/0000-0002-0785-5410 |
| work_keys_str_mv | AT mirnyleonida usingtopologyofthemetabolicnetworktopredictviabilityofmutantstrains AT wunderlichzeba usingtopologyofthemetabolicnetworktopredictviabilityofmutantstrains |