Ing on the cellular conditions.DiscussionWe have developed a novel computational

Ing on the cellular conditions.DiscussionWe have developed a novel computational approach designed to identify regulatory motifs and their properties in a signaling network. It is necessary to understand the regulatory mechanisms and their dynamic regulatory properties to get insight into cellular functions. However, it is still difficult to detect dynamic regulatory properties of specific signaling network using experimental techniques because it requires measurements with high temporal and spatial resolution [4]. We thus tried to solve this issue by developing a novel computational method to detect potential regulatory motifs capable of exhibiting dynamic regulatory properties. Our approach accomplishes the identification of the regulatory motifs by compressing the signaling network and detecting the compressed forms of the regulatory motifs by using our subgraph search algorithm to finds various network structures of the regulatory motifs. Our subgraph search algorithm enabled us to efficiently detect the regulatory motifs in large-scale signaling networks. It employs the ESU algorithm to reduce the search space and uses a path-tree to evaluate the feasibility of newly added node. However, it has a AKT inhibitor 2 limitation that the construction of the path-tree totally depends on the isomorphic graphs of query regulatory motifs. The generation of isomorphic graphs requires a large amounts of times when the size of regulatory motif is larger than 5. In order to overcome this 18204824 limitation, 1315463 RMOD system provides a path-tree library for knownregulatory motifs and allows reusing the library even if users make new regulatory motif using motif design tool. We consider continuously updating our path-tree library, whenever a new regulatory motif is discovered. Introducing network compression into regulatory motif detection enables us not only to detect regulatory motifs with various network structures, but also to reduce network complexity by replacing original network into smaller network. However, it does not always guarantee the run-time reduction. Actually, even though the network compression of known human signaling network, which is composed of 1240 nodes and 3144 edges, shows 23 node reduction, the average run-time of detecting 6-node subgraphs increases [31], whereas the average run-times of detecting subgraphs with size 3? nodes decrease. This is the reason why the effect of subgraph creation on the run-time is larger than that of reduction in network size (data not shown). In summary, RMOD is a web-based system for the analysis of regulatory motifs in a signaling network with a novel computational approach for identifying regulatory motifs and their properties. It includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. Therefore, it can be used both as a discovery tool for analyzing regulatory motifs to understand cellular function in natural systems and as a design tool for identifying specific circuits, which can be used to engineer synthetic circuits. In the near future, RMOD will be extended to have more powerful functions by integrating a simulation tools.RMOD: Regulatory Motif Detection ToolSupporting InformationFile S1 List of original known regulatory motifs for (a) oscillation [12], (b) 16960-16-0 web adaptation [13] and (c) bistable switch [3]. A, B, C in the.Ing on the cellular conditions.DiscussionWe have developed a novel computational approach designed to identify regulatory motifs and their properties in a signaling network. It is necessary to understand the regulatory mechanisms and their dynamic regulatory properties to get insight into cellular functions. However, it is still difficult to detect dynamic regulatory properties of specific signaling network using experimental techniques because it requires measurements with high temporal and spatial resolution [4]. We thus tried to solve this issue by developing a novel computational method to detect potential regulatory motifs capable of exhibiting dynamic regulatory properties. Our approach accomplishes the identification of the regulatory motifs by compressing the signaling network and detecting the compressed forms of the regulatory motifs by using our subgraph search algorithm to finds various network structures of the regulatory motifs. Our subgraph search algorithm enabled us to efficiently detect the regulatory motifs in large-scale signaling networks. It employs the ESU algorithm to reduce the search space and uses a path-tree to evaluate the feasibility of newly added node. However, it has a limitation that the construction of the path-tree totally depends on the isomorphic graphs of query regulatory motifs. The generation of isomorphic graphs requires a large amounts of times when the size of regulatory motif is larger than 5. In order to overcome this 18204824 limitation, 1315463 RMOD system provides a path-tree library for knownregulatory motifs and allows reusing the library even if users make new regulatory motif using motif design tool. We consider continuously updating our path-tree library, whenever a new regulatory motif is discovered. Introducing network compression into regulatory motif detection enables us not only to detect regulatory motifs with various network structures, but also to reduce network complexity by replacing original network into smaller network. However, it does not always guarantee the run-time reduction. Actually, even though the network compression of known human signaling network, which is composed of 1240 nodes and 3144 edges, shows 23 node reduction, the average run-time of detecting 6-node subgraphs increases [31], whereas the average run-times of detecting subgraphs with size 3? nodes decrease. This is the reason why the effect of subgraph creation on the run-time is larger than that of reduction in network size (data not shown). In summary, RMOD is a web-based system for the analysis of regulatory motifs in a signaling network with a novel computational approach for identifying regulatory motifs and their properties. It includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. Therefore, it can be used both as a discovery tool for analyzing regulatory motifs to understand cellular function in natural systems and as a design tool for identifying specific circuits, which can be used to engineer synthetic circuits. In the near future, RMOD will be extended to have more powerful functions by integrating a simulation tools.RMOD: Regulatory Motif Detection ToolSupporting InformationFile S1 List of original known regulatory motifs for (a) oscillation [12], (b) adaptation [13] and (c) bistable switch [3]. A, B, C in the.

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