http://precedings.nature.com/tags/graph%20theory Recently posted documents tagged with 'graph theory' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/1768/version/1 Multivariate experiments and genomics studies applied to mammalian cells often produce lists of genes or proteins altered under treatment/disease vs. control/normal conditions. Such lists can be identified in known protein-protein interaction networks to produce subnetworks that “connect” the genes or proteins from the lists. Such subnetworks are valuable for biologists since they can suggest regulatory mechanisms that are altered under different conditions. Often such subnetworks are overloaded with links and nodes resulting in connectivity diagrams that are illegible due to edge overlap. In this study, we attempt to address this problem by implementing an approximation to the Steiner Tree problem to connect seed lists of mammalian proteins/genes using literature-based protein-protein interaction networks. To avoid over-representation of hubs in the resultant Steiner Trees we assign a cost to Steiner Vertices based on their connectivity degree. We applied the algorithm to lists of genes commonly mutated in colorectal cancer to demonstrate the usefulness of this approach. http://precedings.nature.com/documents/1768/version/1 Mon, 07 Apr 2008 16:22:36 UTC Connecting Seed Lists of Mammalian Proteins Using Steiner Trees hdl:10101/npre.2008.1768.1 2008-04-07 Avi Ma'ayan Nature Precedings 2008-04-07T16:22:36Z Manuscript Biotechnology Cancer Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.1749.1 The slides are from a presentation given by Professor Ravi Iyengar from Mount Sinai School of Medicine at the Drug Forum Meeting #9 that took place in Washington, DC on February 20-21, 2008. The slides describe two projects: one that was published last year, and the other unpublished. These projects used network analysis to explore the relationships between FDA approved drugs and a human protein-protein interaction network. http://dx.doi.org/10.1038/npre.2008.1749.1 Wed, 02 Apr 2008 19:57:56 UTC Systems Pharmacology doi:10.1038/npre.2008.1749.1 2008-04-02 Ravi Iyengar Nature Precedings 2008-04-02T19:57:56Z Presentation Biotechnology Chemistry Pharmacology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/25/version/1 Several meso-scale biological intracellular regulatory networks that have specified directionality of interactions have been recently assembled from experimental literature. Directed networks where links are characterized as positive or negative can be converted to systems of differential equations and analyzed as dynamical systems. Such analyses have shown that networks containing only sign-consistent loops, such as positive feed-forward and feedback loops function as monotone systems that display well-ordered behavior. Perturbations to monotone systems have unambiguous global effects and a predictability characteristic that confers advantages for robustness and adaptability. We find that three intracellular regulatory networks: bacterial and yeast transcriptional networks and a mammalian signaling network contain far more sign-consistent feedback and feed-forward loops than expected for shuffled networks. Inconsistent loops with negative links can be more easily removed from real regulatory networks as compared to shuffled networks. This topological feature in real networks emerges from the presence of hubs that are enriched for either negative or positive links, and is not due to a preference for double negative links in paths. These observations indicate that intracellular regulatory networks may be close to monotone systems and that this network topology contributes to the dynamic stability. http://precedings.nature.com/documents/25/version/1 Tue, 23 Jan 2007 16:58:17 UTC Intracellular Regulatory Networks are close to Monotone Systems hdl:10101/npre.2007.25.1 2007-01-23 Avi Ma'ayan Nature Precedings 2007-01-23T16:58:17Z Manuscript Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2007.24.1 The mammalian cell can be represented as a large modular network that consists of a central signal network that interacts with and regulates multiple cellular machines that are responsible for phenotypic behavior. We have used graph-theory approaches to analyze signal flow through a network representing the hippocampal neuron and find that signal-induced connectivity results in the formation of many regulatory motifs. Information flow through the central signaling network is initiated by extra-cellular signals such as hormones binding to their receptors. The flow of information through the signaling network results in the appearance of regulatory motifs such as feedback loops, feedforward and bifan motifs. Within the large cellular networks, these regulatory motifs are juxtaposed next to each other in several formats such as the stacked configuration or the nested configuration. We have studied the dynamics of regulatory motifs by biochemical computation using ordinary differential equation models. Positive feedback loops can function as bistable switches. Nested feed-forward motifs can give rise to two emergent properties: coincidence detection and prolonged outputs for short inputs. Bifan motifs can control response times, with some configurations working as delays and others promoting rapid responses. Bifan motifs can also act as filters. Feed-forward motifs lead to signal prolongation and thus function as a switch to alter cell state. The functional consequences of organization of motifs within networks as well as the properties of feedback, feedforward and bifans motifs are presented. http://dx.doi.org/10.1038/npre.2007.24.1 Tue, 23 Jan 2007 13:01:12 UTC Dynamic Topology of Biological Networks doi:10.1038/npre.2007.24.1 2007-01-23 Ravi Iyengar Nature Precedings 2007-01-23T13:01:12Z Presentation Molecular Cell Biology Neuroscience Pharmacology Bioinformatics http://creativecommons.org/licenses/by/2.5/