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Molecular interaction maps of bioregulatory networks: a general rubric for systems biology
- Mol Biol Cell
, 2006
"... A standard for bioregulatory network diagrams is urgently needed in the same way that circuit diagrams are needed in electronics. Several graphical notations have been proposed, but none has become standard. We have prepared many detailed bioregulatory network diagrams using the molecular interactio ..."
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A standard for bioregulatory network diagrams is urgently needed in the same way that circuit diagrams are needed in electronics. Several graphical notations have been proposed, but none has become standard. We have prepared many detailed bioregulatory network diagrams using the molecular interaction map (MIM) notation, and we now feel confident that it is suitable as a standard. Here, we describe the MIM notation formally and discuss its merits relative to alternative proposals. We show by simple examples how to denote all of the molecular interactions commonly found in bioregulatory networks. There are two forms of MIM diagrams. “Heuristic ” MIMs present the repertoire of interactions possible for molecules that are colocalized in time and place. “Explicit ” MIMs define particular models (derived from heuristic MIMs) for computer simulation. We show also how pathways or processes can be highlighted on a canonical heuristic MIM. Drawing aMIM diagram, adhering to the rules of notation, imposes a logical discipline that sharpens one’s understanding of the structure and function of a network.
Bioinformatics Advance Access published June 12, 2008 Ensemble Non-negative Matrix Factorization Methods for Clustering Protein-Protein Interactions
"... Motivation: When working with large-scale protein interaction data, an important analysis task is the assignment of pairs of proteins to groups that correspond to higher order assemblies. Previously a common approach to this problem has been to apply standard hierarchical clustering methods to ident ..."
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Motivation: When working with large-scale protein interaction data, an important analysis task is the assignment of pairs of proteins to groups that correspond to higher order assemblies. Previously a common approach to this problem has been to apply standard hierarchical clustering methods to identify such a groups. Here we propose a new algorithm for aggregating a diverse collection of matrix factorizations to produce a more informative clustering, which takes the form of a “soft ” hierarchy of clusters. Results: We apply the proposed Ensemble NMF algorithm to a high-quality assembly of binary protein interactions derived from two proteome-wide studies in yeast. Our experimental evaluation demonstrates that the algorithm lends itself to discovering small localized structures in this data, which correspond to known functional groupings of complexes. In addition, we show that the algorithm also supports the assignment of putative functions for previously uncharacterized proteins, for instance the protein YNR024W, which may be an uncharacterized component of the exosome. Contact:
E. Battistella et al. 506 An integrated model for cellular analysis
, 2005
"... ABSTRACT. We present the MOlecular NETwork (MONET) ontology as a model to integrate data from different networks that govern cell function. To achieve this, different existing ontologies were analyzed and an integrated ontology was built in a way to make it possible to share and reuse knowledge, sup ..."
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ABSTRACT. We present the MOlecular NETwork (MONET) ontology as a model to integrate data from different networks that govern cell function. To achieve this, different existing ontologies were analyzed and an integrated ontology was built in a way to make it possible to share and reuse knowledge, support interoperability between systems, and also allow the formulation of hypotheses through inferences. By studying the cell as an entity of a myriad of elements and networks of interactions, we aim to offer a means to understand the large-scale characteristics responsible for the behavior of the cell and to enable new biological insights.
An Ontology-Empowered Model for Annotating Protein-Protein Interaction Data: a Case Study for Budding Yeast
"... This paper reports on our experience in modeling and employing ontology-inferred knowledge to support and improve data mining tasks of yeast protein interactions for knowledge discovery. This objective has been accomplished by providing simplified access to units of intersecting proteome data and in ..."
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This paper reports on our experience in modeling and employing ontology-inferred knowledge to support and improve data mining tasks of yeast protein interactions for knowledge discovery. This objective has been accomplished by providing simplified access to units of intersecting proteome data and information from different biological databases and bio-ontologies, and utilizing a logical framework to answer questions from biologists. 1.
Protein Interaction- Targeted Drug Discovery: Evaluating Critical Issues
"... The goal of drug discovery is to develop novel small-molecule compounds that ameliorate, cure, and (optimal-ly) prevent clinically significant diseases. It has been much asserted that the resources of genome and proteome projects will contribute significantly towards this goal. The volume of informa ..."
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The goal of drug discovery is to develop novel small-molecule compounds that ameliorate, cure, and (optimal-ly) prevent clinically significant diseases. It has been much asserted that the resources of genome and proteome projects will contribute significantly towards this goal. The volume of information generated through these pro-jects is by any objective standard impressive and over-whelming, providing a great impetus to the development of a bioinformatics community that will be able to make sense of the data onslaught (issues discussed in Refer-ences 12 and 13). From an intellectual perspective, this work has the potential to add a new level of rigorous math-ematical modeling to the common conceptions of cell, tis-sue, and organism over the next decade or so. However, from a pragmatic perspective focused on drug discovery
