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- W2113067631 abstract "Biological networks encapsulate invaluable information about the roles of different biochemical entities and their interactions with each other. Analyzing these networks is essential in order to comprehend the machinery of a cell and to reveal evolutionary differences between different cells and organisms. Three main types of biological networks are protein interaction networks, metabolic networks (or pathways) and regulatory networks. In the literature, the terms “network” and “pathway” are used interchangeably for the metabolic interaction data. An important type of analysis for biological networks is the comparative analysis which aims at identifying functionally similar components of these networks that are shared among different species. Analogous to sequence alignment which identifies sequence similarity, network alignment reveals similar connectivity patterns such as alternative paths and subnetworks. Additional to the comparative analysis, examining solely the topological structure of biological networks also led to interesting observations such as the modular organization, repeating connectivity patterns, the steady states and specific degree distributions that these networks exhibit. In this thesis, we introduce (i) Alignment algorithms for metabolic networks that account for heterogeneous network elements, connected subnetwork mappings and scalability problem in network alignment (ii) An algorithm that predicts functional similarity between reactions based on metabolic flux analysis; (iii) Efficient methods that identify steady states of Boolean regulatory networks using binary decision diagrams (BDDs) and graph partitioning; (iv) An algorithm that identifies dynamic modular structure of regulatory networks." @default.
- W2113067631 created "2016-06-24" @default.
- W2113067631 creator A5036380790 @default.
- W2113067631 creator A5091769289 @default.
- W2113067631 date "2011-01-01" @default.
- W2113067631 modified "2023-09-26" @default.
- W2113067631 title "A comparative study on biological networks: alignment and structural properties" @default.
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