Detecting communities using asymptotical Surprise

Nodes in real-world networks are repeatedly observed to form dense clusters, often referred to as communities. Methods to detect these groups of nodes usually maximize an objective function, which implicitly contains the definition of a community. We here analyze a recently proposed measure called surprise, which assesses the quality of the partition of a network into communities. In its current form, the formulation of surprise is rather difficult to analyze. We here therefore develop an accurate asymptotic approximation. This allows for the development of an efficient algorithm for optimizing surprise. Incidentally, this leads to a straightforward extension of surprise to weighted graphs. Additionally, the approximation makes it possible to analyze surprise more closely and compare it to other methods, especially modularity. We show that surprise is (nearly) unaffected by the well known resolution limit, a particular problem for modularity. However, surprise may tend to overestimate the number of communities, whereas they may be underestimated by modularity. In short, surprise works well in the limit of many small communities, whereas modularity works better in the limit of few large communities. In this sense, surprise is more discriminative than modularity, and may find communities where modularity fails to discern any structure

DEVELOPMENT OF TOOLS FOR ATOM-LEVEL INTERPRETATION OF STABLE ISOTOPE-RESOLVED METABOLOMICS DATASETS

Metabolomics is the global study of small molecules in living systems under a given state, merging as a new ‘omics’ study in systems biology. It has shown great promise in elucidating biological mechanism in various areas. Many diseases, especially cancers, are closely linked to reprogrammed metabolism. As the end point of biological processes, metabolic profiles are more representative of the biological phenotype compared to genomic or proteomic profiles. Therefore, characterizing metabolic phenotype of various diseases will help clarify the metabolic mechanisms and promote the development of novel and effective treatment strategies. Advances in analytical technologies such as nuclear magnetic resonance and mass spectroscopy greatly contribute to the detection and characterization of global metabolites in a biological system. Furthermore, application of these analytical tools to stable isotope resolved metabolomics experiments can generate large-scale high-quality metabolomics data containing isotopic flow through cellular metabolism. However, the lack of the corresponding computational analysis tools hinders the characterization of metabolic phenotypes and the downstream applications. Both detailed metabolic modeling and quantitative analysis are required for proper interpretation of these complex metabolomics data. For metabolic modeling, currently there is no comprehensive metabolic network at an atom-resolved level that can be used for deriving context-specific metabolic models for SIRM metabolomics datasets. For quantitative analysis, most available tools conduct metabolic flux analysis based on a well-defined metabolic model, which is hard to achieve for complex biological system due to the limitations in our knowledge. Here, we developed a set of methods to address these problems. First, we developed a neighborhood-specific coloring method that can create identifier for each atom in a specific compound. With the atom identifiers, we successfully harmonized compounds and reactions across KEGG and MetaCyc databases at various levels. In addition, we evaluated the atom mappings of the harmonized metabolic reactions. These results will contribute to the construction of a comprehensive atom-resolved metabolic network. In addition, this method can be easily applied to any metabolic database that provides a molfile representation of compounds, which will greatly facilitate future expansion. In addition, we developed a moiety modeling framework to deconvolute metabolite isotopologue profiles using moiety models along with the analysis and selection of the best moiety model(s) based on the experimental data. To our knowledge, this is the first method that can analyze datasets involving multiple isotope tracers. Furthermore, instead of a single predefined metabolic model, this method allows the comparison of multiple metabolic models derived from a given metabolic profile, and we have demonstrated the robust performance of the moiety modeling framework in model selection with a 13C-labeled UDP-GlcNAc isotopologue dataset. We further explored the data quality requirements and the factors that affect model selection. Collectively, these methods and tools help interpret SIRM metabolomics datasets from metabolic modeling to quantitative analysis

Synthesis of time-to-amplitude converter by mean coevolution with adaptive parameters

Copyright © 2011 the authors and Scientific Research Publishing Inc. This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)The challenging task to synthesize automatically a time-to-amplitude converter, which unites by its functionality several digital circuits, has been successfully solved with the help of a novel methodology. The proposed approach is based on a paradigm according to which the substructures are regarded as additional mutation types and when ranged with other mutations form a new adaptive individual-level mutation technique. This mutation approach led to the discovery of an original coevolution strategy that is characterized by very low selection rates. Parallel island-model evolution has been running in a hybrid competitive-cooperative interaction throughout two incremental stages. The adaptive population size is applied for synchronization of the parallel evolutions