Search

Steady state metabolic network dynamics analysis (SMDA) is a recently proposed computational metabolomics tool that (i) captures a metabolic network and its rules via a metabolic network database, (ii) mimics the reasoning of a biochemist, given a set of metabolic observations, and (iii) locates efficiently all possible metabolic activation/inactivation (flux) alternatives. However, a number of factors may cause the SMDA algorithm to eliminate feasible flux scenarios. These factors include (i) inherent error margins in observations (measurements), (ii) lack of knowledge to classify measurements as normal versus abnormal, and (iii) choosing a highly constrained metabolic subnetwork to query against. In this work, we first present and formalize these obstacles. Then, we propose techniques to eliminate them and present an experimental evaluation of our proposed techniques. [ABSTRACT FROM AUTHOR]

Metabolism is a representation of the biochemical principles that govern the production, consumption, degradation, and biosynthesis of metabolites in living cells. Organisms respond to changes in their physiological conditions or environmental perturbations (i.e. constraints) via cooperative implementation of such principles. Querying inner working principles of metabolism under different constraints provides invaluable insights for both researchers and educators. In this paper, we propose a metabolism query language (MQL) and discuss its query processing. MQL enables researchers to explore the behavior of the metabolism with a wide-range of predicates including dietary and physiological condition specifications. The query results of MQL are enriched with both textual and visual representations, and its query processing is completely tailored based on the underlying metabolic principles. [ABSTRACT FROM AUTHOR]

Background: Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. Results: In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. Conclusion: GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values. [ABSTRACT FROM AUTHOR]

Motivation: Biological pathways provide significant insights on the interaction mechanisms of molecules. Presently, many essential pathways still remain unknown or incomplete for newly sequenced organisms. Moreover, experimental validation of enormous numbers of possible pathway candidates in a wet-lab environment is time- and effort-extensive. Thus, there is a need for comparative genomics tools that help scientists predict pathways in an organism's biological network. [ABSTRACT FROM PUBLISHER]

This paper describes VISUAL, a graphical icon-based query language with a user-friendly graphical user interface for scientific databases and its query processing techniques. VISUAL is suitable for domains where visualization of the relationships is important for the domain scientist to express queries. In VISUAL, graphical objects are not tied to the underlying formalism; instead, they represent the relationships of the application domain. VISUAL supports relational, nested, and object-oriented models naturally and has formal basis. For ease of understanding and for efficiency reasons, two VISUAL semantics are introduced, namely, the interpretation and execution semantics. Translations from VISUAL to the Object Query Language (for portability considerations) and to an object algebra (for query processing purposes) are presented. Concepts of external and internal queries are developed as modularization tools. [ABSTRACT FROM AUTHOR]

Databases that are mainly used for statistical analysis are called statistical databases (SDB). A statistical database management system (SDBMS) may be defined as a database management system that provides capabilities 1) to model, store, and manipulate data in a manner suitable for the needs of SDB users, and 2) to apply statistical data analysis techniques that range from simple summary statistics to advanced procedures. This paper surveys the existing and proposed SDB data definition and data manipulation (i.e., query) languages. [ABSTRACT FROM AUTHOR]

A statistical database (SDB) may be defined as an ordinary database with the capability of providing statistical information to user queries. The security problem for the SDB is to limit the use of the SDB so that only statistical information is available and no sequence of queries is sufficient to infer protected information about any individual. When such information is obtained, the SDB is said to be compromised. Inference control mechanisms are internal protection mechanisms applied to SDB's. Many researchers have studied different protection mechanisms to prevent an SDB from being compromised. However, most of these mechanisms are either ineffective or inefficient or are only applicable to large SDB's. Auditing in SDB's is initially proposed in the form of investigating log trails manually. In this paper, we present a practical technique for managing the past history of user's queries, discuss how the sequence of all the answered queries of the SDB can be reduced and stored in finite storage, and describe how this storage scheme can provide an effective way of checking compromise. We believe that this will help us develop a more practical and efficient tool for protection in a small SDB than the previously known mechanisms. Further, we extend the idea to checking compromise of a set of queries in a more efficient way than one query at a time. We also show that the problem of maximizing the amount of information to the users without compromising the SDB is NP-complete. [ABSTRACT FROM AUTHOR]

The security problem of a statistical database is to limit database use so that no private information is deducible. This paper discusses the advantages of using a Question-Answering System and a security kernel to enhance the security constraints at the conceptual model level. An SDB design with the goal of helping the OBA in specifying certain security constraints is proposed. [ABSTRACT FROM AUTHOR]

This paper proposes a relational calculus (RC/S) which uses set comparison and set manipulation operators to replace universal quantifiers and negations (with the exception of formulas of type ...R(x), i.e., "x is not in R"). We argue that, compared to the relational calculus (RC) of Codd, RC/S queries are easier to construct and easier to comprehend. We prove that the expressive power of RC is equivalent to the expressive power of RC/S, and give algorithms for translating an RC query into an RC/S query and vice versa. We define a safe RCIS query as one that has finite output and that can be evaluated in finite time. We then define a subset of RC/S queries, called RC/S*, and prove that RC/S* is safe. RC/S* is compared to the existing largest safe subsets of RC, i.e., the evaluable formulas and the allowed formulas. We give algorithms to transform any evaluable formula into an RC/S* query. Also, some RC/S* formulas that are not evaluable are given. RC/S* queries can be directly implemented using a graphical language similar to Query-by-Example (QBE). We briefly describe two different graphical languages that are equivalent, to the RC/S* in expressive power, and compare these languages to the QBE. [ABSTRACT FROM AUTHOR]

Comparing and identifying matching metabolites, reactions, and compartments in genome-scale reconstructed metabolic networks can be difficult due to inconsistent naming in different networks. In this paper, we propose metabolite and reaction matching techniques for matching metabolites and reactions in a given metabolic network to metabolites and reactions in another metabolic network. We employ a variety of techniques that include approximate string matching, similarity score functions and multi-step filtering techniques, all enhanced by a set of rules based on the underlying metabolic biochemistry. The proposed techniques are evaluated by an empirical study on four pairs of metabolic networks, and significant accuracy gains are achieved using the proposed metabolite and reaction identification techniques. [ABSTRACT FROM AUTHOR]

Time-by-Example (TBE) is a user-friendly query language designed specifically for historical relational databases. It follows the graphical structure and the example query concept of QBE, and employs the hierarchical arrangement of subqueries of Abe and STBE. Similar to STBE, TBE handles set- and simple-valued attributes. In addition, to handle time, TBE is capable of manipulating triplet- and set-triplet-valued attributes. The underlying data model used in TBE is an extended relational data model in which nonfirst normal form relations and attribute time stamping (in contrast to tuple time stamping) are used. A triplet is a 3-tuple whose components are the lower and upper bounds of a time interval and a value valid over the interval. A triplet is used as a time- stamped value of a time-dependent attribute. Set-valued time-dependent attributes are modeled by sets of triplets. To process TBE queries and to define a historical relational algebra (HRA), standard operators of the relational algebra and the pack/unpack operators of [21] are augmented by triplet-decomposition, triplet- formation, slice, and drop-time operators. Methodologies for translating TBE queries into HRA expressions and for constructing their parse trees are presented. [ABSTRACT FROM AUTHOR]

Motivation: Discovering the transcriptional regulatory architecture of the metabolism has been an important topic to understand the implications of transcriptional fluctuations on metabolism. The reporter algorithm (RA) was proposed to determine the hot spots in metabolic networks, around which transcriptional regulation is focused owing to a disease or a genetic perturbation. Using a z-score-based scoring scheme, RA calculates the average statistical change in the expression levels of genes that are neighbors to a target metabolite in the metabolic network. The RA approach has been used in numerous studies to analyze cellular responses to the downstream genetic changes. In this article, we propose a mutual information-based multivariate reporter algorithm (MIRA) with the goal of eliminating the following problems in detecting reporter metabolites: (i) conventional statistical methods suffer from small sample sizes, (ii) as z-score ranges from minus to plus infinity, calculating average scores can lead to canceling out opposite effects and (iii) analyzing genes one by one, then aggregating results can lead to information loss. MIRA is a multivariate and combinatorial algorithm that calculates the aggregate transcriptional response around a metabolite using mutual information. We show that MIRA’s results are biologically sound, empirically significant and more reliable than RA.Results: We apply MIRA to gene expression analysis of six knockout strains of Escherichia coli and show that MIRA captures the underlying metabolic dynamics of the switch from aerobic to anaerobic respiration. We also apply MIRA to an Autism Spectrum Disorder gene expression dataset. Results indicate that MIRA reports metabolites that highly overlap with recently found metabolic biomarkers in the autism literature. Overall, MIRA is a promising algorithm for detecting metabolic drug targets and understanding the relation between gene expression and metabolic activity.Availability and implementation: The code is implemented in C# language using .NET framework. Project is available upon request.Contact: cicek@cs.cmu.eduSupplementary information: Supplementary data are available at Bioinformatics online [ABSTRACT FROM PUBLISHER]

Metabolic networks have become one of the centers of attention in life sciences research with the advancements in the metabolomics field. A vast array of studies analyzes metabolites and their interrelations to seek explanations for various biological questions, and numerous genome-scale metabolic networks have been assembled to serve for this purpose. The increasing focus on this topic comes with the need for software systems that store, query, browse, analyze and visualize metabolic networks. PathCase Metabolomics Analysis Workbench (PathCaseMAW) is built, released and runs on a manually created generic mammalian metabolic network. The PathCaseMAW system provides a database-enabled framework and Web-based computational tools for browsing, querying, analyzing and visualizing stored metabolic networks. PathCaseMAW editor, with its user-friendly interface, can be used to create a new metabolic network and/or update an existing metabolic network. The network can also be created from an existing genome-scale reconstructed network using the PathCaseMAW SBML parser. The metabolic network can be accessed through a Web interface or an iPad application. For metabolomics analysis, steady-state metabolic network dynamics analysis (SMDA) algorithm is implemented and integrated with the system. SMDA tool is accessible through both the Web-based interface and the iPad application for metabolomics analysis based on a metabolic profile. PathCaseMAW is a comprehensive system with various data input and data access subsystems. It is easy to work with by design, and is a promising tool for metabolomics research and for educational purposes. [ABSTRACT FROM AUTHOR]

Background: There are multiple representation formats for Systems Biology computational models, and the Systems Biology Markup Language (SBML) is one of the most widely used. SBML is used to capture, store, and distribute computational models by Systems Biology data sources (e.g., the BioModels Database) and researchers. Therefore, there is a need for all-in-one web-based solutions that support advance SBML functionalities such as uploading, editing, composing, visualizing, simulating, querying, and browsing computational models. Results: We present the design and implementation of the Model Composition Tool (Interface) within the PathCase- SB (PathCase Systems Biology) web portal. The tool helps users compose systems biology models to facilitate the complex process of merging systems biology models. We also present three tools that support the model composition tool, namely, (1) Model Simulation Interface that generates a visual plot of the simulation according to user's input, (2) iModel Tool as a platform for users to upload their own models to compose, and (3) SimCom Tool that provides a side by side comparison of models being composed in the same pathway. Finally, we provide a web site that hosts BioModels Database models and a separate web site that hosts SBML Test Suite models. Conclusions: Model composition tool (and the other three tools) can be used with little or no knowledge of the SBML document structure. For this reason, students or anyone who wants to learn about systems biology will benefit from the described functionalities. SBML Test Suite models will be a nice starting point for beginners. And, for more advanced purposes, users will able to access and employ models of the BioModels Database as well. [ABSTRACT FROM AUTHOR]

Metabolomics is a relatively new ''omics'' platform, which analyzes a discrete set of metabolites detected in bio-fluids or tissue samples of organisms. It has been used in a diverse array of studies to detect biomarkers and to determine activity rates for pathways based on changes due to disease or drugs. Recent improvements in analytical methodology and large sample throughput allow for creation of large datasets of metabolites that reflect changes in metabolic dynamics due to disease or a perturbation in the metabolic network. However, current methods of comprehensive analyses of large metabolic datasets (metabolomics) are limited, unlike other ''omics'' approaches where complex techniques for analyzing coexpression/coregulation of multiple variables are applied. This paper discusses the shortcomings of current metabolomics data analysis techniques, and proposes a new multivariate technique (ADEMA) based on mutual information to identify expected metabolite level changes with respect to a specific condition. We show that ADEMA better predicts De Novo Lipogenesis pathway metabolite level changes in samples with Cystic Fibrosis (CF) than prediction based on the significance of individual metabolite level changes. We also applied ADEMA's classification scheme on three different cohorts of CF and wildtype mice. ADEMA was able to predict whether an unknown mouse has a CF or a wildtype genotype with 1.0, 0.84, and 0.9 accuracy for each respective dataset. ADEMA results had up to 31% higher accuracy as compared to other classification algorithms. In conclusion, ADEMA advances the state-of-the-art in metabolomics analysis, by providing accurate and interpretable classification results. [ABSTRACT FROM AUTHOR]

With the recent advances in experimental technologies, such as gas chromatography and mass spectrometry, the number of metabolites that can be measured in biofluids of individuals has markedly increased. Given a set of such measurements, a very common task encountered by biologists is to identify the metabolic mechanisms that lead to changes in the concentrations of given metabolites and interpret the metabolic consequences of the observed changes in terms of physiological problems, nutritional deficiencies, or diseases. In this paper, we present the steady-state metabolic network dynamics analysis (SMDA) approach in detail, together with its application in a cystic fibrosis study. We also present a computational performance evaluation of the SMDA tool against a mammalian metabolic network database. The query output space of the SMDA tool is exponentially large in the number of reactions of the network. However, (i) larger numbers of observations exponentially reduce the output size, and (ii) exploratory search and browsing of the query output space is provided to allow users to search for what they are looking for. [ABSTRACT FROM AUTHOR]

Background: Integration of metabolic pathways resources and metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation of metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis are desirable and intellectually challenging computational tasks. Results: PathCase Systems Biology (PathCase-SB) is built and released. This paper describes PathCase-SB user interfaces developed to date. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate systems biology models data and metabolic network data of selected biological data sources on the web (currently, BioModels Database and KEGG, respectively), and to provide more powerful and/or new capabilities via the new web-based integrative framework. Conclusions: Each of the current four PathCase-SB interfaces, namely, Browser, Visualization, Querying, and Simulation interfaces, have expanded and new capabilities as compared with the original data sources. PathCase-SB is already available on the web and being used by researchers across the globe. [ABSTRACT FROM AUTHOR]

Contact: tekin@case.edu [ABSTRACT FROM PUBLISHER]