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Ozden F, Siper MC, Acarsoy N, Elmas T, Marty B, Qi X, Cicek AE. DORMAN: Database of Reconstructed MetAbolic Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1474-1480. [PMID: 31581093 DOI: 10.1109/tcbb.2019.2944905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Genome-scale reconstructed metabolic networks have provided an organism specific understanding of cellular processes and their relations to phenotype. As they are deemed essential to study metabolism, the number of organisms with reconstructed metabolic networks continues to increase. This everlasting research interest lead to the development of online systems/repositories that store existing reconstructions and enable new model generation, integration, and constraint-based analyses. While features that support model reconstruction are widely available, current systems lack the means to help users who are interested in analyzing the topology of the reconstructed networks. Here, we present the Database of Reconstructed Metabolic Networks - DORMAN. DORMAN is a centralized online database that stores SBML-based reconstructed metabolic networks published in the literature, and provides web-based computational tools for visualizing and analyzing the model topology. Novel features of DORMAN are (i) interactive visualization interface that allows rendering of the complete network as well as editing and exporting the model, (ii) hierarchical navigation that provides efficient access to connected entities in the model, (iii) built-in query interface that allow posing topological queries, and finally, and (iv) model comparison tool that enables comparing models with different nomenclatures, using approximate string matching. DORMAN is online and freely accessible at http://ciceklab.cs.bilkent.edu.tr/dorman.
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Cicek AE, Qi X, Cakmak A, Johnson SR, Han X, Alshalwi S, Ozsoyoglu ZM, Ozsoyoglu G. An online system for metabolic network analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau091. [PMID: 25267793 PMCID: PMC4178370 DOI: 10.1093/database/bau091] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
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. Database URL: http://nashua.case.edu/PathwaysMAW/Web
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Affiliation(s)
- Abdullah Ercument Cicek
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Xinjian Qi
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Ali Cakmak
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Stephen R Johnson
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Xu Han
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Sami Alshalwi
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Zehra Meral Ozsoyoglu
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Gultekin Ozsoyoglu
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
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Pornputtapong N, Wanichthanarak K, Nilsson A, Nookaew I, Nielsen J. A dedicated database system for handling multi-level data in systems biology. SOURCE CODE FOR BIOLOGY AND MEDICINE 2014; 9:17. [PMID: 25053973 PMCID: PMC4106218 DOI: 10.1186/1751-0473-9-17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Accepted: 07/01/2014] [Indexed: 11/10/2022]
Abstract
Background Advances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging. Methods To overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase. Results and conclusion In this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.
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Affiliation(s)
- Natapol Pornputtapong
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Kwanjeera Wanichthanarak
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Avlant Nilsson
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Intawat Nookaew
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Jens Nielsen
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
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Coskun SA, Cicek AE, Lai N, Dash RK, Ozsoyoglu ZM, Ozsoyoglu G. An online model composition tool for system biology models. BMC SYSTEMS BIOLOGY 2013; 7:88. [PMID: 24006914 PMCID: PMC3846440 DOI: 10.1186/1752-0509-7-88] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 08/21/2013] [Indexed: 11/21/2022]
Abstract
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.
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Affiliation(s)
- Sarp A Coskun
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.
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Cicek AE, Bederman I, Henderson L, Drumm ML, Ozsoyoglu G. ADEMA: an algorithm to determine expected metabolite level alterations using mutual information. PLoS Comput Biol 2013; 9:e1002859. [PMID: 23341761 PMCID: PMC3547803 DOI: 10.1371/journal.pcbi.1002859] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2012] [Accepted: 10/23/2012] [Indexed: 01/07/2023] Open
Abstract
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. Metabolomics is an experimental approach that analyzes differences in metabolite levels detected in experimental samples. It has been used in the literature to understand the changes in metabolism with respect to diseases or drugs. Unlike transcriptomics or proteomics, which analyze gene and protein expression levels respectively, the techniques that consider co-regulation of multiple metabolites are quite limited. In this paper, we propose a novel technique, called ADEMA, which computes the expected level changes for each metabolite with respect to a given condition. ADEMA considers multiple metabolites at the same time and is mutual information (MI)-based. We show that ADEMA predicts metabolite level changes for young mice with Cystic Fibrosis (CF) better than significance testing that considers one metabolite at a time. Using three different datasets that contain CF and wild-type (WT) mice, we show that ADEMA can classify an individual as being CF or WT based on the metabolic profiles (with 1.0, 0.84, and 0.9 accuracy, respectively). Compared to other well-known classification algorithms, ADEMA's accuracy is higher by up to 31%.
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Affiliation(s)
- A Ercument Cicek
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, USA.
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iPathCase(KEGG): An iPad interface for KEGG metabolic pathways. Health Inf Sci Syst 2013; 1:4. [PMID: 25825656 PMCID: PMC4336120 DOI: 10.1186/2047-2501-1-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2012] [Accepted: 09/04/2012] [Indexed: 11/14/2022] Open
Abstract
Background Kyoto Encyclopedia of Genes and Genomes (KEGG) is an online and integrated molecular database for several organisms. KEGG has been a highly useful site, helping domain scientists understand, research, study, and teach metabolisms by linking sequenced genomes to higher level systematic functions. KEGG databases are accessible through the web pages of the system, but the capabilities of the web interface are limited. Third party systems have been built over the KEGG data to provide extensive functionalities. However, there have been no attempts towards providing a tablet interface for KEGG data. Recognizing the rise of mobile technologies and the importance of tablets in education, this paper presents the design and implementation of iPathCaseKEGG, an iPad interface for KEGG data, which is empowered with multiple browsing and visualization capabilities. Results iPathCaseKEGG has been implemented and is available, free of charge, in the Apple App Store (locatable by searching for “Pathcase” in the app store). The application provides browsing and interactive visualization functionalities on the KEGG data. Users can pick pathways, visualize them, and see detail pages of reactions and molecules using the multi-touch interface of iPad. Conclusions iPathCaseKEGG provides a mobile interface to access KEGG data. Interactive visualization and browsing functionalities let users to interact with the data in multiple ways. As the importance of tablets and their usage in research education continue to rise, we think iPathCaseKEGG will be a useful tool for life science instructors and researchers.
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Coskun SA, Qi X, Cakmak A, Cheng E, Cicek AE, Yang L, Jadeja R, Dash RK, Lai N, Ozsoyoglu G, Ozsoyoglu ZM. PathCase-SB: integrating data sources and providing tools for systems biology research. BMC SYSTEMS BIOLOGY 2012; 6:67. [PMID: 22697505 PMCID: PMC3410775 DOI: 10.1186/1752-0509-6-67] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2011] [Accepted: 06/14/2012] [Indexed: 11/10/2022]
Abstract
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.
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Affiliation(s)
- Sarp A Coskun
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Xinjian Qi
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Ali Cakmak
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - En Cheng
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - A Ercument Cicek
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Lei Yang
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Rishiraj Jadeja
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Ranjan K Dash
- Department of Physiology, Medical College of Wisconsin, Milwaukee, USA
| | - Nicola Lai
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Pediatrics, Case Western Reserve University, Cleveland, USA
| | - Gultekin Ozsoyoglu
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Zehra Meral Ozsoyoglu
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
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