Publications by authors named "Alexander Mazein"

27 Publications

  • Page 1 of 1

Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling.

Brief Bioinform 2021 Apr 8. Epub 2021 Apr 8.

Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.
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http://dx.doi.org/10.1093/bib/bbab103DOI Listing
April 2021

SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks.

Brief Bioinform 2021 Mar 24. Epub 2021 Mar 24.

European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France.

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.
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http://dx.doi.org/10.1093/bib/bbab049DOI Listing
March 2021

Newt: a comprehensive web-based tool for viewing, constructing, and analyzing biological maps.

Bioinformatics 2020 Oct 3. Epub 2020 Oct 3.

i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara, Turkey.

Motivation: Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries.

Results: We developed a web-based tool named Newt for viewing, constructing, and analyzing biological maps in standard formats such as SBGN, SBML, and SIF.

Availability: Newt's source code is publicly available on GitHub and freely distributed under the GNU LGPL. Ample documentation on Newt can be found on http://newteditor.org and on YouTube.
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http://dx.doi.org/10.1093/bioinformatics/btaa850DOI Listing
October 2020

Systems biology graphical notation markup language (SBGNML) version 0.3.

J Integr Bioinform 2020 Jun 22;17(2-3). Epub 2020 Jun 22.

cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.

This document defines Version 0.3 Markup Language (ML) support for the Systems Biology Graphical Notation (SBGN), a set of three complementary visual languages developed for biochemists, modelers, and computer scientists. SBGN aims at representing networks of biochemical interactions in a standard, unambiguous way to foster efficient and accurate representation, visualization, storage, exchange, and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. SBGN is defined neutrally to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML, the eXtensible Markup Language. The notable changes from the previous version include the addition of attributes for better specify metadata about maps, as well as support for multiple maps, sub-maps, colors, and annotations. These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g., BioPAX and SBML) and between tools supporting SBGN (e. g., CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at http://sbgn.org. With this effort, we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.
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http://dx.doi.org/10.1515/jib-2020-0016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756621PMC
June 2020

RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis.

Database (Oxford) 2020 01;2020

Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France.

Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.
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http://dx.doi.org/10.1093/database/baaa017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170216PMC
January 2020

cd2sbgnml: bidirectional conversion between CellDesigner and SBGN formats.

Bioinformatics 2020 04;36(8):2620-2622

Institut National de la Santé et de la Recherche Médicale (INSERM), U900, F-75005 Paris, France.

Motivation: CellDesigner is a well-established biological map editor used in many large-scale scientific efforts. However, the interoperability between the Systems Biology Graphical Notation (SBGN) Markup Language (SBGN-ML) and the CellDesigner's proprietary Systems Biology Markup Language (SBML) extension formats remains a challenge due to the proprietary extensions used in CellDesigner files.

Results: We introduce a library named cd2sbgnml and an associated web service for bidirectional conversion between CellDesigner's proprietary SBML extension and SBGN-ML formats. We discuss the functionality of the cd2sbgnml converter, which was successfully used for the translation of comprehensive large-scale diagrams such as the RECON Human Metabolic network and the complete Atlas of Cancer Signalling Network, from the CellDesigner file format into SBGN-ML.

Availability And Implementation: The cd2sbgnml conversion library and the web service were developed in Java, and distributed under the GNU Lesser General Public License v3.0. The sources along with a set of examples are available on GitHub (https://github.com/sbgn/cd2sbgnml and https://github.com/sbgn/cd2sbgnml-webservice, respectively).

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz969DOI Listing
April 2020

Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0.

J Integr Bioinform 2019 Jun 13;16(2). Epub 2019 Jun 13.

cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA.

The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).
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http://dx.doi.org/10.1515/jib-2019-0022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798820PMC
June 2019

Human-like layout algorithms for signalling hypergraphs: outlining requirements.

Brief Bioinform 2018 Oct 5. Epub 2018 Oct 5.

European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon,50 Avenue Tony Garnier, Lyon, France.

The use of signalling pathway hypergraphs represented as process description diagrams is steadily becoming more pervasive in the field of biology. This makes ever more evident the necessity for an effective automated layout that can replicate high-quality manually drawn diagrams. The complexity and idiosyncrasies of these diagrams, as well as the specific tasks the end users perform with them, mean that a layout must meet many requirements beyond the simple metrics used in existing automated computational approaches. In this paper we outline these requirements, examine existing ones and describe new ones. We demonstrate state-of-the-art layout techniques enhanced with novel functionalities to meet part of the requirements. For comparatively small signalling pathways our enhanced algorithm provides results close to manually drawn layouts. In addition, we suggest technical approaches that may be suited for fulfilling the identified requirements currently not covered.
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http://dx.doi.org/10.1093/bib/bby099DOI Listing
October 2018

AsthmaMap: An expert-driven computational representation of disease mechanisms.

Clin Exp Allergy 2018 08;48(8):916-918

European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, Lyon, France.

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http://dx.doi.org/10.1111/cea.13211DOI Listing
August 2018

Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms.

NPJ Syst Biol Appl 2018 2;4:21. Epub 2018 Jun 2.

1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France.

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.
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http://dx.doi.org/10.1038/s41540-018-0059-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984630PMC
June 2018

A computational framework for complex disease stratification from multiple large-scale datasets.

BMC Syst Biol 2018 05 29;12(1):60. Epub 2018 May 29.

Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK.

Background: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states.

Methods: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification.

Results: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes.

Conclusions: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
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http://dx.doi.org/10.1186/s12918-018-0556-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975674PMC
May 2018

Community-driven roadmap for integrated disease maps.

Brief Bioinform 2019 03;20(2):659-670

Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.

The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.
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http://dx.doi.org/10.1093/bib/bby024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556900PMC
March 2019

MINERVA-a platform for visualization and curation of molecular interaction networks.

NPJ Syst Biol Appl 2016 22;2:16020. Epub 2016 Sep 22.

Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg.

Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories.
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http://dx.doi.org/10.1038/npjsba.2016.20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516855PMC
September 2016

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks.

Bioinformatics 2017 04;33(7):1096-1098

European Institute for Systems Biology and Medicine (EISBM), CIRI CNRS UMR 5308, CNRS-ENS-UCBL-INSERM, Lyon, France.

Summary: The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing.

Availability And Implementation: The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/ . The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework .

Contact: ibalaur@eisbm.org.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btw731DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408918PMC
April 2017

STON: exploring biological pathways using the SBGN standard and graph databases.

BMC Bioinformatics 2016 Dec 5;17(1):494. Epub 2016 Dec 5.

European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France.

Background: When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks.

Results: We present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database.

Conclusion: STON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.
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http://dx.doi.org/10.1186/s12859-016-1394-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5139139PMC
December 2016

EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer.

J Comput Biol 2017 Oct 14;24(10):969-980. Epub 2016 Sep 14.

1 European Institute for Systems Biology and Medicine (EISBM) , CIRI UMR CNRS 5308, CNRS-ENS-UCBL-INSERM, Université Claude Bernard, Lyon, France .

The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis. We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.
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http://dx.doi.org/10.1089/cmb.2016.0095DOI Listing
October 2017

Representing and querying disease networks using graph databases.

BioData Min 2016 25;9:23. Epub 2016 Jul 25.

European Institute for Systems Biology and Medicine (EISBM), CIRI UMR CNRS 5308, CNRS-ENS-UCBL-INSERM, Lyon, France.

Background: Systems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data.

Results: We show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes.

Conclusions: Our study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.
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http://dx.doi.org/10.1186/s13040-016-0102-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4960687PMC
July 2016

Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness.

Methods Mol Biol 2016 ;1386:43-60

European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France.

Recent advances in genomics have led to the rapid and relatively inexpensive collection of patient molecular data including multiple types of omics data. The integration of these data with clinical measurements has the potential to impact on our understanding of the molecular basis of disease and on disease management. Systems medicine is an approach to understanding disease through an integration of large patient datasets. It offers the possibility for personalized strategies for healthcare through the development of a new taxonomy of disease. Advanced computing will be an important component in effectively implementing systems medicine. In this chapter we describe three computational challenges associated with systems medicine: disease subtype discovery using integrated datasets, obtaining a mechanistic understanding of disease, and the development of an informatics platform for the mining, analysis, and visualization of data emerging from translational medicine studies.
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http://dx.doi.org/10.1007/978-1-4939-3283-2_3DOI Listing
October 2016

A comprehensive machine-readable view of the mammalian cholesterol biosynthesis pathway.

Biochem Pharmacol 2013 Jul 10;86(1):56-66. Epub 2013 Apr 10.

Division of Pathway Medicine, University of Edinburgh, Chancellor's Building, Little France Crescent, Edinburgh EH16 4SB, Scotland, UK.

Cholesterol biosynthesis serves as a central metabolic hub for numerous biological processes in health and disease. A detailed, integrative single-view description of how the cholesterol pathway is structured and how it interacts with other pathway systems is lacking in the existing literature. Here we provide a systematic review of the existing literature and present a detailed pathway diagram that describes the cholesterol biosynthesis pathway (the mevalonate, the Kandutch-Russell and the Bloch pathway) and shunt pathway that leads to 24(S),25-epoxycholesterol synthesis. The diagram has been produced using the Systems Biology Graphical Notation (SBGN) and is available in the SBGN-ML format, a human readable and machine semantically parsable open community file format.
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http://dx.doi.org/10.1016/j.bcp.2013.03.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912678PMC
July 2013

A community-driven global reconstruction of human metabolism.

Nat Biotechnol 2013 May 3;31(5):419-25. Epub 2013 Mar 3.

Center for Systems Biology, University of Iceland, Reykjavik, Iceland.

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
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http://dx.doi.org/10.1038/nbt.2488DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3856361PMC
May 2013

A model of flux regulation in the cholesterol biosynthesis pathway: Immune mediated graduated flux reduction versus statin-like led stepped flux reduction.

Biochimie 2013 Mar 1;95(3):613-21. Epub 2012 Jun 1.

Division of Pathway Medicine, University of Edinburgh Medical School, Chancellor's Building, 49 Little France Crescent, Edinburgh, Scotland, United Kingdom.

The cholesterol biosynthesis pathway has recently been shown to play an important role in the innate immune response to viral infection with host protection occurring through a coordinate down regulation of the enzymes catalysing each metabolic step. In contrast, statin based drugs, which form the principle pharmaceutical agents for decreasing the activity of this pathway, target a single enzyme. Here, we build an ordinary differential equation model of the cholesterol biosynthesis pathway in order to investigate how the two regulatory strategies impact upon the behaviour of the pathway. We employ a modest set of assumptions: that the pathway operates away from saturation, that each metabolite is involved in multiple cellular interactions and that mRNA levels reflect enzyme concentrations. Using data taken from primary bone marrow derived macrophage cells infected with murine cytomegalovirus or treated with IFNγ, we show that, under these assumptions, coordinate down-regulation of enzyme activity imparts a graduated reduction in flux along the pathway. In contrast, modelling a statin-like treatment that achieves the same degree of down-regulation in cholesterol production, we show that this delivers a step change in flux along the pathway. The graduated reduction mediated by physiological coordinate regulation of multiple enzymes supports a mechanism that allows a greater level of specificity, altering cholesterol levels with less impact upon interactions branching from the pathway, than pharmacological step reductions. We argue that coordinate regulation is likely to show a long-term evolutionary advantage over single enzyme regulation. Finally, the results from our models have implications for future pharmaceutical therapies intended to target cholesterol production with greater specificity and fewer off target effects, suggesting that this can be achieved by mimicking the coordinated down-regulation observed in immunological responses.
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http://dx.doi.org/10.1016/j.biochi.2012.05.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3585962PMC
March 2013

The Edinburgh human metabolic network reconstruction and its functional analysis.

Mol Syst Biol 2007 18;3:135. Epub 2007 Sep 18.

Computational Systems Biology, School of Informatics, The University of Edinburgh, Edinburgh, UK.

A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts.
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http://dx.doi.org/10.1038/msb4100177DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2013923PMC
October 2007