To date, proposed in vitro assays are mainly based on single biomarkers, which so far have not been able to classify and stratify chemicals into subgroups, related to risk or potency.Due to the recent European legislations posing a ban of animal tests for safety assessment within the cosmetic industry, development of Recently, we presented the Genomic Allergen Rapid Detection (GARD) assay for assessment of chemical sensitizers. In this paper, we show how the genome wide readout of GARD can be expanded and used to identify differentially regulated pathways relating to individual chemical sensitizers. In this study, we investigated the mechanisms of action of a range of skin sensitizers through pathway identification, pathway classification and transcription factor analysis and related this to the reactive mechanisms and potency of the sensitizing agents.By transcriptional profiling of chemically stimulated MUTZ-3 cells, 33 canonical pathways intimately involved in sensitization to chemical substances were identified. The results showed that metabolic processes, cell cycling and oxidative stress responses are the key events activated during skin sensitization, and that these functions are engaged differently depending on the reactivity mechanisms of the sensitizing agent. Furthermore, the results indicate that the chemical reactivity groups seem to gradually engage more pathways and more molecules in each pathway with increasing sensitizing potency of the chemical used for stimulation. Also, a switch in gene regulation from up to down regulation, with increasing potency, was seen both in genes involved in metabolic functions and cell cycling. These observed pathway patterns were clearly reflected in the regulatory elements identified to drive these processes, where 33 regulatory elements have been proposed for further analysis.in vitro, by the identification of key cellular events, such as metabolic and cell cycling pathways.This study demonstrates that functional analysis of biomarkers identified from our genomics study of human MUTZ-3 cells can be used to assess sensitizing potency of chemicals  Allergic contact dermatitis (ACD) is an inflammatory skin disease characterized by eczema and recurrent episodes of itching . Hundredin vitro tests urgent.The Local Lymph Node Assay (LLNA) , which iin vitro test, Genomic Allergen Rapid Detection (GARD), based on MUTZ-3 cells .The transcriptome of MUTZ-3 cells stimulated with skin sensitizers and non-sensitizers was analyzed, using DNA microarray technology, with triplicate samples of 40 individual stimulations . Due to A Principal Component Analysis (PCA) analysis of the complete expression data set visualized that the studied chemical reactivities were indeed quite distinct, as shown in Figure ® for further analysis.The apparent differences in transcriptional profiles among different reactivity groups led us to further explore the differential engagement of signaling and metabolic pathways and the upstream regulators initiating such pathways. To this end, each chemical reactivity group was compared to the set of non-sensitizers in an ANOVA, yielding 9 lists of differentially regulated genes. These 9 gene lists were subsequently imported into IPA®, a number of regulated pathways were identified. In total, 387 regulated pathways were detected in the 9 different chemical reactivity groups. By applying a cut-off of three times, the default IPA® significance  ≤ 3.9), 33 pathways were selected for a comprehensive investigation. The differential engagement patterns of the 33 pathways, related to sensitizer reactivity, are summarized in Figure ® input gene lists were identified. When studying the 33 selected pathways, it is evident that increasing potency correlates with enhanced pathway involvement in terms of number of regulated genes, as well as a shift towards down regulation of the pathways components.Using the Core Analysis of the pathway software IPAFinally, to identify the drivers in relation to potency, analysis of upstream regulators was performed. In Figure The overall impression is that more genes, associated with various pathways, are down-regulated than up-regulated in MUTZ3 cells stimulated with sensitizers. The sensitizers act mainly on cell cycling, cellular stress and injury, and metabolic pathways, in that order. Relevant identified sub functions are apoptosis, cellular growth and development, cellular immune response, intracellular and second messenger signaling, and metabolism and biosynthesis of glycan, lipids, vitamins and cofactors and nucleotides. The general trend is that both metabolic and cell cycle associated pathways are engaged gradually and in correlation with potency. Oxidative stress related pathways are another group of highly engaged pathways. However, these do not correlate with potency in the same manner as the other pathways do. For example, the NRF2-mediated oxidative stress response pathway shows a reversed pattern compared to the other pathways presented in this study. The overall impression of the NRF2 pathway is that phase I and II enzymes, as well as antioxidant proteins, are up-regulated, for an example see Additional file ® software.A few metabolic pathways are generally regulated. These are the nucleotide and lipid metabolism pathways. Chemicals belonging to the nucleophilic aromatic substitution (NAR) and coordination bonds (Koor) group down regulate nucleotide metabolism the most. As the current chemical selection includes only highly potent sensitizers from the Koor and NAR group, we cannot extrapolate these findings to less potent ones. Also, the NAR group constitutes a somewhat odd case, since this is a small group of highly experimental allergens with limited clinical relevance. The other metabolic pathways are regulated in a more mixed fashion, but again, amino acid metabolism is gradually down-regulated with increasing chemical potency. The Glutathione metabolism pathway is interesting in that it goes from clear up regulation by the less potent chemicals to clear down regulation by the NAR group. While the division of pathways into signaling or metabolic pathways is a somewhat arbitrary matter, we still believe it reflects the major cellular functions quite well. We have followed the KEGG standard  at the tlet-7 microRNAs.The pathways of cell cycle regulation are also gradually down-regulated with increasing potency. This could indicate that these chemicals induce cell cycle arrest. As discussed later this seems to be connected to activation state of the up-stream regulators identified in the analyses, especially the The apoptosis pathway Aryl hydrocarbon receptor (AHR) signaling is also among the frequently regulated. As seen from Figure Finally, lipid metabolism is generally affected by all the chemical groups. For instance, addition/elimination to carboxylic acid derivatives (Add/El) clearly down regulates Sphingolipid metabolism and the remaining chemical groups show a mixed or up-regulated pattern.From the pathway analysis it is clear that various chemical reactivity groups stimulate pathway activation or inhibition differentially. This selective effect is e.g. observed for chemicals of the Michael addition or 1,4-Addition to a/b unsaturated carbonyl (MA) group, which up regulate several of the signaling pathways, as well as chemicals of the addition/elimination to carboxylic acid derivatives (Add/El). The groups prehapten  + prohapten  and Add/El (addition/elimination to carboxylic acid derivatives) both show a very limited activity in most pathways studied.Considering the activity levels and changes of transcription observed and summarized in Figures It should be noted that all samples in this study are collected at single concentrations and single time-points. Most likely, the observed differences between chemical groups depend in some instances on a varying reactivity speed. As an example, investigations of transcription factors, connecting directly to selected cell cycle pathways, show that the activity level of the pathway molecules can depend on temporal factors. Those chemicals that did not show a great deal of activity in the pathway did indeed have a relatively higher activity level among the upstream regulators of these pathways. Thus, there may be pathways that are more consistently regulated than is observed in this study, although such similarities may be masked by varying reactivity of the chemical sensitizers.® software using molecules predicted to be up or down regulated based on analysis of the expression patterns of the nine chemical reactivity groups studied. The molecular interactions were studied in detail using the MetaCore™ software for pathways analyses focusing on uni- or bidirectional binding and regulation actions of the included molecules. All interactions shown in Figure let-7 . The de-regulation of let-7 miRNAs has previously been described in association to cancer, where the let-7 family members act as tumor suppressors acting e.g. through MYC and E2f2 [let-7 and specifically the member let-7a-5p was predicted to be significantly activated in six out of the nine groups, with very high activation scores (z-score 2.6 to 6.6) and regulated with borderline detection in the three remaining groups . This finding fits well with the known down regulation of let-7 by MYC, through binding of MYC to the transcription start site of the let-7 g pri-miRNA[let-7 is known to be active in cell cycle regulation, DNA replication, cell division, apoptosis and development [let-7 have been observed, giving rise to loss of cell cycle control. In our case, down regulation of cell cycle functions is evident for the majority of the chemical reactivity groups studied, while let-7 was predicted to be up regulated. In a review by Hou et al. [let-7 RNAs were described as heavily down regulated by environmental chemicals, cigarette smoke and up-regulated by others, such as hexahydro-1,3,5-triazine (RDX). let-7 up-regulation has also been demonstrated in skin samples from psoriasis patients compared to healthy controls [let-7a-5p and mir-483-3p. We propose that these microRNAs may have similar functions, as described above, in skin, when chemically challenged. For details about molecular interactions found see Additional file Next, the significantly predicted upstream regulators were investigated. Figure and E2f2 . Both MYpri-miRNA. let-7 ielopment . In the u et al. , let-7 Rcontrols , suggestlet-7 is known to inhibit a number of molecules: ABCC10, BACH1, BLIMP1 (PRDI-BF1), Bax, CCR7, Dicer, EZH2, HMGA2, HMGI/Y, HOXA9, IL-6, NAV1.9, PGHD, SOCS1, UHRF2, and MYC that are all found regulated in the data set. BCL2L1 and DICER1 were found to be up-regulated while all other were down-regulated. Among the transcription regulators MYC and let-7 miRNAs are known to bind and interact bi-directionally. Furthermore, let-7 is regulated by a number of other molecules, several of which were seen regulated in the present analysis, namely PPARA, DROSHA, XPO5, and KDM5B. Finally, let-7 binds to the predicted activated transcription regulator TGFB1.® database. Many of them have known relevant functions in skin and skin disorders, including CDKN1A, CDKN2A, CCND1, CSF2, MYC, PTEN, SMARCB1, TGM2, TP53, E2F2, Retinoic acid, NFkB, TGFB1, TNF-α and let-7. Of particular interest, CSF2, NFkB, TGFB1, TNF-α and TP53 have been associated with hypersensitivity and allergy reactions. BRD4, CCND1, CDKN2A, E2F1, FOXM1, HGF, KDM5B. MYB, MYC, NFkB2, PTEN, RB1, RBL1/2, SMARCB1, TGFB1, TP53, Butyric acid and let-7 are all regulators of the cell cycle. In addition, a majority of these molecules are associated with various forms of cancer, as many carcinomas act through disruption of the cell cycle. Of interest, many of the predicted elements, such as CDKN2A, E2F1, E2F2, MYC, SMARCB1, TP53, TGM2, TNF-α, Retinoic acid, NFkB and TGFB1 are known regulators or members of the Aryl Hydrocarbon Receptor signaling pathway.The ascribed biological functions of the upstream regulators were extracted from the IPAThe NFkB complex was also predicted to be activated in this study. This extensively studied nucleic protein has a wide range of functions. Of interest to this study is mainly its involvement in pathways of Xenobiotics Metabolism, Production of Nitric Oxide and Reactive Oxygen Species in Macrophages and Toll like receptor signaling. NFkB showed a heterogeneous pattern, with up regulation in four groups  and down regulation in one (ProH + PreH). This may be a relevant finding given the role this molecule plays in epithelial cells where it on one hand maintains immunological homeostasis and on the other induces inflammatory processes . NFkB haActivation of MUTZ-3 cells with skin sensitizers resulted in regulation of a number of previously known, as well as novel, canonical pathways. The 33 most significantly regulated pathways were studied in detail. After stratification of sensitizing chemicals into chemical reactivity groups, it was evident that pathway engagement and the number of regulated molecules within such pathways were linked to the sensitizing potency of the stimulatory agent. For instance, cell cycle associated pathways were engaged gradually in correlation with the sensitizing potency of the chemicals used for cell stimulations. These observations can be utilized when designing tools for assessment of sensitizing potency of chemicals in predictive assays. Currently, the primary prediction call obtained from GARD is binary, i.e. compounds are classified as sensitizers or non-sensitizers. However, this readout can now for the first time be complemented with a potency call based on mechanistic information obtained through pathway analysis. While implementation of such classifications are not straight-forward, similar attempts have been done within cancer research, where pathway activity has successfully been associated with subtypes of cancer and progression of disease -34.Oxidative stress related pathways are also in the group of highly engaged pathways. These do not correlate with potency to the same extent that cell cycle pathways do. For example, the NRF2-mediated oxidative stress response pathway shows a higher engagement by less potent chemical groups compared to more potent ones. This may be a consideration when studying more clinically relevant allergens, as these are rarely in the strong to extreme but rather in the week to medium range of potency.Pathways regulating metabolism and biosynthesis processes constitute the third large group of canonical pathways identified to be regulated in skin sensitization. These pathways are regulated to a lesser extent but still show the same pattern as seen in the cell cycling pathways.No canonical pathway was unanimously regulated within each data set. The importance of this fact becomes apparent when considering the design of various proposed tests for animal-free assessment of sensitization, which today are mainly based on analysis of single biomarkers. It is evident that excessively simplistic tests lack the power to accurately assess compounds as sensitizers or non-sensitizers, if the target chemical domain is wide. Recognition of the specific subtype of chemicals to be tested based on reactive mechanism or otherwise, will be crucial in order to train and configure predictive models accurately.In the present transcriptional profiling study, we have shown that skin sensitizers trigger a large number of signaling and metabolic pathways in MUTZ-3 cells. Among these, we found examples of well-known pathways, as well as pathways previously not described in relation to chemical sensitization. Importantly, a majority of the pathways described show a clear correlation with both chemical reactivity and sensitizing potency.The strongest correlation between pathway usage and potency is found in the cell cycle, nucleotide metabolism and cancer pathways. As these groupings are obviously rather arbitrary it is probably more correct to say that functions involving cell cycle regulation and cell cycle control both in healthy and diseased conditions correlate very well with chemical potency. Nucleotide metabolism is tightly connected to the cell cycle and consequently a down regulation of nucleotide metabolism in parallel to a down regulation of cell cycle functions is expected. The apoptosis pathway Aryl Hydrocarbon Receptor signaling is a novel finding with regard to potency prediction. This pathway is among the highly regulated ones but still shows a decrease in activity with decrease in potency.let-7.Finally, transcription regulation of the sensitization process was investigated, by prediction of activation and inhibition of upstream regulators. A network of regulatory elements that seemed to be generally involved was identified. These seemed to be centered on the transcription regulator MYC and the miRNA in vitro test for assessment of sensitization potency. Few chemicals appear to induce identical responses in vitro, indicating that excessively simplistic assays will fail to correctly classify unknown samples. Subdivisions among sensitizers, by reactive domain or otherwise, should be considered with caution and predictive models should be trained accordingly. In brief, we demonstrate for the first time that genomic approaches contain valuable mechanistic information associated with potency assessment of chemicals, which is in line with the current demand of evidence based toxicology.The presented results have implications for the design of predictive ACD: Allergic contact dermatitis; Add/El: Addition/elimination to carboxylic acid derivatives; AHR: Aryl Hydrocarbon Receptor; HCA: Hierarchical cluster analysis; Koor: Coordination bonds; LLNA: Local Lymph Node Assay; MA: Michael addition or 1,4-Addition to a/b unsaturated carbonyl; NAR: Nucleophilic aromatic substitution; PCA: Principal component analysis; PreH Prehapten: Chemical activation (outside the skin); ProH Prohapten: Enzymatic activation; SB: Schiff’s Base reaction; SN2: Bimolecular nucleophilic substitution.The authors declare no conflicts of interests.ML and CB designed the sensitization study strategy. HJ and ML set up and optimized the cell-based assay. HJ performed the cellular stimulations with chemicals. AA, HJ and ML designed the analysis strategy. AA performed the data analysis and prepared the text and figures. AB contributed with chemical knowledge and data set stratification guidelines. AA, HJ and ML drafted the manuscript. All authors revised and approved the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/2050-6511/15/5/prepubTable of gene involvement shown per reactivity group and per pathway, used for Figure Click here for fileTable of upstream regulators predicted to be up or down regulated in the IPA® analysis. Predictions were based on expression data from the nine chemical reactivity groups studied.Click here for fileNRF2 Oxidative Stress Pathway map for Michael addition or 1,4-Addition to a/b unsaturated carbonyl reactive chemicals. The pathway map shows the NRF2 Oxidative Stress Pathway with expression data from the 1000 most significant genes regulated by MA. Red shows up regulation and green down regulation of a given molecule. The less significantly regulated molecules were not shown in this figure, indicated here as white molecule symbols.Click here for fileA detailed list of molecules in the Aryl Hydrocarbon Receptor (AHR) signaling pathway. The file shows all the molecules included in the AHR signaling pathway, as defined by IPA®. Common identifiers are included to enable analysis in any software.Click here for fileAll upstream regulators identified in pathway analysis in IPA®. This file contains the complete result from the prediction analysis of the expression patterns seen in the nine data sets, one from each chemical reactivity group studied. The upstream regulators were predicted from the pattern of up or down regulated target molecules. Significance of finding is given as z-scores for each upstream regulator and an attached predicted activation status shows if the molecule is predicted to be up or down regulated. Molecules measured on the microarrays (observed) are recognized by a value in the fold change column.Click here for file


Escherichia coli. The obtained network contained 774 components and 1232 interactions, highlighting the complexity of bacterial multi-level regulations. The structured representation of this network represents a valuable resource for systemic studies of E. coli, as illustrated from the application to multi-omics data. Some current issues in network representation are discussed on the basis of this work.Combinations of ‘omics’ investigations  are increasingly applied to get comprehensive understanding of biological systems. Because the latter are organized as complex networks of molecular and functional interactions, the intuitive interpretation of multi-omics datasets is difficult. Here we describe a simple strategy to visualize and analyze multi-omics data. Graphical representations of complex biological networks can be generated using Cytoscape where all molecular and functional components could be explicitly represented using a set of dedicated symbols. This representation can be used i) to compile all biologically-relevant information regarding the network through web link association, and ii) to map the network components with multi-omics data. A Cytoscape plugin was developed to increase the possibilities of both multi-omic data representation and interpretation. This plugin allowed different adjustable colour scales to be applied to the various omics data and performed the automatic extraction and visualization of the most significant changes in the datasets. For illustration purpose, the approach was applied to the central carbon metabolism of  The graphical visualization and analysis of multi-omics data is a challenge in systems biology Cyclone” the Gaggle” http://www.genespring.com; “Ingenuity Pathways Analysis”: http://www.ingenuity.com/), but they are quite expensive and lack the flexibility of open-source software. Some tools have been developed to represent either metabolic or regulatory networks . These components were compiled using the open source software Cytoscape The aim of this work was to develop a strategy for the representation of complex biomolecular networks that facilitates the intuitive interpretation of multi-omics datasets. A graphical formalism  was intrEscherichia coli central carbon metabolism and its regulations. Cellular metabolism represents a valuable example of a complex and tightly regulated biomolecular network. The central carbon metabolism is composed of a set of highly interconnected reactions that provide a variety of molecules, energy and redox power to the cell to sustain survival, growth, and adaptation. It carries some of the most basic processes of life and is subjected to intense regulation. The central carbon metabolism and its regulation have been extensively studied and a large wealth of information is available to generate a highly detailed network describing all known metabolic and regulatory interactions.For illustration, the graphical formalism was applied to build up a map of E. coli central carbon metabolism. The network was consequently delimited to include all the central carbon metabolic pathways: glycolysis/gluconeogenesis, pentose phosphate, TCA, glyoxylate, Entner-Doudoroff, methylglyoxal and acetate  and personal expertise. The final network (e.g. Csr system). As most of the transcriptional factors are themselves under transcriptional control, all the indirect degrees of transcriptional regulations were included.The second step was to collect and gather all the molecular and functional information related to this network and its regulation from relevant databases: the information was compiled for metabolic pathways . Consequently, the graphical layout  were extremely pleiotropic. Likewise, ATP, ADP, AMP, phosphate and coenzyme A have numerous implications in enzymatic activity control. For these pleiotropic regulators, a “passageway” representation was introduced to avoid too many crossing lines over the central part of the graph . The resulting interaction map provides a unique graphical interface to access the knowledge accumulated about E. coli central carbon metabolism and a valuable illustration of the complexity of its regulations.The third step consisted in organizing the network layout to facilitate the intuitive reading of the biological information despite the considerably high number of components. The aim is to optimize the clarity of the representation  while respecting the academic conventions for intuitiveness. This is not feasible with the automatic tools for network representation that are currently available . Becausel layout  was orgaIt has to be noted that, using the same strategy, the same network could be represented in a different manner depending on the biological question. For illustration, it could be organised around pleiotropic transcriptional regulators if the question was mainly related to global regulation.e.g., Ecocyc, Brenda, Pubmed, etc.). In most cases, this allows getting molecule structures, reaction details, enzymatic effectors and their targets, gene and protein properties, or gene regulation networks  of the PTS gene expression at both the transcriptional and translational levels. This activation is likely to be controlled by the Mlc/dgsA transcriptional factor. Another transport system, i.e. the mannose PTS operon, is also under the control of Mlc/DgsA. However, this operon is down-regulated (green), which could be explained by the influence of the NagC transcriptional factor. In spite of the induction of the PTS transport system, the glycolytic flux seems to be stable (yellow). This apparent lack of effect is due to the fact that flux data were expressed relative to the rate of glucose uptake. Indeed, the absolute rate of glycolysis in the fast-growing cells was higher than in the slow-growing cells. In addition, the display of the relative flux data showed a significant redirection of the carbon flux towards the pentose phosphate pathway (PPP) when the growth rate is increased. The genes encoding the PPP enzymes were mostly upregulated at the transcriptional level , but not at the protein level. The correlation of transcriptomic and proteomic data showed a Pearson score of 0.04 for PPP components, compared to 0.68 for glycolysis  can be associated to only one kind of attribute . To apply the colour scale to all omics data, all biological components must be declared with the same and unique attribute. Such generic design does not allow processing separately the different types of omics data to account for differences in data format  or differences in the amplitudes of changes between, which can be highly different from one type of data to another. The nature of changes can be different too. For instance, the flux through a reversible reaction can be orientated in the direction opposite to that of a reference condition, The reverse direction can be expressed as a negative value, and has to be explicitly visualized on the graphical representation using dedicated attributes. The application of a unique colour scale to all types of data can hamper the visibility or nature of changes associated with some types of omics data compared to other types.Here, we propose MODAM (stands for Multi-Omic Data Miner), a new plugin to overcome this problem and to extend Cytoscape functionalities toward data mining in multi-omics datasets. These functionalities are accessible through a user-friendly GUI . A stroni.e. components they are directly connected to). A second network can be obtained by extracting only up-regulated components and their neighbours (E. coli strain (Δzwf) deleted for the gene zwf encoding glucose-6-phosphate dehydrogenase (G6PDH) and its isogenic wild type strain zwf strain blocks the flux of carbon through the oxidative part of this pathway, resulting in significant metabolic rearrangements. The sub-network of transcriptional factors involved in the metabolic adaptation to zwf knock-out could be automatically extracted from the selection , all implicated in succinate metabolism, is regulated by the four transcription factors. This kind of conclusion could not be directly raised from the initial network representation since the considered components and their interactions are widely distributed over the graph.The complete interaction network contains hundreds of components among which many of them were not monitored by omics data or for which changes were not significant. In the biological interpretation of the results, these parts of the network can be discarded in order to focus on the core of the modifications. To do so, MODAM includes extra features like the automatic extraction of three sub-networks highlighting the most significant data. A first sub-network contains all the biological entities that are down regulated . Since tighbours . Finallyighbours . To illuelection  of the melection  nicely hE. coli central carbon metabolism network presented in this work has been set up as default on the launch of MODAM, as well as the best Cytoscape parameters and visual properties to display the whole network. This will provide any user with fast access to the knowledge compiled in this work. Users can also create their own networks following the conventions given in the manual of the plugin and, this way, can apply MODAM to their own networks.Last but not least, the https://sites.google.com/site/modamplugin/). The central concept is to decompose the structural and functional components from gene expression to translation, from activities to fluxes. It was possible to represent the different types of interaction and therefore, to conciliate regulatory and structural networks on the same figure, and to map this network with multi-omics data. The visualization strategy could be divided in three steps: (i) network delineation and data gathering; (ii) compilation, justification and organization, using Cytoscape; (iii) data mapping and interpretation using the custom-made plugin MODAM.This work presents a simple, handy and inexpensive strategy to facilitate the visualization and analysis of complex multi-omics datasets , different network layouts and representation conventions could be adopted for the same biomolecular network. Whatever the focus, a generic layout issue is the density of nodes and interactions to represent, which increases with the network size. The first rule is to minimize the distance between interacting nodes. However, some nodes – e.g. pleiotropic regulators in the E. coli network - have multiple interactions that spread over multiple sub-parts of the network. For such highly connected nodes, the ‘proximity rule’ was by-passed by creating “passageways” circling the zone of interaction. This passageway solution was very efficient to clarify the representation without loss of information or in data visualization capability. In addition to the visualization aspects, the formalism introduced here will be of interest to translate classical drawing conventions into computation. The graph structure facilitates the application of automatic mining methods (e.g. automatic search of highly connected genes that can correspond to important regulators) and can be exported to other bioinformatics software.The graphical representation of complex biological networks and multi-omics data is currently a challenge as many problems are known to be computationally difficult E. coli network presented here. The extension to a different organism (the bacterium Clostridium acetobutylicum) required only two weeks (data not shown). For such less studied organisms, the graphical formalism could be adapted  to account for uncertain information or to include data from close organisms. The proposed strategy is not limited to metabolism but can be applied to other cellular processes, e.g. signaling pathways, cellular cycle, stress responses, etc.Cytoscape proved to be a highly versatile and flexible graphical platform, and met expectations as regard to the diversity of biological entities and interactions to represent. This versatility may be useful to users willing to extend the formalism introduced in this work. In terms of time, it took a couple of months for a single person to establish the zwf deletion, which is of special interest for further comprehensive understanding of metabolic robustness. It allowed also the detection of discrepancies between transcriptomics and proteomics data in E. coli growing at different growth rates, rising the need for further investigations to determine whether this was due to technical issues or to the occurrence of post-transcriptional controls.The main limitation was the representation and exploration of multi-omic datasets, which was overcome by developing the MODAM plugin. As shown from the examples provided in this work, MODAM greatly facilitates data mapping , allows multi-omic data representation  and improves interpretation (automatic extraction of most significant information). MODAM could highlight the transcriptional factors involved in the metabolic reorganizations caused by the E. coli proved to be a valuable illustration of the potential of the proposed visualization strategy, of its value for data assessment and mining, and for the formulation of new working hypothesis. The compilation work performed here can be freely exploited and transposed to different format/applications. In spite of the biological system complexity, the strategy does not require strong bioinformatics background and is accessible and user-friendly to users interested in omics data visualization and compilation of biological knowledge. Finally, the richness of the information displayed on a single figure as presented in this work is a first achievement and demonstrates the possibility of the approach. Taken in conjunction with current similar efforts, like the standardization of visual languages The application to the central carbon metabolism of Figure S1Mapping of multi-omics data (fast growth versus slow growth) using the “normal mode”. Plot of a full set of omics data extracted from Ishii et al. (2007), and corresponding to the comparison of the growth of E. coli at two growth rates (0.7 and 0.2 h−1). Data are displayed as ratios relative to the wild type. The color scale tends from green for a greater value at the low growth rate to red for a greater value in the rapid growing cells, through yellow for equivalent values. Grey shapes are nodes with no associated values.(EPS)Click here for additional data file.Figure S2Automatic layout of the network using cytoscape. Manual layout (a), cytoscape circular layout (b), hierarchical layout (c), sugiyama layout (d), spring embedded (e) and organic (f).(EPS)Click here for additional data file.Figure S3Mapping of multi-omics data (zwf) using the “Normal mode.” Plot of a full set of omics data extracted from Ishii et al (2007), and corresponding to the comparison of the growth of E. coli zwf mutant (encoding the first reaction of the pentose phosphate pathway) versus its isogenic wild type control. Data are displayed as ratios relative to the wild type. The color scale tends from green for a greater value at the low growth rate to red for a greater value in the rapid growing cells, through yellow for equivalent values. Negative fluxes are represented by a blue scale . Grey shapes are nodes with no associated values.(EPS)Click here for additional data file.Figure S4Mapping of multi-omics data (zwf) using the “Impact mode.” Plot of a full set of omics data extracted from Ishii et al (2007), and corresponding to the comparison of the growth of E. coli zwf mutant (encoding the first reaction of the pentose phosphate pathway) versus its isogenic wild type control. Data are displayed as ratios relative to the wild type. The color scale tends from green for a greater value at the low growth rate to red for a greater value in the rapid growing cells, through yellow for equivalent values. Negative fluxes are represented by a blue scale . Grey shapes are nodes with no associated values. Sizes of the gene, protein and metabolite nodes are adjusted depending on the fold change.(EPS)Click here for additional data file.


Plants are important as foods, pharmaceuticals, biorenewable chemicals, fuel resources, bioremediation tools and general tools for recombinant technology. The study of plant biological pathways is advanced by easy access to integrated data sources. Today, various plant data sources are scattered throughout the web, making it increasingly complicated to build comprehensive datasets.Glycine max) and grapevine (Vitis vinifera) data. Pathways are enriched with known or predicted information on sub cellular location. MetNet Online enables pathways, interactions and entities to be browsed or searched by multiple categories such as sub cellular compartment, pathway ontology, and GO term. In addition to this, the “My MetNet” feature allows registered users to bookmark content and track, import and export customized lists of entities. Users can also construct custom networks using existing pathways and/or interactions as building blocks.MetNet Online is a web-based portal that provides access to a regulatory and metabolic plant pathway database. The database and portal integrate Arabidopsis, soybean (http://www.metnetonline.org. Extensive video tutorials on how to use the site are available throughhttp://www.metnetonline.org/tutorial/.The site can be reached at Plants are an increasingly facilitating and augmenting  human life and plant systems biology resources exist in a variety of locations. Those rSaccharomyces cerevisiae (for model comparison) and Hypericum spp. MetNetDB is the underlying database and draws the majority of its information from external databases  to facilitate data-flow to external applications. For bioinformatics software developers, a separate application programming interface (API) is provided. MetNet http://www.mysql.com) that serves as the central repository for all MetNet applications[MetNetDB is a MySQL database  IDs.Grapevine pathways were all manually constructed by field experts using CellDesigner. A specihttp://www.graphviz.org) is used to generate graphical representations of pathways and networks  and interactions can occur between any number of entities or between entities and other interactions . A pathway is a collection of interactions that provides a convenient unit to represent prevailing cellular functions. Pathways in MetNet are predefined and cannot be changed by a user, but can be integrated with each other or with other interactions to form new units. A network in MetNet is a collection of interactions for which the granularity is determined by the user when (s) he creates it. A network can consist of any number of interactions or it can be a combination of some already defined pathways. It can also map to exactly one pathway or it can map to a pathway minus transcription/translation events. Networks are virtual and transient objects.The database can be browsed based on different ontologies or navigation trees including pathway category , entity participation, cellular location  and interaction type . After navigating through a tree and selecting a node of interest, a list of pathways is displayed (either in list-form or by thumbnail) and the pathway information screen can then be chosen or the pathway can be visualized directly. Visualization of a particular pathway may be different from what one is used to seeing at other locations or textbooks, as we rely on GraphViz’s algorithms to render the underlying graphs: no manual intervention (e.g. to make cycles more obvious) is currently implemented in our data flow.Information about a pathway consists of general comments and literature references, location information, interactions contained within the pathway and participating entities. Sources and sinks for the pathway are displayed in a separate tab as part of the participating entities. This is critical information for simulations in which the pathway is treated as a black box . At the top-right of the pathway information screen, a toolbar is shown with export functions to various programs and a link to visualize the pathway (discussed separately). Entities can be browsed  independently of pathways and the entity information screen contains location information, possible synonyms, pathway participation and categorized interactions. Additional tabs are available on the pathway information screen.The literature tab interfaces with PubMed to retrieve a current literature feed, in which the name of the pathway selected by the user is used as a search-term. Cellular context (i.e. the location within the cell where an entity is present when participating in an interaction) is represented separately, so that one can get an idea of the various roles a protein or metabolite might play. Another tab shows connected pathways that share one or more entities.MetNet Online visualizes pathways with their known or predicted sub cellular locations. This information is not available anywhere else . Sub cellular location information can help scientists develop hypotheses on gene function. Entities are color-coded according to assigned location and shape-coded according to entity type. Interactions are color-coded according to type for easy identification and visualization.MetNet Online’s search function is integrated, rather than providing different search functions for entities, interactions and pathways. Thus, search operations for “regulation”, “biosynthesis”, “AT4G40090”, “AGP3” or “malate” use the same interface. Search results are grouped by entity types, interactions and pathways. When searching for “glucose”, for example, not only the “glucose” metabolite is presented but also the “glucose-UDP biosynthesis” pathway, among others. Synonyms are taken into account. Searches for “H2O” and “water” or “O2” and “oxygen” both lead to the same entity. Typos and misspellings result in suggestions that often point a visitor in the right direction and when no results are found for a search, potential alternatives are suggested. An example of this would be when “giberelin” is entered, the alternative “gibberellin” is proposed.http://www.graphviz.org) .dot file is generated and transformed into its visual representation (dot layout). In the upper left corner of the screen, a thumbnail of the complete pathway is shown to allow easy navigation through complex maps. An indexed list of all participating entities is displayed underneath the thumbnail.When visualizing a pathway, a GraphViz . Fisher’s exact test is available for both visitors and registered users. Registered userscan automatically apply the Fisher’s exact test to their stored customized lists of entities and interactions. Visitors have to specify their entities of interest manually in a text field. While a user can extract and visualize data from the network in a wide variety of ways, no user can change anything in the original network without going through a MetNet curator.By providing the option to export pathways and subnetworks to other file formats, MetNet Online leverages existing software that incorporates a wide range of supplementary layout algorithms  on developing soybean embryos. He identifies a list of differentially (under- and over-) expressed genes. He saves the probe-names (or gene names) as a separate text file (soybean_de.csv) and visits the MetNet Online website. He logs into his My MetNet account and creates a personalized list by uploading the text file. Because the probe-names are recognized by MetNet, he looks for pathways that are over-represented among the differentially expressed genes. Because numerous genes (thousands) are differentially expressed during embryo development, many pathways show up in an initial quantitative screen. As such, the user decides to use the Fisher exact test module to rank the pathway over-representation by p-value. This presents him with useful information; the list of pathways is still large but they are now ranked by p-value for relevance. He examines the pathways with the lowest p-values and is thus able to identify other potential gene targets for future experiments and verification. The two use case scenarios for MetNet are described in detail in an online video tutorial atMolecular biologists, physiologists and biotechnologists aim to understand the function of particular genes, polypeptides or metabolites, and to develop testable hypotheses as to how these biological entities function to influence the overall biological network. Easy and convenient access to integrated information from a variety of biological data repositories can greatly facilitate these goals.We have built a new portal, MetNet Online, which provides a gateway to integrative systems biology applications. The site generates simple pathways or complex representations of customized interaction sets or combined pathways.In addition to existing data for Arabidopsis, soybean and grapevine biological networks, MetNet Online incorporates manually curated interactions, in particular signal transduction interactions, and also introduces a sub cellular location data layer. Our site supplements other previously created tools, and interfaces with many of them. Users can integrate pathways and interactions to build custom network and track objects  that are of particular interest to them.http://www.metnetonline.org. Extensive video tutorials on how to use the site are available throughhttp://www.metnetonline.org/tutorial/.The MetNet Online site can be reached atThe author(s) declare that they have no competing interests.YS conceptualized and implemented the web portal, and prepared this manuscript. YW was responsible for back-end data integration, data update, and data services. JL established the initial schema for the database and developed curation tools. ESW is principal investigator of this project and supervised this manuscript. All authors read and approved the final manuscript.Table S1. Comparing MetNet Online with other resources.Click here for file


A large repertoire of gene-centric data has been generated in the field of zebrafish biology. Although the bulk of these data are available in the public domain, most of them are not readily accessible or available in nonstandard formats. One major challenge is to unify and integrate these widely scattered data sources. We tested the hypothesis that active community participation could be a viable option to address this challenge. We present here our approach to create standards for assimilation and sharing of information and a system of open standards for database intercommunication. We have attempted to address this challenge by creating a community-centric solution for zebrafish gene annotation. The Zebrafish GenomeWiki is a ‘wiki’-based resource, which aims to provide an altruistic shared environment for collective annotation of the zebrafish genes. The Zebrafish GenomeWiki has features that enable users to comment, annotate, edit and rate this gene-centric information. The credits for contributions can be tracked through a transparent microattribution system. In contrast to other wikis, the Zebrafish GenomeWiki is a ‘structured wiki’ or rather a ‘semantic wiki’. The Zebrafish GenomeWiki implements a semantically linked data structure, which in the future would be amenable to semantic search.Database URL:http://genome.igib.res.in/twiki Recent advances in genomics have required biologists to revisit accepted paradigms of how genes function and interact, as well as how genes cooperate to modulate a diverse array of complex biological processes such as development, metabolism and behavior . ApplicaIntegration of these data sets into a common platform has been inhibited by the need for systematic manual curation  of inforSuch community participation in an open environment has been successfully applied for covering the long tail in biological annotations . These mDanio rerio), a popular vertebrate model organism, has a genome of ∼1.5 billion base pairs distributed over 25 chromosomes. The zebrafish reference genome (http://www.ensembl.org/Danio_rerio/Info/Index) and genes and transcript annotations are readily available from major genome browsers and databases such as National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/genome/guide/zebrafish), Ensembl (http://www.sanger.ac.uk/Projects/D_rerio/zmp/), Zebrafish Tilling Project (https://webapps.fhcrc.org/science/tilling/), zinc finger nuclease-targeted mutations (http://zfishbook.org/) provide periodic updates regarding the transgenic and mutant lines that have been generated. Previously, we created the FishMap . The annotation process began with selecting an available gene of interest and systematically collecting information for a particular gene, including the gene name, gene identifier (ID), RefSeq ID and transcript IDs, in a preapproved format provided in the Zebrafish GenomeWiki. The key reference anchor points in the Zebrafish GenomeWiki for any gene is the gene name, gene ID and RefSeq ID. Therefore, we ensured that key reference anchor IDs correlated between the ZFIN, Ensembl and Zebrafish GenomeWiki databases. The annotation process also included extensive literature survey for both biological functions and mutant phenotypes. The volunteers were encouraged to discuss and share information online through online media. Curators further manually cross-checked all entries for inadvertent annotation errors  for our curation. For gene nomenclature, we used the official nomenclature from ZFIN. For each annotated gene, the ZFIN-approved gene name anchors its corresponding GeneCard  in the Zhttp://swish-e.org/) for efficient and speedy retrieval of the data. It indexes the entire data and quickly extracts the data on the basis of the keyword provided.Zebrafish GenomeWiki is a structured wiki, hierarchically organized into webs and subwebs. The wiki follows a hierarchy wherein the main web pages are the gene pages providing gene level information and links to other webs and external resources. The wiki also incorporates FishNet, a search engine for zebrafish gene annotation and resources. FishNet uses Swish-e (Simple Web Indexing System for Humans—Enhanced) . The success of community annotation . The database also permits users to download a database snapshot for archiving on their private servers.The Zebrafish GenomeWiki provides a dynamic real-time interface for uploading and extracting information. It saves information regarding all updates and modifications performed by any user in real time. Therefore, if a user inadvertently introduces a mistake during the annotation process, the error can be easily modified by reverting back to the last correct version  archived for security purposes. and the database is updated quarterly. The data are derived from ensemble and ZFIN and is thereon updated automatically.Success of any wiki-based resource depends on active community participation. Because studies show that community members are most willing to contribute voluntarily when their contributions are readily recognized , 37, thehttp://linkeddata.org/).In the first version of the Zebrafish GenomeWiki, we have manually curated 600 genes for which the users have provided biological annotations. In addition, the Zebrafish GenomeWiki also contains ∼52 896 transcripts and 4150 proteins. The Zebrafish GenomeWiki, we describe here, is a starting point toward systematically collating annotations for genes and gene products within a structured wiki platform. The system solves to a large extent the issues with unstructured data on wiki-based annotation platforms and provides for an alternative strategy combining the advantages of a structured database-driven annotation system with the openness of a wiki-based annotation system. The future would be to integrate this into standard ontologies and make available as Resource Description Framework (RDF) tuples, thus making it compatible with the semantic web technology . Recent We understand that to encourage and sustain high-quality annotation activity, apart from the peer review system and organizational hierarchy, there should be enough incentives to sustain the endeavor. All open source and open data initiatives have inbuilt incentive mechanisms, which sustain the organization and the community. We would be keen to work with journals and databases, and with upcoming initiatives like the Bioresource Research Impact Factor  and makeSupplementary data are available at Database Online.


In biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations.This study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis.This function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations. Pathway analysis is the currently best method for understanding the biological meanings of a set of genes derived from high-throughput experiments, such as gene expression microarray ,2. In biPathways are not isolated entities in a cell, but may have cross-talks. In biology, the term "cross-talk" refers to the phenomenon that signal components in signal transduction can be shared between different signaling pathways . PathwaySeveral computational approaches have been developed to identify pathway cross-talks. An intuitive method involves considering shared components between pathways ,7,8. BecThough these computational methods could determine the pathway cross-talks by p-values from different statistic methods, non-statistical significance may be biologically significant. In other words, cross-talking pathways may share only a few components or be connected by a few PPIs. For instance, the BMP and canonical WNT pathways in the Pathway Interaction Database (PID)  only shaTwo pathways interact with each other in order to participate or regulate a particular process for a specific condition. For instance, the cross-talk between the glucocorticoid receptor (GR) and T-cell antigen receptor (TCR) signaling pathways results in apoptosis during the development of thymocytes . AdditioIn this study, we presented a function-based approach (FBA) to identify pathway cross-talks, measuring the functional similarity between pathways via the Gene Ontology (GO) annotations of pathway components. In our previous study this method has successfully been used to understand the functional relationship between RAS-regulated pathways . Here, wThere are many pathway databases available in public domains, such as KEGG , BioCartPathway data were downloaded from Pathway Interaction Database (PID) , which dWe collected 168 pathways from PID after removing redundant and less informative pathways  is widely used for functional annotations of genes. Most genes have been assigned with multiple GO terms, and the assignments of GO terms are generally based on pathways in which the genes participate. Therefore, the function annotations of a pathway could be inferred from the GO terms of the pathway components. The function-based approach (FBA) that we proposed applies this character to measure the functional similarities between pathways, comprising two steps: 1) inferring the representative GO terms for each pathway, and 2) calculating the similarity among pathways.Though pathway components may find numerous relevant GO terms, these terms are not all suitable in describing the function of a given pathway. This problem is similar to the GO term enrichment analysis, which is conducted to investigate whether gene sets associated with particular GO terms. The Fisher exact test has been successfully used to identify "enriched" GO terms of a given gene set ,21. If aOn average, 261 representative GO terms emerged per pathway  between two pathways was computed via cosine measure. The higher the funSim score, the more functionally related this pathway pair.In principle, functionally related pathways should share GO term annotations. Each pathway pair has on average 79 common GO terms  score of the 14,028 combinations for 168 pathways. The distribution of these scores is shown in Figure The application of the aforementioned procedure calculated the functional similarity . However, the reliability of this cut-off value was further examined by two methods.In order to decide whether two pathways interact with each other, a cut-off value is required. The cut-off value was estimate by comparing the distribution of s Figure . To contfunSim were examined whether it can correctly reveal the functional relations. If two pathways are functionally related, they are more likely to fall into the same functional category. Therefore, the pathway pair from the same functional category should have higher funSim than those not in the same category. To test this hypothesis, pathways were manually classified into 8 categories based on the description of each pathway in PID. A pathway may be assigned into more than one category because a pathway may play multiple roles in a cell. Since 45 pathways lacked a clear description, only 123 pathways were classified  and "circadian rhythm pathway (circadianpathway)" (max funSim = 0.29). The mechanism of both pathways may be different from other pathways. The "effect of Botulinum toxin", for example, is involved in the transportation of neuron transmitters and muscle contraction causes. Therefore, the mechanism and effects of this pathway are quite different from other signal transduction pathways.Secondly, since all pathways are part of regulator circuit in the cell, each pathway is expected to have at least one functionally related pathway. Thus, the largest funSim as 0.5 is reliable to distinguish the functionally related pathway pairs. Based on this cut-off value, 4,661 out of 14,028 pathway pairs were functionally related pathway pairs (FRPs), which were among 166 pathways  were further analyzed using protein-protein interactions (PPIs). Finally, 1,681 SIPs were identified. These two methods are complementary to each other, so they should be taken together to find all putative pathway cross-talk pairs, and we denote these two methods as the physical entity-based approach (PEBA). Therefore, PEBA identified 4,093 putative pathway cross-talk pairs among 167 pathways  was chosen as a positive control to study its related pathways. This pathway is known to play diverse functions in vertebrates -32 and hTo summarize the results from these three different testing examples, it appears that FBA has the ability to discover pathway cross-talks. Moreover, FBA can identify the most documented pathway interactions. Because of the lack of negative data, the false positive rate of each approach cannot be evaluated. Here, an alternative evaluation, pathway co-expression analysis, was applied to assess the false positive rate.e.g. tissue. Thus, pathway co-expression is a requisite for a real pathway cross-talk. This character can be used to evaluate the false positive rate of pathway cross-talk prediction methods: if the co-expression of a predicted pathway pair does not behave well, this pair may be a false positive.In a biological point of view, a pair of pathway can interact with each other if they are expressed at the same time and in the same place, Although the protein expression data are the best evidence to support co-expression, such data are difficult to obtain. This study used gene expression as an alternative, because a gene needs to be expressed before the protein is expressed. The expression of a pathway in a given tissue was assessed by the presence of expressed sequence tags (ESTs) for the components in this pathway. If two pathways are expressed in more common tissue, it implies these two pathways may have higher probability of being cross-talks. Here, the co-expression value of a pair of pathway was presented by the arithmetic average of Jaccard coefficient and overlap coefficient (see Methods section for details).-16, Wilcoxon signed-rank test). This is not surprising, since the common pairs that pass the criteria of the two distinct approaches are the most reliable pathway cross-talk pairs. Interestingly, the average co-expression value of unique pairs of FBA is significantly higher than that of PEBA . This result shows that the predicted pairs by FBA are more reliable than those by PEBA. In other words, the false positive rate of FBA might be less than that of PEBA.The distributions of co-expression values of putative pathway cross-talk pairs are given in Figure The previous results reveal that FBA is advantageous in discovering real pathway cross-talks. Furthermore, FBA can assist researchers to find some pathways associations which mechanisms are explainable. First, we focused on the pathway pairs which are predicted as FRPs and have only one common gene. Although these cases are unable to pass the criterion of SOPs, but this single shared component may be important for pathway interaction. Additionally, this shared component could assist us to understand the regulating relations between pathways without additional information.i.e. RHOA, and are connected by 20 PPIs which is less than expected value. Hence, this pair is unable to pass the criteria of PEBA. However, FBA predicted this pair is highly function-related with funSim of 0.65. Merging these two pathways by the Pathway Integration Tool (PINT) [There were 2,301 pathway pairs with one common gene. In these pairs, 399 and 329 pairs are FRPs and SIPs, respectively, and 172 pairs are both FRPs and SIPs. We examined the 227 unique pathway pairs which are only identified by FBA and found several pathway interactions which are biologically significant. The first example is the pathway pair of S1P4 pathway (s1p_s1p4_pathway) and RHOA signaling pathway (rhoa_pathway). These two pathways share only one gene, l (PINT)  shows thi.e. TFGB1, and are connected by 41 PPIs, they do not satisfy the criteria of PEBA. However, they have a significantly high functional similarity score (funSim = 0.69), implying these two pathways should interact with each other. Indeed, this pathway association has been observed in different situations. In epithelial cells, syndecan-1 expression is regulated via TGFβ-mediated signaling [Another similar example is the interaction between syndecan-1-mediated signaling pathway (syndecan_1_pathway) and TGFβ receptor signaling pathway (tgfbrpathway). Though these two pathways share only one gene, ignaling . In cardignaling . These ei.e. non-SOP), and the number of PPIs between this pair fails to pass the statistical test (i.e. non-SIP), this pathway interactions is easy to ignore by PEBA. Therefore, FBA is able to re-connect the relations of these pathway pairs. Total of 200 FRPs among 104 pathways are unable to be detected by PEBA and without common genes. Among these pathway interactions, several pathways formed cliques, which is a set of pathways fully connected to each other. We used CFinder [If a pathway pair does not share any component , atypical NF-κB , and EGFR (et_egfrpathway) pathways Figure  reveals Because there are many pathway databases available, we examined if FBA can produce the same results by using different pathway data. We manually collected corresponding signaling events from PID and Reactome, respectively. The ten signaling events which are all curated by PID and Reactome, respectively, were analyzed and listed in Table funSim scores of pathway pairs from the same database were performed. As shown in Figure funSim score between using pathways from PID and Reactome is highly correlated (Pearson correlation coefficient = 0.78). When cut-off value of 0.5 was used to distinguish if the two pathways interact with each other, 41 out of 45 pathway pairs are consistent between two pathway databases. The four inconsistent cases are the pathway pairs PDGFR-mTOR, mTOR-TGFβ, VEGFR-TGFβ, and PDGFR-p38 MAPK. The first three cases are the funSim > 0.5 by using PID data and funSim < 0.5 by using Reactome, but the last one is opposite.Firstly, the funSim scores of pathway pairs from different databases, e.g. for each pathway pairs, one pathway is from PID, and the other is from Reactome, were calculated. The funSim scores for PID-Reactome (i.e. the first pathway from PID and the second one from Reactome) and Reactome-PID (i.e. the first pathway from Reactome and the second one from PID) reveal still high correlation (Pearson correlation coefficient = 0.67)  Figure . Only 8 funSim of 0.5 can be a general threshold to determine the pathway cross-talks.The results show that prediction of pathway cross-talks by FBA is robust and reproducible even though the pathways curated by different databases are diverse. Additionally, the Despite the fact that FBA has the ability to predict the pathway cross-talks, it does have limitations. Because the relations are inferred from the GO annotations of pathway, the performance of FBA may be dependent on the quality of GO annotations. In this study, Fisher exact test with a multiple testing correction was applied to selection of the representative GO terms of a pathway. However, this procedure might still infer the false pathway-GO term associations due to the quality of GO annotations of genes  and the Some important pathway interactions still fail to be identified by these computational approaches. The notable example is NOTCH signaling pathway (notch_pathway). This pathway is important to developmental process and able to integrate with several major pathways . HoweverWe present a novel approach to identifying pathway cross-talks by measuring the functional relations between pathways. The functional relations rely on GO annotations of pathway components through the vector space model. The concept of this function-based approach (FBA) quite differs from the physical entity-based approach (PEBA) which identifies pathway cross-talks based on shared components and protein-protein interaction. The comparison with PEBA corroborates the contribution of FBA. Many well-studied pathway cross-talks can only be successfully predicted by FBA. Though FBA identified approximately 4,600 putative pathway interactions among 166 pathways, the false positive rate of FBA might be significantly less than that of PEBA, assessed by pathway co-expression analysis. Therefore, FBA not only is more sensitive to detect cross-talks but also infers more biologically significantly and explainable pathway relations. The most important character of FBA is that the analysis results are reproducible even though different pathway data were used. With the development of pathway analysis and visualization tools, this approach can assist biological researchers to propose a potential mechanism and to prioritize the pathways for further experimental design.Undoubtedly, discovery of pathway cross-talks is indeed important for understanding biological regulations, but we may face several challenges. The first problem is the lack of gold standard data sets. Therefore, we are unable to evaluate the prediction power of a new method. In this study, we collected real pathway interactions from PID and literature. Although the collection is not comprehensive, it may be sufficient for evaluation of different methods. Additionally, we used an alternative way, considering the pathway co-expression, to assess the false positive rate. The second problem is that pathways are dynamic, not static. Pathway may interact with the other pathways in a specific condition, but dot not influence each other in another condition. Several studies address this problem by incorporating gene expression information to filter the active interactions. Finally, because each method has its own advantage on predicting certain pathway cross-talks, how to integrate different characters to improve the precision of prediction is an important task in the future.Pathway data containing 184 pathways with 2,346 genes were downloaded from the Pathway Interaction Database (PID)  in Aug. PID also records some pathway interaction information. These pathway interactions were extracted from XML format file of each pathway. Finally, 143 pathway interactions among 118 pathways were obtained from PID and regarded as positive data.http://www.geneontology.org/), and the GO annotations for all human genes were downloaded from NCBI Entrez Gene (ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz). Only GO terms from the biological process ontology were analyzed. To avoid an overestimation of the performance of our pathway cross-talk predicting method, only annotations based on directly experimental evidence were considered, including EXP (inferred from experiment), IDA (inferred from direct assay), IPI , IMP (inferred from mutant phenotype), IGI (inferred from genetic interaction), IEP (inferred from expression pattern), and IC (inferred by curator). In other words, these gene-GO term associations are supported by relevant literature.The proposed function-based approach (FBA) utilizes GO annotations to measure the functional similarity between pathways. The first step required determining the GO terms that could represent the biological function of a pathway. The GO terms were obtained from the GO website  method [P-values < 0.05 were considered representative GO terms for a pathway.The over-representation analysis method, a common method to assess the enrichment of specific biological themes in a gene list , was use) method . GO termip, as follows:A vector space model was used to compute the functional similarity between pathways. A pathway is represented by a specific vector i, j wis the weight of the representative GO term j for pathway i, and n is the number of representative GO terms associated with all pathways. If the pathway does not have a given GO term, the weight of the GO term for this pathway is 0. Since not all GO terms are equally informative, the TF-IDF , a GO term's semantics inherits the biological meanings of all its parent terms. In other words, when a term is used to describe a gene, all its parent terms also apply to this gene. Therefore, the frequency of GO term j appearing in pathway i, ji, tf, is given by:where i, j ais the number of genes in pathway i annotated by the term j, and children(j) is the set of child terms of term j.where 1, p2)funSim in intra- and inter-categories were compared. If a given functional category has These two factors were used to evaluate the effect of the function-based method on measurement of functional similarity between pathways.P-values were adjusted via BH method. The pathway pairs with adjusted P-value < 0.05 were regarded as the significantly overlapping pathway pairs (SOPs).An intuitive manner of studying pathway cross-talks is by measuring the number of shared components between pathways. Li et al  utilizedhttp://csb2.ym.edu.tw/quasipro), which integrated PPI data from nine sources, including DIP [The remaining pathway pairs that were not significantly overlapping (non-SOPs) were studied via protein-protein interactions (PPIs). PPIs were downloaded from QuasiPro .PPIs were used to assess the pathway cross-talks according to the procedure proposed by Li et al. . The numhttp://cgap.nci.nih.gov/Tissues/). 46 tissues were present in the EST dataset, excluding uncharacterized and pooled tissues. ESTs from the same tissues were pooled together. A gene is considered to be expressed in a specific tissue if at least one of its EST sequences is found in the dataset.The gene expression information was based on the ESTs (Expression Sequence Tags) from UniGene (Build 222). The standardized tissue names for libraries were obtained from the Cancer Genome Anatomy Project  and overlap coefficient (OC) defined as:The co-expression value between two pathways was measured by combining Jaccard coefficient , KEGG (B), and PID (C), respectively, are curated with different components (D).Click here for fileList of PID pathways used in this study.Click here for fileEnriched GO terms for each pathway. (A) Distribution of the number of enriched GO terms per pathway. (B) Distribution of the number of common GO terms per pathway pair.Click here for fileManually curated functional categories.Click here for fileList of putative pathway cross-talks predicted by FBA and PEBA.Click here for file


The PathoLogic component of the Pathway Tools software performs prediction of metabolic pathways in sequenced and annotated genomes. This article provides a detailed presentation of the PathoLogic algorithm. The algorithm consists of two phases. The reactome inference phase infers the reactions catalyzed by the organism from the set of enzymes present in the annotated genome. The pathway inference phase infers the metabolic pathways present in the organism from the reactions catalyzed by the organism. Both phases draw on the MetaCyc database of metabolic reactions and pathways. MetaCyc contains two data fields to support pathway inference: the expected taxonomic range of each pathway, and a list of key reactions for pathways. These fields have significantly increased the predictive accuracy of PathoLogic. Mus musculus [Saccharomyces cerevisiae [Arabidopsis thaliana [Drosophila melanogaster [Escherichia coli [Homo sapiens [We describe the metabolic pathway prediction algorithm used by the Pathway Tools software ,2. Pathwmusculus , Saccharrevisiae , Arabidothaliana , Drosophnogaster , Escherihia coli , and Hom sapiens . See [12The Pathway Tools pathway prediction algorithm predicts pathways in a sequenced genome by recognizing in that genome previously known pathways from the MetaCyc database . PathwayPathway Tools contains several computational inference components , which aThe MetaCyc metabolic database is the reference pathway database used by PathoLogic for pathway prediction . VersionThe pathway information in MetaCyc has been specially honed to support pathway prediction in the following respects. First, curators sometimes divide pathways into smaller segments that correspond to atomic units conserved by evolution. This process is informed by curators observations of which pathway segments occur in different organisms, and can yield more accurate pathway predictions. For example, imagine that organism A contains a linear pathway P1 consisting of six consecutive reactions, R1–R6. Imagine, however, that organism B contains a smaller portion of P1 consisting of reactions R4–R6, which we will call P2. If P1 were present in MetaCyc and we were performing a pathway prediction in organism B, and B contained enzymes for reactions R4–R6, we would predict P1 as present in B. However, this inference would be incorrect because B in fact contains the shorter pathway P2. When MetaCyc curators encounter this situation, they define pathway P2 in MetaCyc, and a new pathway P3 consisting of reactions R1–R3. Pathway P1 will be redefined as a superpathway containing P2 and P3. In this way PathoLogic can properly recognize whichever of P1, P2, or P3 is present in a given genome.A second way in which MetaCyc is optimized for pathway prediction is that it contains information on the expected taxonomic groups in which most MetaCyc pathways occur. Curators have recorded this taxonomic range information (in slot Taxonomic–Range) at whatever granularity they deem appropriate to capture their expectation of the taxonomic groups in which the pathway will be found. Taxonomic range assignments vary from very broad  to very specific , and are based on both the taxonomic groups in which the pathway is known to occur, and the function of the pathway. The next section describes how PathoLogic uses taxonomic range information.MetaCyc curators sometimes specify key reactions within a pathway. Key reactions are used by PathoLogic to differentiate between related pathway variants. Key reactions are reactions whose presence is considered as a critical signature for the presence of the pathway.MetaCyc contains a large collection of enzyme names obtained from the Enzyme Nomenclature List prepared by the International Union of Biochemistry and Molecular Biology  and fromPathway prediction is divided into two phases: prediction of the reactome (reaction complement) of the organism, and prediction of pathway complement of the organism from its predicted reactome.The input to PathoLogic is an annotated genome. The genome is provided as a set of files. For each replicon or contig in the genome, the user provides:An annotation file, in either Genbank format  or in PaA sequence file, formatted in FASTA format We have found that considerable variability exists in how different groups interpret the Genbank file format, and that it is not unusual to find Genbank files that are incompatible with the interpretation used by PathoLogic. Therefore, if Genbank format files are provided, care should be exercised in determining that proper tag names are used within the file , and that information is formatted properly within each tag . Exact details of expected file formats are provided in the Pathway Tools User’s Guide .The first step in pathway prediction is to infer the set of biochemical reactions that can be catalyzed by the enzymes encoded by the genome, that is, to infer the reactome of the organism. Because reactions are the direct building blocks of pathways, once we know the reactome of the organism, we can more easily assess the evidence for what pathways are present.The output of the reactome inference phase is the set of inferred reactions. All inferred reactions and their substrates are copied from MetaCyc to the organism-specific PGDB.The PathoLogic reactome inference module examines each gene product in the genome, and attempts to infer what if any reactions are catalyzed by that gene product. Often no reactions are inferred since many genes have no assigned function, and many genes that have assigned functions are not metabolic enzymes .The reactions catalyzed by a gene product are inferred from three information fields present in the Genbank or PathoLogic file for a gene product: the EC number, gene product name (enzyme name), and Gene Ontology (GO) terms. Ideally, the user would provide the gene product name plus either the EC number or GO terms (note that EC number and GO terms are redundant to a significant degree since a large number of Gene Ontology molecular function terms correspond directly to EC numbers). We prefer to have EC number or GO terms provided because these terms are from a controlled vocabulary and are therefore much easier to recognize than are uncontrolled enzyme names. However, many metabolic enzymes have not yet been assigned EC numbers or GO terms; therefore, controlled vocabulary terms cannot be assigned to many metabolic enzymes. For multifunctional enzymes, a gene product name and either EC number or GO terms should be provided for each enzyme function.We emphasize that it is the preceding information only that is used to infer the reaction(s) catalyzed by each gene product. Sequence information is not used by PathoLogic in reactome inference. Note also that many reactions predicted by PathoLogic do not end up being attached to metabolic pathways after PathoLogic pathway inference, which can occur because a reaction is not a component of any pathway in MetaCyc, and because no pathway of which the reaction is a component was inferred as present in the PGDB.The reactome inference algorithm infers the reactions catalyzed by protein P as follows.For each EC number ECi attached to P, look up all reactions R annotated with ECi in MetaCyc. Infer that P catalyzes every reaction in R for each ECi.If GO terms are attached to P, their associated EC numbers (if any) are also used in the previous step.For each enzyme name (gene product name) ENi attached to P, perform a lookup of ENi in MetaCyc. If a single matching reaction is associated with ENi, infer that P catalyzes the reaction associated with ENi in MetaCyc. The lookup of ENi in MetaCyc involves the following processing:Convert ENi to lower case and remove punctuation characters such as space and hyphen to eliminate variation in styling of enzyme names.Look up ENi in MetaCyc. If found, declare a match and stop any further searching. If not found, try the following step.Remove various prefixes and suffixes, one at a time, from ENi that can interfere with matching such as “putative,” “hypothetical,” “predicted,” “precursor,” “secreted,” and many other qualifiers, some based on regular expressions. For each removal, search for the reduced name in MetaCyc. Once a match is found, declare a match and stop further searching.If the same enzyme name matches multiple reactions, flag the name as ambiguous and do not infer reactions for it.The reactome inference module also compares the reactions inferred from enzyme name, EC numbers, and GO terms to ensure that they are consistent. If, for example, the enzyme name and EC number identify reaction sets with a null intersection, then a warning is produced, and PathoLogic infers the reaction for the EC number.Pathway inference is based on the set of catalyzed reactions that were imported from MetaCyc during the reactome inference step. All pathways inferred during the phase described have also come from a fixed reference database, namely MetaCyc. In the following when we say a pathway is inferred, it means that PathoLogic has predicted that the pathway is present in this organism. All inferred pathways are copied from MetaCyc to the PGDB being generated for this organism. When we say a reaction is present in the generated PGDB, we mean the reaction has been inferred as present in the organism by the reactome inference step, or that the reaction is marked as spontaneous in MetaCyc.Inference of a pathway is based on the approach that if no rule can be found to reject  a pathway, then the pathway should be inferred as present in that PGDB. PathoLogic applies the following rules to infer pathways. The rules are applied to base pathways (not to super pathways) individually and in no particular order.If a pathway is mostly absent in the generated PGDB, it is not inferred. A pathway is mostly absent if it has more than one missing reaction in the generated PGDB, and if it has no more than one reaction present.A reaction is unique (to a pathway) if the reaction is used in only one pathway in the generated PGDB. If a pathway P has a unique reaction, considering the pathways that were not inferred, P is inferred. In the particular case that two or more pathways share a unique reaction among them (meaning the reaction is found only in that set of pathways), and the only reason to keep these pathways is based on the uniqueness of that reaction, then one of the pathways is randomly selected to be kept.A biosynthetic pathway that is a proper subset of some other pathway and is missing its final two reactions is not inferred. Similarly, a degradative pathway that is a proper subset of some other pathway and is missing its initial step is not inferred. The intuition behind the preceding rules is that multiple biosynthetic pathways sometimes share common initial steps, and it is the final steps that are most indicative of the presence of the pathway (and conversely for catabolic pathways). An energy metabolism pathway with no unique reactions having fewer than half of the reactions present in the generated PGDB is not inferred.If the reactions in a pathway P1 are a superset of the reactions of an already inferred pathway P2, and P1 has more missing reactions than P2, P1 is not inferred.MetaCyc pathways contain taxonomic range assignments that indicate the range of organisms in which a pathway is likely to be found. Because MetaCyc taxonomic range assignments are approximate and are based on incomplete knowledge, PathoLogic does allow pathways to be inferred in organisms outside their taxonomic range, but it requires very stringent evidence to do so. A pathway outside its taxonomic range will be predicted as present if and only if all reactions in the pathway are present in the generated PGDB. The use of the taxonomic range information can be deactivated by the user.Many pathways of MetaCyc have some reactions designated as key reactions. Any pathway that is missing at least one key reaction in the generated PGDB is not inferred. If all key reactions of a pathway are present in the generated PGDB, the pathway is inferred.Electron transfer pathways are inferred if and only if they are complete, that is if and only if all their reactions are present in the generated PGDB. No other electron transport pathways are inferred.MetaCyc contains superpathways composed of individual (base) pathways, or a combination of base pathways and superpathways. A MetaCyc superpathway is inferred in the generated PGDB if and only if all its component superpathways and base pathways are present in the generated PGDB. Since superpathways can be part of other super-pathways, the decision to include or exclude a superpathway is performed recursively starting from the superpathways that do not include any child superpathways.An evaluation of the PathoLogic algorithm performed one year ago  using a As MetaCyc has grown in size in the last several years it has been a challenge to maintain the accuracy of PathoLogic. The introduction of key reactions and taxonomic range information in MetaCyc increased the accuracy of PathoLogic substantially. We have recently improved the speed of PathoLogic by approximately a factor of 5 to decrease the time it requires to process large metagenomics datasets. To our knowledge, the pathway prediction algorithms used by KEGG and Reactome have not been published. Therefore, we cannot compare our algorithm to theirs.PathoLogic can generate a “pathway evidence report” Web page that lists all pathways it has predicted in an organism and the evidence supporting each predicted pathway (example page  ). This MetaCyc is freely and openly available to all users. Pathway Tools runs on Linux, Windows, and Macintosh; it is freely available to academic users, and is available to commercial users for a fee. MetaCyc and Pathway Tools may be obtained from .


The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared.  Biomedical ontologies are being created to provide a semantic context for the molecular species and reactions that they contain. Current advances in both topics suggest an information integration cycle based on shared knowledge-bases, but because of different languages  spoken by the data sources and different abstraction levels, there is a lack of an overall frame capable of identifying overlaps and duplications . One canmodel, is a set of biochemical species whose evolution in time is determined by the reactions they participate in. These reactions, as well as the species, are specified in the SBML file. Reactions employ MathML [Biological processes can be effectively modeled by systems of ordinary differential equations (ODE). The equations themselves, along with their rate constants and initial conditions, need to be supplied to the ODE solver in a machine-readable format. A very popular standard format for describing the information content of models is the Systems Biology Markup Language (SBML) . The SBMy MathML  sectionsy MathML  have recdata access facility collects information about multiple pathways and existing biological databases.1. The parser module reads different file formats  and extracts relevant information.2. A core module assembles the knowledge, parsed from different sources, into a coherent ontology . This means that whenever a new modeling format is introduced, a new parser  can be connected to OREMP to interface with it as well. Similarly, different users can define different versions of the inPathway in Table kinetics in the meta-format) to achieve a common description that can be represented as a single ontology. The structure of this ontology is presented in Table A key part of this approach is the designed meta-format. Around the latter the information is collated and merged together while preserving model identity; meaning that all reactions coming from all models are collated together, but, despite such fusion, each reaction preserves internally the link to the original model file it belongs to , another computational step is taken:It is worth noting that, if we delete the link coming with the logic module computes N-order species set-set reachability of all the reactions within the loaded and aligned models .5. The In empirical models, as said for model repositories, the detection of duplicates is extremely important because (for instance) a duplicate reaction may lead to erroneous results. The duplicates are revealed to the user, allowing individuals to retain editorial power over their models. It also assists researchers in understanding how the resulting models of their work fit into models produced by others. The N-order reachability (duplicate reaction detection) among species-sets builds a reaction composition analysis by constructing a matrix which represents a directed graph. Each vertex is a set of species and each edge is a reaction, which abstracts the overall species-set connectivity. This graph does not become a multi-graph for each set of duplicate reactions (first-order duplicate) because only one reaction is taken as a representative of its duplicate reactions. Through this reachability computation, a dictionary of potentially equivalent reaction compositions is built: candidate paths of the same starting and ending sets of species, but involving alternative intermediate paths. Figure The extended ontology is exported in OWL2  and can The system has been tested in three real-world applications. (i) In a simple example, we demonstrated  the systhttp://cytosolve.mit.edu. System contribution to the integration of computational pathway models is the detection of duplicated reactions among different models . No matter the models chosen for simulation, once the species are aligned, the system identifies duplication problems in the reaction-models. From the user point of view this process is transparent: he/she receives a warning message that details the duplicated reactions and is prompted to confirm conflict elimination, and to resolve any inconsistency in reaction kinetic rate constants. What follows is an example outline of the process that starts at http://cytosolve.mit.edu and moves from isolated pathways to their coherent parallel solution, employing OREMP to detect reaction duplicates.This system is embedded in the latest release of Cytosolve , which cRemote Solving: Multiple Simulation begins• Cytosolve, step 1, Select Models: models BIOMD..1 and BIOMD..2 are selected• Step 2, Align Models: OREMP points out the overlaps among the two models, Figure 4• Step 3, Choose Initial Conditions: the user silences the reaction in conflict and possibly re-uploads BIOMD..1• Step 4, Simulate: the simulation takes place and the results are visualized, Figure 5• Step 5, The prototype can be executed on groups of arbitrary models within Cytosolve homepage, repeating Steps 1-5 above, and choosing Upload as model source.one minute on a dual-core 2 GHz Intel CPU. The previously described knowledge-discovery-step involving N-order reachability has been taken on these resources as well. For each species configuration in the database, all alternative circuit paths have been computed. This took about 2 hours on a quad-core 2 GHz AMD CPU and resulted in a dictionary of thousands "biological equivalent" circuits . The latter dictionary namely OREMPdb, is composed of:The system has been tested against the entire BioModels.net curated collection  that con• An ordered dictionary of pathway building blocks• The list of equivalent reactions overall used• All of the potentially equivalent N-order reaction compositionsEGF - 2 EGFR- GAP - Shc species can directly become 2 - GAP - Shc* EGF - EGFRor, alternatively, the former can first become EGF - 2 EGFRi- GAP - Shc, then 2 - GAP - ShcEGF - EGFRi*, and finally 2 - GAP - ShcEGF - EGFR*. This is just one and very simple example of the N-order analysis. In general, given two species-sets, OREMPdb provides all of the alternative ways to traverse from one to the other; as stated in the coming section, Protégé is the ideal tool to browse and query OREMPdb and perform this kind of queries.With this method the observed edge/vertex ratio for the BioModels.net curated DB is 1.19, similarly to other biological pathway databases - HumanCyc DB  has a raFrom a logic point of view, the system is constructed of three layers. The bottom layer represents the original biochemical pathways, read in their primitive format (such as SBML and CellML). The second layer abstracts (through the work-flow 1-6 detailed in §Methods) the pathways into a minimalistic and quantitative meta-format  coming from different sources.The system ensures a consistent merging of the resources, automatically aligning the species and showing the end-user possible duplications among reactions in the different pathways.Once the species alignment is done and duplicate reaction have been detected, the N-order reachability step is taken: for each reaction in each pathway the set of "alternative circuits" is computed. This means that given an arbitrary number of pathways, the system will identify all of the alternative ways to traverse from a species-set to another, employing all of the available reactions. In the last layer, all the information gathered is exported in OWL2, and Protégé is employed to visually edit, compare, and finalize the biochemical information exported. Protégé query interface allows the user to formulate "semantically-enabled" queries that were impractical when dealing with previously heterogeneous, unaligned models, Figure We described OREMP, but other tools are available too in the context of data integration; a major distinction in this context has to be done between those softwares that filter out the dynamics of computational pathway models, such as , and thoTo our knowledge, this is the first time that the information coming from different biological data sources has been aggregated into a single quantitative ontology. OREMP application can combine several pathways, merge and combine pathways, or revert to the original pathways, and inspect single-model details and employ external biological annotations. The system is independent of the different file formats in which the pathways are written and contains an extensible collection of parser modules. These advantages are fully transferred to Cytosolve, which employs this system to ensure semantically-correct parallel simulations. We selected OWL2 as export format and we adopted Protégé as default "Data Warehouse" for information storage, retrieval and reasoning; there OREMPdb, provides a single semantic access point to the whole Biomodel.net database of curated models .API: Application Programming Interface; BioModels: European Bioinformatics Institute Biological Model Database; BioPortal: National Center for Biomedical Ontology Biology Portal; CellML: Cell Markup Language; ChEBI: Chemical Entities of Biological Interest; EcoCyc: Encyclopedia of Escherichia coli K-12 MG1655 Genes and Metabolism; EGF: Epidermal Growth Factor; EGFR: Epidermal Growth Factor Receptor; GAP: RAS p21 Protein Activator 1; GO: Gene Ontology; HumanCyc: Encyclopedia of Homo sapiens Genes and Metabolism; KEGG: Kyoto Encyclopedia of Genes and Genomes; MAP kinase: Mitogen-Activated Protein kinase; MathML: Mathematical Markup Language; MIRIAM: Minimum Information Required In the Annotation of Models; MML: Mathematical Modeling Language; ODE: Ordinary Differential Equation; OREMPdb: Ontology Reasoning Engine for Molecular Pathways database; OREMP: Ontology Reasoning Engine for Molecular Pathways; OWL2: Web Ontology Language, version 2; RDF: Resource Description Framework; SBML: Systems Biology Markup Language; SHC: SHC Transforming Protein 2; UniProt: Universal Protein Resource; XML: Extensible Markup Language.The authors declare that they have no competing interests.RU developed the software with the supervision of GN, CFD conceived the work and supervised the entire project development. All authors read and approved the final manuscript.