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Martens M, Kreidl F, Ehrhart F, Jean D, Mei M, Mortensen HM, Nash A, Nymark P, Evelo CT, Cerciello F. A Community-Driven, Openly Accessible Molecular Pathway Integrating Knowledge on Malignant Pleural Mesothelioma. Front Oncol 2022; 12:849640. [PMID: 35558518 PMCID: PMC9088009 DOI: 10.3389/fonc.2022.849640] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/29/2022] [Indexed: 12/28/2022] Open
Abstract
Malignant pleural mesothelioma (MPM) is a highly aggressive malignancy mainly triggered by exposure to asbestos and characterized by complex biology. A significant body of knowledge has been generated over the decades by the research community which has improved our understanding of the disease toward prevention, diagnostic opportunities and new treatments. Omics technologies are opening for additional levels of information and hypotheses. Given the growing complexity and technological spread of biological knowledge in MPM, there is an increasing need for an integrating tool that may allow scientists to access the information and analyze data in a simple and interactive way. We envisioned that a platform to capture this widespread and fast-growing body of knowledge in a machine-readable and simple visual format together with tools for automated large-scale data analysis could be an important support for the work of the general scientist in MPM and for the community to share, critically discuss, distribute and eventually advance scientific results. Toward this goal, with the support of experts in the field and informed by existing literature, we have developed the first version of a molecular pathway model of MPM in the biological pathway database WikiPathways. This provides a visual and interactive overview of interactions and connections between the most central genes, proteins and molecular pathways known to be involved or altered in MPM. Currently, 455 unique genes and 247 interactions are included, derived after stringent manual curation of an initial 39 literature references. The pathway model provides a directly employable research tool with links to common databases and repositories for the exploration and the analysis of omics data. The resource is publicly available in the WikiPathways database (Wikipathways : WP5087) and continues to be under development and curation by the community, enabling the scientists in MPM to actively participate in the prioritization of shared biological knowledge.
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Affiliation(s)
- Marvin Martens
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Franziska Kreidl
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands.,Department of Bioinformatics - BiGCaT, MHeNs, Maastricht University, Maastricht, Netherlands
| | - Didier Jean
- Centre de Recherche des Cordeliers, Inserm, Sorbonne Université, Université de Paris, Functional Genomics of Solid Tumors, Paris, France
| | - Merlin Mei
- Oak Ridge Associated Universities, Research Triangle Park, Durham, NC, United States.,Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - Holly M Mortensen
- Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - Alistair Nash
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, WA, Australia
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands.,Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, Netherlands
| | - Ferdinando Cerciello
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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2
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Miller RA, Kutmon M, Bohler A, Waagmeester A, Evelo CT, Willighagen EL. Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions. PLoS One 2022; 17:e0263057. [PMID: 35436299 PMCID: PMC9015122 DOI: 10.1371/journal.pone.0263057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 01/11/2022] [Indexed: 11/22/2022] Open
Abstract
To grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of, for example, signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format making it easier for humans to interpret. Still, these diagrams can be represented in machine readable formats, as done in the KEGG, Reactome, and WikiPathways databases. However, while humans are good at interpreting the message of the creators of diagrams, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles which need harmonizing to offer semantically enriched access. Particularly challenging, here, are the interactions between the biological entities that underlie the biological causality. These interactions provide information about the biological process (metabolic conversion, inhibition, etc.), the direction, and the participating entities. Availability of the interactions in a semantic and harmonized format is essential for searching the full network of biological interactions. We here study how the graphically-modelled biological knowledge in diagrams can be semantified and harmonized, and exemplify how the resulting data is used to programmatically answer biological questions. We find that we can translate graphically modelled knowledge to a sufficient degree into a semantic model and discuss some of the current limitations. We then use this to show that reproducible notebooks can be used to explore up- and downstream targets of MECP2 and to analyse the sphingolipid metabolism. Our results demonstrate that most of the graphical biological knowledge from WikiPathways is modelled into the semantic layer with the semantic information intact and connectivity information preserved. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified.
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Affiliation(s)
- Ryan A. Miller
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- * E-mail:
| | - Martina Kutmon
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Anwesha Bohler
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Andra Waagmeester
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Micellio, Antwerp, Belgium
| | - Chris T. Evelo
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Egon L. Willighagen
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
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3
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Hanspers K, Kutmon M, Coort SL, Digles D, Dupuis LJ, Ehrhart F, Hu F, Lopes EN, Martens M, Pham N, Shin W, Slenter DN, Waagmeester A, Willighagen EL, Winckers LA, Evelo CT, Pico AR. Ten simple rules for creating reusable pathway models for computational analysis and visualization. PLoS Comput Biol 2021; 17:e1009226. [PMID: 34411100 PMCID: PMC8375987 DOI: 10.1371/journal.pcbi.1009226] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, California, United States of America
| | - Martina Kutmon
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Susan L. Coort
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Daniela Digles
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Lauren J. Dupuis
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Finterly Hu
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Elisson N. Lopes
- Instituto de Ciencias Biologicas, Departamento de Bioquimica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Marvin Martens
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Nhung Pham
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Woosub Shin
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Denise N. Slenter
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | | | - Egon L. Willighagen
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Laurent A. Winckers
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Chris T. Evelo
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, California, United States of America
- * E-mail:
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Hanspers K, Riutta A, Summer-Kutmon M, Pico AR. Pathway information extracted from 25 years of pathway figures. Genome Biol 2020; 21:273. [PMID: 33168034 PMCID: PMC7649569 DOI: 10.1186/s13059-020-02181-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/16/2020] [Indexed: 12/16/2022] Open
Abstract
Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figures published between 1995 and 2019 and extracted 1,112,551 instances of human genes, comprising 13,464 unique NCBI genes, participating in a wide variety of biological processes. This collection represents an order of magnitude more genes than found in the text of the same papers, and thousands of genes missing from other pathway databases, thus presenting new opportunities for discovery and research.
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Affiliation(s)
- Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
| | - Anders Riutta
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
| | - Martina Summer-Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.,Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA.
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5
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Ehrhart F, Janssen KJM, Coort SL, Evelo CT, Curfs LMG. Prader-Willi syndrome and Angelman syndrome: Visualisation of the molecular pathways for two chromosomal disorders. World J Biol Psychiatry 2019; 20:670-682. [PMID: 29425059 DOI: 10.1080/15622975.2018.1439594] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Objectives: Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are two syndromes that are caused by the same chromosomal deletion on 15q11.2-q13. Due to methylation patterns, different genes are responsible for the two distinct phenotypes resulting in the disorders. Patients of both disorders exhibit hypotonia in neonatal stage, delay in development and hypopigmentation. Typical features for PWS include hyperphagia, which leads to obesity, the major cause of mortality, and hypogonadism. In AS, patients suffer from a more severe developmental delay, they have a distinctive behaviour that is often described as unnaturally happy, and a tendency for epileptic seizures. For both syndromes, we identified and visualised molecular downstream pathways of the deleted genes that could give insight on the development of the clinical features.Methods: This was done by consulting literature, genome browsers and pathway databases to identify molecular interactions and to construct downstream pathways.Results: A pathway visualisation was created and uploaded to the open pathway database WikiPathways covering all molecular pathways that were found.Conclusions: The visualisation of the downstream pathways of PWS- and AS-deleted genes shows that some of the typical symptoms are caused by multiple genes and reveals critical gaps in the current knowledge.
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Affiliation(s)
- Friederike Ehrhart
- GCK, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Kelly J M Janssen
- GCK, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Susan L Coort
- Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Chris T Evelo
- GCK, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Leopold M G Curfs
- Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
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6
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Miller RA, Woollard P, Willighagen EL, Digles D, Kutmon M, Loizou A, Waagmeester A, Senger S, Evelo CT. Explicit interaction information from WikiPathways in RDF facilitates drug discovery in the Open PHACTS Discovery Platform. F1000Res 2018; 7:75. [PMID: 30416713 PMCID: PMC6206606 DOI: 10.12688/f1000research.13197.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/24/2018] [Indexed: 12/11/2022] Open
Abstract
Open PHACTS is a pre-competitive project to answer scientific questions developed recently by the pharmaceutical industry. Having high quality biological interaction information in the Open PHACTS Discovery Platform is needed to answer multiple pathway related questions. To address this, updated WikiPathways data has been added to the platform. This data includes information about biological interactions, such as stimulation and inhibition. The platform's Application Programming Interface (API) was extended with appropriate calls to reference these interactions. These new methods of the Open PHACTS API are available now.
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Affiliation(s)
- Ryan A Miller
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands
| | | | - Egon L Willighagen
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands
| | - Daniela Digles
- Pharmacoinformatics Research Group, Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Martina Kutmon
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands.,Maastricht Center for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | | | - Andra Waagmeester
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands.,Micelio, Antwerp, Belgium
| | | | - Chris T Evelo
- Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands.,Maastricht Center for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.,Open PHACTS Foundation, Science Park, Cambridge, UK
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7
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Dawood M, Hamdoun S, Efferth T. Multifactorial Modes of Action of Arsenic Trioxide in Cancer Cells as Analyzed by Classical and Network Pharmacology. Front Pharmacol 2018; 9:143. [PMID: 29535630 PMCID: PMC5835320 DOI: 10.3389/fphar.2018.00143] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 02/09/2018] [Indexed: 12/13/2022] Open
Abstract
Arsenic trioxide is a traditional remedy in Chinese Medicine since ages. Nowadays, it is clinically used to treat acute promyelocytic leukemia (APL) by targeting PML/RARA. However, the drug's activity is broader and the mechanisms of action in other tumor types remain unclear. In this study, we investigated molecular modes of action by classical and network pharmacological approaches. CEM/ADR5000 resistance leukemic cells were similar sensitive to As2O3 as their wild-type counterpart CCRF-CEM (resistance ratio: 1.88). Drug-resistant U87.MG ΔEGFR glioblastoma cells harboring mutated epidermal growth factor receptor were even more sensitive (collateral sensitive) than wild-type U87.MG cells (resistance ratio: 0.33). HCT-116 colon carcinoma p53-/- knockout cells were 7.16-fold resistant toward As2O3 compared to wild-type cells. Forty genes determining cellular responsiveness to As2O3 were identified by microarray and COMPARE analyses in 58 cell lines of the NCI panel. Hierarchical cluster analysis-based heat mapping revealed significant differences between As2O3 sensitive cell lines and resistant cell lines with p-value: 1.86 × 10-5. The genes were subjected to Galaxy Cistrome gene promoter transcription factor analysis to predict the binding of transcription factors. We have exemplarily chosen NF-kB and AP-1, and indeed As2O3 dose-dependently inhibited the promoter activity of these two transcription factors in reporter cell lines. Furthermore, the genes identified here and those published in the literature were assembled and subjected to Ingenuity Pathway Analysis for comprehensive network pharmacological approaches that included all known factors of resistance of tumor cells to As2O3. In addition to pathways related to the anticancer effects of As2O3, several neurological pathways were identified. As arsenic is well-known to exert neurotoxicity, these pathways might account for neurological side effects. In conclusion, the activity of As2O3 is not restricted to acute promyelocytic leukemia. In addition to PML/RARA, numerous other genes belonging to diverse functional classes may also contribute to its cytotoxicity. Network pharmacology is suited to unravel the multifactorial modes of action of As2O3.
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Affiliation(s)
| | | | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Mainz, Germany
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8
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Luna A, McFadden GB, Aladjem MI, Kohn KW. Predicted Role of NAD Utilization in the Control of Circadian Rhythms during DNA Damage Response. PLoS Comput Biol 2015; 11:e1004144. [PMID: 26020938 PMCID: PMC4462596 DOI: 10.1371/journal.pcbi.1004144] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Accepted: 01/20/2015] [Indexed: 02/06/2023] Open
Abstract
The circadian clock is a set of regulatory steps that oscillate with a period of approximately 24 hours influencing many biological processes. These oscillations are robust to external stresses, and in the case of genotoxic stress (i.e. DNA damage), the circadian clock responds through phase shifting with primarily phase advancements. The effect of DNA damage on the circadian clock and the mechanism through which this effect operates remains to be thoroughly investigated. Here we build an in silico model to examine damage-induced circadian phase shifts by investigating a possible mechanism linking circadian rhythms to metabolism. The proposed model involves two DNA damage response proteins, SIRT1 and PARP1, that are each consumers of nicotinamide adenine dinucleotide (NAD), a metabolite involved in oxidation-reduction reactions and in ATP synthesis. This model builds on two key findings: 1) that SIRT1 (a protein deacetylase) is involved in both the positive (i.e. transcriptional activation) and negative (i.e. transcriptional repression) arms of the circadian regulation and 2) that PARP1 is a major consumer of NAD during the DNA damage response. In our simulations, we observe that increased PARP1 activity may be able to trigger SIRT1-induced circadian phase advancements by decreasing SIRT1 activity through competition for NAD supplies. We show how this competitive inhibition may operate through protein acetylation in conjunction with phosphorylation, consistent with reported observations. These findings suggest a possible mechanism through which multiple perturbations, each dominant during different points of the circadian cycle, may result in the phase advancement of the circadian clock seen during DNA damage. Many physiological processes are regulated by the circadian clock, and we are continuing to learn about the role of the circadian clock in disease. Research in recent years has begun to shed light on the feedback mechanisms that exist between circadian regulation and other processes, including metabolism and the response to DNA damage. A challenge has been to understand the dynamic nature of the protein interactions of these processes, which often involve protein modification as a means of communicating cellular states, such as damaged DNA. Here we have devised a model that simulates an alteration of the circadian clock that is observed during DNA damage response. A novel aspect of this model is the inclusion of SIRT1, a protein that regulates core circadian proteins through modification and helps to repress gene expression. SIRT1 is dependent on a metabolite regulated by the circadian clock and is depleted during DNA damage. In conjunction with a second form of protein modification, our results suggest that multiple forms of protein modification may contribute to the experimentally observed alterations to circadian function.
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Affiliation(s)
- Augustin Luna
- Laboratory of Molecular Pharmacology, National Cancer Institute, Bethesda, Maryland, United States of America
- Department of Bioinformatics, Boston University, Boston, Massachusetts, United States of America
- * E-mail:
| | - Geoffrey B. McFadden
- Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
| | - Mirit I. Aladjem
- Laboratory of Molecular Pharmacology, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Kurt W. Kohn
- Laboratory of Molecular Pharmacology, National Cancer Institute, Bethesda, Maryland, United States of America
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9
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Cheng HC, Angermann BR, Zhang F, Meier-Schellersheim M. NetworkViewer: visualizing biochemical reaction networks with embedded rendering of molecular interaction rules. BMC SYSTEMS BIOLOGY 2014; 8:70. [PMID: 24934175 PMCID: PMC4094451 DOI: 10.1186/1752-0509-8-70] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Accepted: 06/05/2014] [Indexed: 01/01/2023]
Abstract
Background Network representations of cell-biological signaling processes frequently contain large numbers of interacting molecular and multi-molecular components that can exist in, and switch between, multiple biochemical and/or structural states. In addition, the interaction categories (associations, dissociations and transformations) in such networks cannot satisfactorily be mapped onto simple arrows connecting pairs of components since their specifications involve information such as reaction rates and conditions with regard to the states of the interacting components. This leads to the challenge of having to reconcile competing objectives: providing a high-level overview without omitting relevant information, and showing interaction specifics while not overwhelming users with too much detail displayed simultaneously. This problem is typically addressed by splitting the information required to understand a reaction network model into several categories that are rendered separately through combinations of visualizations and/or textual and tabular elements, requiring modelers to consult several sources to obtain comprehensive insights into the underlying assumptions of the model. Results We report the development of an application, the Simmune NetworkViewer, that visualizes biochemical reaction networks using iconographic representations of protein interactions and the conditions under which the interactions take place using the same symbols that were used to specify the underlying model with the Simmune Modeler. This approach not only provides a coherent model representation but, moreover, following the principle of “overview first, zoom and filter, then details-on-demand,” can generate an overview visualization of the global network and, upon user request, presents more detailed views of local sub-networks and the underlying reaction rules for selected interactions. This visual integration of information would be difficult to achieve with static network representations or approaches that use scripted model specifications without offering simple but detailed symbolic representations of molecular interactions, their conditions and consequences in terms of biochemical modifications. Conclusions The Simmune NetworkViewer provides concise, yet comprehensive visualizations of reaction networks created in the Simmune framework. In the near future, by adopting the upcoming SBML standard for encoding multi-component, multi-state molecular complexes and their interactions as input, the NetworkViewer will, moreover, be able to offer such visualization for any rule-based model that can be exported to that standard.
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Affiliation(s)
- Hsueh-Chien Cheng
- Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Building 4, 4 Memorial Drive, 20892 Bethesda, USA.
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10
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Luna A, Aladjem MI, Kohn KW. SIRT1/PARP1 crosstalk: connecting DNA damage and metabolism. Genome Integr 2013; 4:6. [PMID: 24360018 PMCID: PMC3898398 DOI: 10.1186/2041-9414-4-6] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Accepted: 12/02/2013] [Indexed: 12/20/2022] Open
Abstract
An intricate network regulates the activities of SIRT1 and PARP1 proteins and continues to be uncovered. Both SIRT1 and PARP1 share a common co-factor nicotinamide adenine dinucleotide (NAD+) and several common substrates, including regulators of DNA damage response and circadian rhythms. We review this complex network using an interactive Molecular Interaction Map (MIM) to explore the interplay between these two proteins. Here we discuss how NAD + competition and post-transcriptional/translational feedback mechanisms create a regulatory network sensitive to environmental cues, such as genotoxic stress and metabolic states, and examine the role of those interactions in DNA repair and ultimately, cell fate decisions.
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Affiliation(s)
- Augustin Luna
- Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
- Bioinformatics Program, Boston University, Boston, MA 02215, USA
| | - Mirit I Aladjem
- Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Kurt W Kohn
- Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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11
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Fried JY, van Iersel MP, Aladjem MI, Kohn KW, Luna A. PathVisio-Faceted Search: an exploration tool for multi-dimensional navigation of large pathways. Bioinformatics 2013; 29:1465-6. [PMID: 23547033 DOI: 10.1093/bioinformatics/btt146] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
PURPOSE The PathVisio-Faceted Search plugin helps users explore and understand complex pathways by overlaying experimental data and data from webservices, such as Ensembl BioMart, onto diagrams drawn using formalized notations in PathVisio. The plugin then provides a filtering mechanism, known as a faceted search, to find and highlight diagram nodes (e.g. genes and proteins) of interest based on imported data. The tool additionally provides a flexible scripting mechanism to handle complex queries. AVAILABILITY The PathVisio-Faceted Search plugin is compatible with PathVisio 3.0 and above. PathVisio is compatible with Windows, Mac OS X and Linux. The plugin, documentation, example diagrams and Groovy scripts are available at http://PathVisio.org/wiki/PathVisioFacetedSearchHelp. The plugin is free, open-source and licensed by the Apache 2.0 License.
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Affiliation(s)
- Jake Y Fried
- Computer Engineering, University of Maryland, College Park, MD 20740, USA.
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12
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De Ambrosi C, Barla A, Tortolina L, Castagnino N, Pesenti R, Verri A, Ballestrero A, Patrone F, Parodi S. Parameter space exploration within dynamic simulations of signaling networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2013; 10:103-120. [PMID: 23311364 DOI: 10.3934/mbe.2013.10.103] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.
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Affiliation(s)
- Cristina De Ambrosi
- DIBRIS Department of Informatics, Bioengineering, Robotics and Systems Engineering, Universita degli Studi di Genova - Via Balbi, 5 - 16126 Genova, Italy.
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van Iersel MP, Villéger AC, Czauderna T, Boyd SE, Bergmann FT, Luna A, Demir E, Sorokin A, Dogrusoz U, Matsuoka Y, Funahashi A, Aladjem MI, Mi H, Moodie SL, Kitano H, Le Novère N, Schreiber F. Software support for SBGN maps: SBGN-ML and LibSBGN. Bioinformatics 2012; 28:2016-21. [PMID: 22581176 PMCID: PMC3400951 DOI: 10.1093/bioinformatics/bts270] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Motivation: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. Availability and implementation: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net. Contact:sbgn-libsbgn@lists.sourceforge.net
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Chandan K, van Iersel MP, Aladjem MI, Kohn KW, Luna A. PathVisio-Validator: a rule-based validation plugin for graphical pathway notations. ACTA ACUST UNITED AC 2011; 28:889-90. [PMID: 22199389 DOI: 10.1093/bioinformatics/btr694] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
PURPOSE The PathVisio-Validator plugin aims to simplify the task of producing biological pathway diagrams that follow graphical standardized notations, such as Molecular Interaction Maps or the Systems Biology Graphical Notation. This plugin assists in the creation of pathway diagrams by ensuring correct usage of a notation, and thereby reducing ambiguity when diagrams are shared among biologists. Rulesets, needed in the validation process, can be generated for any graphical notation that a developer desires, using either Schematron or Groovy. The plugin also provides support for filtering validation results, validating on a subset of rules, and distinguishing errors and warnings.
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Affiliation(s)
- Kumar Chandan
- Keshav Memorial Institute of Technology, Hyderabad, Andhra Pradesh, India.
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Machado D, Costa RS, Rocha M, Ferreira EC, Tidor B, Rocha I. Modeling formalisms in Systems Biology. AMB Express 2011; 1:45. [PMID: 22141422 PMCID: PMC3285092 DOI: 10.1186/2191-0855-1-45] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2011] [Accepted: 12/05/2011] [Indexed: 12/18/2022] Open
Abstract
Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.
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Affiliation(s)
- Daniel Machado
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Rafael S Costa
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Miguel Rocha
- Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Eugénio C Ferreira
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Bruce Tidor
- Department of Biological Engineering/Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Isabel Rocha
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
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Chylek LA, Hu B, Blinov ML, Emonet T, Faeder JR, Goldstein B, Gutenkunst RN, Haugh JM, Lipniacki T, Posner RG, Yang J, Hlavacek WS. Guidelines for visualizing and annotating rule-based models. MOLECULAR BIOSYSTEMS 2011; 7:2779-95. [PMID: 21647530 PMCID: PMC3168731 DOI: 10.1039/c1mb05077j] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Rule-based modeling provides a means to represent cell signaling systems in a way that captures site-specific details of molecular interactions. For rule-based models to be more widely understood and (re)used, conventions for model visualization and annotation are needed. We have developed the concepts of an extended contact map and a model guide for illustrating and annotating rule-based models. An extended contact map represents the scope of a model by providing an illustration of each molecule, molecular component, direct physical interaction, post-translational modification, and enzyme-substrate relationship considered in a model. A map can also illustrate allosteric effects, structural relationships among molecular components, and compartmental locations of molecules. A model guide associates elements of a contact map with annotation and elements of an underlying model, which may be fully or partially specified. A guide can also serve to document the biological knowledge upon which a model is based. We provide examples of a map and guide for a published rule-based model that characterizes early events in IgE receptor (FcεRI) signaling. We also provide examples of how to visualize a variety of processes that are common in cell signaling systems but not considered in the example model, such as ubiquitination. An extended contact map and an associated guide can document knowledge of a cell signaling system in a form that is visual as well as executable. As a tool for model annotation, a map and guide can communicate the content of a model clearly and with precision, even for large models.
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Affiliation(s)
- Lily A Chylek
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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Luna A, Sunshine ML, van Iersel MP, Aladjem MI, Kohn KW. PathVisio-MIM: PathVisio plugin for creating and editing Molecular Interaction Maps (MIMs). ACTA ACUST UNITED AC 2011; 27:2165-6. [PMID: 21636591 DOI: 10.1093/bioinformatics/btr336] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION A plugin for the Java-based PathVisio pathway editor has been developed to help users draw diagrams of bioregulatory networks according to the Molecular Interaction Map (MIM) notation. Together with the core PathVisio application, this plugin presents a simple to use and cross-platform application for the construction of complex MIM diagrams with the ability to annotate diagram elements with comments, literature references and links to external databases. This tool extends the capabilities of the PathVisio pathway editor by providing both MIM-specific glyphs and support for a MIM-specific markup language file format for exchange with other MIM-compatible tools and diagram validation. AVAILABILITY The PathVisio-MIM plugin is freely available and works with versions of PathVisio 2.0.11 and later on Windows, Mac OS X and Linux. Information about MIM notation and the MIMML format is available at http://discover.nci.nih.gov/mim. The plugin, along with diagram examples, instructions and Java source code, may be downloaded at http://discover.nci.nih.gov/mim/mim_pathvisio.html.
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Affiliation(s)
- Augustin Luna
- Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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