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A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks. PLoS One 2015; 10:e0130033. [PMID: 26067297 PMCID: PMC4489432 DOI: 10.1371/journal.pone.0130033] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Accepted: 05/15/2015] [Indexed: 12/29/2022] Open
Abstract
Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour.
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102
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Rodriguez A, Crespo I, Fournier A, del Sol A. Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET. PLoS One 2015; 10:e0127216. [PMID: 26058016 PMCID: PMC4461287 DOI: 10.1371/journal.pone.0127216] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 04/13/2015] [Indexed: 01/09/2023] Open
Abstract
High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell's response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.
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Affiliation(s)
- Ana Rodriguez
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Isaac Crespo
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Anna Fournier
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
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Abstract
Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.
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Affiliation(s)
- Nicolas Le Novère
- Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
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104
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Naldi A, Monteiro PT, Müssel C, Kestler HA, Thieffry D, Xenarios I, Saez-Rodriguez J, Helikar T, Chaouiya C. Cooperative development of logical modelling standards and tools with CoLoMoTo. Bioinformatics 2015; 31:1154-9. [PMID: 25619997 DOI: 10.1093/bioinformatics/btv013] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Accepted: 01/05/2015] [Indexed: 01/17/2023] Open
Abstract
The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments.
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Affiliation(s)
- Aurélien Naldi
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | - Pedro T Monteiro
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | - Christoph Müssel
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | | | - Hans A Kestler
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Res
| | - Denis Thieffry
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | - Ioannis Xenarios
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | - Julio Saez-Rodriguez
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | - Tomas Helikar
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | - Claudine Chaouiya
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland, Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Lisbon, Portugal, Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal, Medical Systems Biology, Ulm University, Germany, Friedrich-Schiller University Jena, Jena, Germany, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany, Institut de Biologie de l'École Normale Supérieure (IBENS)-UMR CNRS 8197-INSERM 1024, Paris, France, Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom and Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
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105
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Vaga S, Bernardo-Faura M, Cokelaer T, Maiolica A, Barnes CA, Gillet LC, Hegemann B, van Drogen F, Sharifian H, Klipp E, Peter M, Saez-Rodriguez J, Aebersold R. Phosphoproteomic analyses reveal novel cross-modulation mechanisms between two signaling pathways in yeast. Mol Syst Biol 2014; 10:767. [PMID: 25492886 PMCID: PMC4300490 DOI: 10.15252/msb.20145112] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Cells respond to environmental stimuli via specialized signaling pathways. Concurrent stimuli trigger multiple pathways that integrate information, predominantly via protein phosphorylation. Budding yeast responds to NaCl and pheromone via two mitogen-activated protein kinase cascades, the high osmolarity, and the mating pathways, respectively. To investigate signal integration between these pathways, we quantified the time-resolved phosphorylation site dynamics after pathway co-stimulation. Using shotgun mass spectrometry, we quantified 2,536 phosphopeptides across 36 conditions. Our data indicate that NaCl and pheromone affect phosphorylation events within both pathways, which thus affect each other at more levels than anticipated, allowing for information exchange and signal integration. We observed a pheromone-induced down-regulation of Hog1 phosphorylation due to Gpd1, Ste20, Ptp2, Pbs2, and Ptc1. Distinct Ste20 and Pbs2 phosphosites responded differently to the two stimuli, suggesting these proteins as key mediators of the information exchange. A set of logic models was then used to assess the role of measured phosphopeptides in the crosstalk. Our results show that the integration of the response to different stimuli requires complex interconnections between signaling pathways.
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Affiliation(s)
- Stefania Vaga
- Department of Biology, Institute of Molecular Systems Biology ETH Zürich, Zürich, Switzerland
| | - Marti Bernardo-Faura
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Cambridge, UK
| | - Thomas Cokelaer
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Cambridge, UK
| | - Alessio Maiolica
- Department of Biology, Institute of Molecular Systems Biology ETH Zürich, Zürich, Switzerland
| | - Christopher A Barnes
- Department of Biology, Institute of Molecular Systems Biology ETH Zürich, Zürich, Switzerland Department of Biology, Institute of Biochemistry, ETH Zürich, Zürich, Switzerland
| | - Ludovic C Gillet
- Department of Biology, Institute of Molecular Systems Biology ETH Zürich, Zürich, Switzerland
| | - Björn Hegemann
- Department of Biology, Institute of Biochemistry, ETH Zürich, Zürich, Switzerland
| | - Frank van Drogen
- Department of Biology, Institute of Biochemistry, ETH Zürich, Zürich, Switzerland
| | - Hoda Sharifian
- Department of Biology, Institute of Biochemistry, ETH Zürich, Zürich, Switzerland
| | - Edda Klipp
- Department of Biology, Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Matthias Peter
- Department of Biology, Institute of Biochemistry, ETH Zürich, Zürich, Switzerland
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Cambridge, UK
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology ETH Zürich, Zürich, Switzerland Faculty of Science, University of Zurich, Zurich, Switzerland
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106
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Dräger A, Palsson BØ. Improving collaboration by standardization efforts in systems biology. Front Bioeng Biotechnol 2014; 2:61. [PMID: 25538939 PMCID: PMC4259112 DOI: 10.3389/fbioe.2014.00061] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 11/14/2014] [Indexed: 11/17/2022] Open
Abstract
Collaborative genome-scale reconstruction endeavors of metabolic networks would not be possible without a common, standardized formal representation of these systems. The ability to precisely define biological building blocks together with their dynamic behavior has even been considered a prerequisite for upcoming synthetic biology approaches. Driven by the requirements of such ambitious research goals, standardization itself has become an active field of research on nearly all levels of granularity in biology. In addition to the originally envisaged exchange of computational models and tool interoperability, new standards have been suggested for an unambiguous graphical display of biological phenomena, to annotate, archive, as well as to rank models, and to describe execution and the outcomes of simulation experiments. The spectrum now even covers the interaction of entire neurons in the brain, three-dimensional motions, and the description of pharmacometric studies. Thereby, the mathematical description of systems and approaches for their (repeated) simulation are clearly separated from each other and also from their graphical representation. Minimum information definitions constitute guidelines and common operation protocols in order to ensure reproducibility of findings and a unified knowledge representation. Central database infrastructures have been established that provide the scientific community with persistent links from model annotations to online resources. A rich variety of open-source software tools thrives for all data formats, often supporting a multitude of programing languages. Regular meetings and workshops of developers and users lead to continuous improvement and ongoing development of these standardization efforts. This article gives a brief overview about the current state of the growing number of operation protocols, mark-up languages, graphical descriptions, and fundamental software support with relevance to systems biology.
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Affiliation(s)
- Andreas Dräger
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Cognitive Systems, Center for Bioinformatics Tübingen (ZBIT), Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Bernhard Ø. Palsson
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
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107
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Weiss MS, Peñalver Bernabé B, Shin S, Asztalos S, Dubbury SJ, Mui MD, Bellis AD, Bluver D, Tonetti DA, Saez-Rodriguez J, Broadbelt LJ, Jeruss JS, Shea LD. Dynamic transcription factor activity and networks during ErbB2 breast oncogenesis and targeted therapy. Integr Biol (Camb) 2014; 6:1170-82. [PMID: 25303361 PMCID: PMC4237672 DOI: 10.1039/c4ib00086b] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Tissue development and disease progression are multi-stage processes controlled by an evolving set of key regulatory factors, and identifying these factors necessitates a dynamic analysis spanning relevant time scales. Current omics approaches depend on incomplete biological databases to identify critical cellular processes. Herein, we present TRACER (TRanscriptional Activity CEll aRrays), which was employed to quantify the dynamic activity of numerous transcription factor (TFs) simultaneously in 3D and networks for TRACER (NTRACER), a computational algorithm that allows for cellular rewiring to establish dynamic regulatory networks based on activity of TF reporter constructs. We identified major hubs at various stages of culture associated with normal and abnormal tissue growth (i.e., ELK-1 and E2F1, respectively) and the mechanism of action for a targeted therapeutic, lapatinib, through GATA-1, which were confirmed in human ErbB2 positive breast cancer patients and human ErbB2 positive breast cancer cell lines that were either sensitive or resistant to lapatinib.
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Affiliation(s)
- M S Weiss
- Chemical and Biological Engineering Department, Northwestern University, Evanston, IL, USA.
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108
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Trairatphisan P, Mizera A, Pang J, Tantar AA, Sauter T. optPBN: an optimisation toolbox for probabilistic Boolean networks. PLoS One 2014; 9:e98001. [PMID: 24983623 PMCID: PMC4077690 DOI: 10.1371/journal.pone.0098001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2013] [Accepted: 04/27/2014] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks. RESULTS We introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers. In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network. SUMMARY The optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks.
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Affiliation(s)
- Panuwat Trairatphisan
- Systems Biology Group, Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg
| | - Andrzej Mizera
- Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg, Luxembourg
| | - Jun Pang
- Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg, Luxembourg
- Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg
| | - Alexandru Adrian Tantar
- Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg
| | - Thomas Sauter
- Systems Biology Group, Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg
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109
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von der Heyde S, Bender C, Henjes F, Sonntag J, Korf U, Beißbarth T. Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines. BMC SYSTEMS BIOLOGY 2014; 8:75. [PMID: 24970389 PMCID: PMC4087127 DOI: 10.1186/1752-0509-8-75] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Accepted: 06/10/2014] [Indexed: 12/19/2022]
Abstract
Background Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging. The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitor erlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated protein signalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds in case of overexpression or mutations. Dimerisation with other receptors allows to bypass pathway blockades. Our intention is to reconstruct the ErbB network to reveal resistance mechanisms. We used longitudinal proteomic data of ErbB receptors and downstream targets in the ErbB-2 amplified breast cancer cell lines BT474, SKBR3 and HCC1954 treated with erlotinib, trastuzumab or pertuzumab, alone or combined, up to 60 minutes and 30 hours, respectively. In a Boolean modelling approach, signalling networks were reconstructed based on these data in a cell line and time course specific manner, including prior literature knowledge. Finally, we simulated network response to inhibitor combinations to detect signalling nodes reflecting growth inhibition. Results The networks pointed to cell line specific activation patterns of the MAPK and PI3K pathway. In BT474, the PI3K signal route was favoured, while in SKBR3, novel edges highlighted MAPK signalling. In HCC1954, the inferred edges stimulated both pathways. For example, we uncovered feedback loops amplifying PI3K signalling, in line with the known trastuzumab resistance of this cell line. In the perturbation simulations on the short-term networks, we analysed ERK1/2, AKT and p70S6K. The results indicated a pathway specific drug response, driven by the type of growth factor stimulus. HCC1954 revealed an edgetic type of PIK3CA-mutation, contributing to trastuzumab inefficacy. Drug impact on the AKT and ERK1/2 signalling axes is mirrored by effects on RB and RPS6, relating to phenotypic events like cell growth or proliferation. Therefore, we additionally analysed RB and RPS6 in the long-term networks. Conclusions We derived protein interaction models for three breast cancer cell lines. Changes compared to the common reference network hint towards individual characteristics and potential drug resistance mechanisms. Simulation of perturbations were consistent with the experimental data, confirming our combined reverse and forward engineering approach as valuable for drug discovery and personalised medicine.
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Affiliation(s)
| | | | | | | | | | - Tim Beißbarth
- Statistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
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Egea JA, Henriques D, Cokelaer T, Villaverde AF, MacNamara A, Danciu DP, Banga JR, Saez-Rodriguez J. MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics. BMC Bioinformatics 2014; 15:136. [PMID: 24885957 PMCID: PMC4025564 DOI: 10.1186/1471-2105-15-136] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Accepted: 04/24/2014] [Indexed: 11/28/2022] Open
Abstract
Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.
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Affiliation(s)
| | | | | | | | | | | | - Julio R Banga
- (Bio)Process Engineering Group, Spanish National Research Council, IIM-CSIC, 36208 Vigo, Spain.
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Li S, Kang L, Zhao XM. A survey on evolutionary algorithm based hybrid intelligence in bioinformatics. BIOMED RESEARCH INTERNATIONAL 2014; 2014:362738. [PMID: 24729969 PMCID: PMC3963368 DOI: 10.1155/2014/362738] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Revised: 01/29/2014] [Accepted: 01/29/2014] [Indexed: 11/18/2022]
Abstract
With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.
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Affiliation(s)
- Shan Li
- Department of Mathematics, Shanghai University, Shanghai 200444, China
| | - Liying Kang
- Department of Mathematics, Shanghai University, Shanghai 200444, China
| | - Xing-Ming Zhao
- Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
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112
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Santra T, Kolch W, Kholodenko BN. Navigating the multilayered organization of eukaryotic signaling: a new trend in data integration. PLoS Comput Biol 2014; 10:e1003385. [PMID: 24550716 PMCID: PMC3923657 DOI: 10.1371/journal.pcbi.1003385] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The ever-increasing capacity of biological molecular data acquisition outpaces our ability to understand the meaningful relationships between molecules in a cell. Multiple databases were developed to store and organize these molecular data. However, emerging fundamental questions about concerted functions of these molecules in hierarchical cellular networks are poorly addressed. Here we review recent advances in the development of publically available databases that help us analyze the signal integration and processing by multilayered networks that specify biological responses in model organisms and human cells
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Affiliation(s)
- Tapesh Santra
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
| | - Walter Kolch
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland
- School of Medicine and Medical Science, University College Dublin, Belfield, Dublin, Ireland
| | - Boris N. Kholodenko
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland
- School of Medicine and Medical Science, University College Dublin, Belfield, Dublin, Ireland
- * E-mail:
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113
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Kowald A, Klipp E. Mathematical models of mitochondrial aging and dynamics. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2014; 127:63-92. [PMID: 25149214 DOI: 10.1016/b978-0-12-394625-6.00003-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Research on the role of mitochondria in aging and disease is rapidly growing. Furthermore, in recent years, it also became clear that mitochondria are dynamic structures undergoing constant and rapid cycles of fusion and fission. The involvement of mitochondria in multiple complex processes makes them a prime target for mathematical and computational modeling. This review consists of two parts. In the first (Section 2), we provide a detailed introduction to the underlying concepts of mathematical modeling to help the reader who is not so familiar with these techniques to judge the requirements and results that can be obtained through modeling. In the second part (Section 3), we review existing mathematical and computational models that investigate mitochondrial dynamics and the role of mitochondria for the aging process.
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Affiliation(s)
- Axel Kowald
- Theoretical Biophysics, Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Edda Klipp
- Theoretical Biophysics, Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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Chaouiya C, Bérenguier D, Keating SM, Naldi A, van Iersel MP, Rodriguez N, Dräger A, Büchel F, Cokelaer T, Kowal B, Wicks B, Gonçalves E, Dorier J, Page M, Monteiro PT, von Kamp A, Xenarios I, de Jong H, Hucka M, Klamt S, Thieffry D, Le Novère N, Saez-Rodriguez J, Helikar T. SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. BMC SYSTEMS BIOLOGY 2013; 7:135. [PMID: 24321545 PMCID: PMC3892043 DOI: 10.1186/1752-0509-7-135] [Citation(s) in RCA: 101] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Accepted: 11/26/2013] [Indexed: 05/03/2023]
Abstract
BACKGROUND Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.
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Affiliation(s)
- Claudine Chaouiya
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
| | - Duncan Bérenguier
- Institut de Mathématiques de Luminy, Campus de Luminy, Case 907, 13288 Marseille Cedex 9, France
| | - Sarah M Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Aurélien Naldi
- Center for Integrative Genomics, University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Martijn P van Iersel
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nicolas Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Andreas Dräger
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany
| | - Finja Büchel
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany
| | - Thomas Cokelaer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Bryan Kowal
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Benjamin Wicks
- College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Emanuel Gonçalves
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Julien Dorier
- Swiss-Prot & Vital-IT group, SIB- Swiss Institute of Bioinformatics, Center for Integrative Genomics, University of Lausanne, Quartier Sorge - Batiment Genopode, CH-1015 Lausanne, Switzerland
| | - Michel Page
- INRIA Grenoble – Rhône-Alpes, 655 avenue de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France
- IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, 38040 Grenoble Cedex 9, France
| | - Pedro T Monteiro
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
- Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento (INESC-ID), Rua Alves Redol 9, 1000-029 Lisbon, Portugal
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Ioannis Xenarios
- Swiss-Prot & Vital-IT group, SIB- Swiss Institute of Bioinformatics, Center for Integrative Genomics, University of Lausanne, Quartier Sorge - Batiment Genopode, CH-1015 Lausanne, Switzerland
| | - Hidde de Jong
- INRIA Grenoble – Rhône-Alpes, 655 avenue de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France
| | - Michael Hucka
- Computing and Mathematical sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Denis Thieffry
- Institut de Biologie de l’Ecole Normale Supérieure (IBENS) - UMR CNRS 8197 - INSERM 1024 46 rue d’Ulm, 75230 Paris Cedex 05, France
| | - Nicolas Le Novère
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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Büchel F, Rodriguez N, Swainston N, Wrzodek C, Czauderna T, Keller R, Mittag F, Schubert M, Glont M, Golebiewski M, van Iersel M, Keating S, Rall M, Wybrow M, Hermjakob H, Hucka M, Kell DB, Müller W, Mendes P, Zell A, Chaouiya C, Saez-Rodriguez J, Schreiber F, Laibe C, Dräger A, Le Novère N. Path2Models: large-scale generation of computational models from biochemical pathway maps. BMC SYSTEMS BIOLOGY 2013; 7:116. [PMID: 24180668 PMCID: PMC4228421 DOI: 10.1186/1752-0509-7-116] [Citation(s) in RCA: 126] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2013] [Accepted: 10/23/2013] [Indexed: 11/10/2022]
Abstract
BACKGROUND Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data. RESULTS To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps. CONCLUSIONS To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized.
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Affiliation(s)
- Finja Büchel
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Nicolas Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Babraham Institute, Babraham Research Campus, Cambridge, UK
| | - Neil Swainston
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Clemens Wrzodek
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Tobias Czauderna
- Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben D-06466, Germany
| | - Roland Keller
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Florian Mittag
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Michael Schubert
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Mihai Glont
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Martijn van Iersel
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Sarah Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Matthias Rall
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Michael Wybrow
- Caulfield School of Information Technology, Monash University, Victoria 3800, Australia
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Douglas B Kell
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
- School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
| | | | - Pedro Mendes
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
- School of Computer Science, The University of Manchester, Manchester M13 9PL, UK
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Andreas Zell
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | | | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Falk Schreiber
- Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben D-06466, Germany
- Institute of Computer Science, University of Halle-Wittenberg, Halle, Germany
| | - Camille Laibe
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Andreas Dräger
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
- Present address: University of California, San Diego, Bioengineering Department, La Jolla, CA 92093-0412, USA
| | - Nicolas Le Novère
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Babraham Institute, Babraham Research Campus, Cambridge, UK
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Gonçalves E, Mirlach F, Saez-Rodriguez J. Cyrface: An interface from Cytoscape to R that provides a user interface to R packages. F1000Res 2013; 2:192. [PMID: 24715956 PMCID: PMC3962008 DOI: 10.12688/f1000research.2-192.v1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/16/2013] [Indexed: 11/12/2023] Open
Abstract
There is an increasing number of software packages to analyse biological experimental data in the R environment. In particular, Bioconductor, a repository of curated R packages, is one of the most comprehensive resources for bioinformatics and biostatistics. The use of these packages is increasing, but it requires a basic understanding of the R language, as well as the syntax of the specific package used. The availability of user graphical interfaces for these packages would decrease the learning curve and broaden their application. Here, we present a Cytoscape plug-in termed Cyrface that allows Cytoscape plug-ins to connect to any function and package developed in R. Cyrface can be used to run R packages from within the Cytoscape environment making use of a graphical user interface. Moreover, it links the R packages with the capabilities of Cytoscape and its plug-ins, in particular network visualization and analysis. Cyrface's utility has been demonstrated for two Bioconductor packages (CellNOptR and DrugVsDisease), and here we further illustrate its usage by implementing a workflow of data analysis and visualization. Download links, installation instructions and user guides can be accessed from the Cyrface homepage ( http://www.ebi.ac.uk/saezrodriguez/cyrface/).
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Affiliation(s)
- Emanuel Gonçalves
- The European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, CB10 1SD, UK
| | - Franz Mirlach
- The European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, CB10 1SD, UK
- University of Applied Science Weihenstephan-Triesdorf, Weidenbach, 91746, Germany
| | - Julio Saez-Rodriguez
- The European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, CB10 1SD, UK
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117
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Gonçalves E, Mirlach F, Saez-Rodriguez J. Cyrface: An interface from Cytoscape to R that provides a user interface to R packages. F1000Res 2013; 2:192. [PMID: 24715956 PMCID: PMC3962008 DOI: 10.12688/f1000research.2-192.v2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/03/2014] [Indexed: 01/08/2023] Open
Abstract
There is an increasing number of software packages to analyse biological experimental data in the R environment. In particular, Bioconductor, a repository of curated R packages, is one of the most comprehensive resources for bioinformatics and biostatistics. The use of these packages is increasing, but it requires a basic understanding of the R language, as well as the syntax of the specific package used. The availability of user graphical interfaces for these packages would decrease the learning curve and broaden their application. Here, we present a Cytoscape app termed Cyrface that allows Cytoscape apps to connect to any function and package developed in R. Cyrface can be used to run R packages from within the Cytoscape environment making use of a graphical user interface. Moreover, it can link R packages with the capabilities of Cytoscape and its apps, in particular network visualization and analysis. Cyrface's utility has been demonstrated for two Bioconductor packages ( CellNOptR and DrugVsDisease), and here we further illustrate its usage by implementing a workflow of data analysis and visualization. Download links, installation instructions and user guides can be accessed from the Cyrface's homepage ( http://www.ebi.ac.uk/saezrodriguez/cyrface/) and from the Cytoscape app store ( http://apps.cytoscape.org/apps/cyrface).
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Affiliation(s)
- Emanuel Gonçalves
- The European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, CB10 1SD, UK
| | - Franz Mirlach
- The European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, CB10 1SD, UK
- University of Applied Science Weihenstephan-Triesdorf, Weidenbach, 91746, Germany
| | - Julio Saez-Rodriguez
- The European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, CB10 1SD, UK
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118
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Melas IN, Kretsos K, Alexopoulos LG. Leveraging systems biology approaches in clinical pharmacology. Biopharm Drug Dispos 2013; 34:477-88. [PMID: 23983165 PMCID: PMC4034589 DOI: 10.1002/bdd.1859] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Accepted: 08/12/2013] [Indexed: 01/15/2023]
Abstract
Computational modeling has been adopted in all aspects of drug research and development, from the early phases of target identification and drug discovery to the late-stage clinical trials. The different questions addressed during each stage of drug R&D has led to the emergence of different modeling methodologies. In the research phase, systems biology couples experimental data with elaborate computational modeling techniques to capture lifecycle and effector cellular functions (e.g. metabolism, signaling, transcription regulation, protein synthesis and interaction) and integrates them in quantitative models. These models are subsequently used in various ways, i.e. to identify new targets, generate testable hypotheses, gain insights on the drug's mode of action (MOA), translate preclinical findings, and assess the potential of clinical drug efficacy and toxicity. In the development phase, pharmacokinetic/pharmacodynamic (PK/PD) modeling is the established way to determine safe and efficacious doses for testing at increasingly larger, and more pertinent to the target indication, cohorts of subjects. First, the relationship between drug input and its concentration in plasma is established. Second, the relationship between this concentration and desired or undesired PD responses is ascertained. Recognizing that the interface of systems biology with PK/PD will facilitate drug development, systems pharmacology came into existence, combining methods from PK/PD modeling and systems engineering explicitly to account for the implicated mechanisms of the target system in the study of drug–target interactions. Herein, a number of popular system biology methodologies are discussed, which could be leveraged within a systems pharmacology framework to address major issues in drug development.
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Affiliation(s)
- Ioannis N Melas
- National Technical University of Athens, Athens, Greece; Protatonce Ltd, Athens, Greece
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Guziolowski C, Videla S, Eduati F, Thiele S, Cokelaer T, Siegel A, Saez-Rodriguez J. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming. Bioinformatics 2013; 29:2320-6. [PMID: 23853063 PMCID: PMC3753570 DOI: 10.1093/bioinformatics/btt393] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Revised: 06/17/2013] [Accepted: 07/04/2013] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. RESULTS We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. AVAILABILITY caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. SUPPLEMENTARY INFORMATION Supplementary materials are available at Bioinformatics online. CONTACT santiago.videla@irisa.fr.
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Birtwistle MR, Mager DE, Gallo JM. Mechanistic vs. Empirical network models of drug action. CPT Pharmacometrics Syst Pharmacol 2013; 2:e72. [PMID: 24448020 PMCID: PMC4026635 DOI: 10.1038/psp.2013.51] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Declining success rates coupled with increased costs is leading to an inevitable breaking point in the drug development pipeline. Can we avoid it by incorporating the vast mechanistic understanding of drug action? A recent review highlights this dilemma and proposes "quantitative logic gate" modeling as a solution.(1) The goal of this commentary is to contrast this approach with mechanistic biochemical network models, which, although alluded to by Kiruoac and Onsum, requires a closer analysis.
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Affiliation(s)
- M R Birtwistle
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - D E Mager
- Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, New York, USA
| | - J M Gallo
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Trairatphisan P, Mizera A, Pang J, Tantar AA, Schneider J, Sauter T. Recent development and biomedical applications of probabilistic Boolean networks. Cell Commun Signal 2013; 11:46. [PMID: 23815817 PMCID: PMC3726340 DOI: 10.1186/1478-811x-11-46] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2013] [Accepted: 06/22/2013] [Indexed: 12/13/2022] Open
Abstract
Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered.A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed.A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels.
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Affiliation(s)
| | - Andrzej Mizera
- Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg
| | - Jun Pang
- Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg
| | - Alexandru Adrian Tantar
- Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg
- Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg
| | - Jochen Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg
- Saarland University Medical Center, Department of Internal Medicine II, Homburg, Saarland, Germany
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Luxembourg
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Kubisch J, Türei D, Földvári-Nagy L, Dunai ZA, Zsákai L, Varga M, Vellai T, Csermely P, Korcsmáros T. Complex regulation of autophagy in cancer - integrated approaches to discover the networks that hold a double-edged sword. Semin Cancer Biol 2013; 23:252-61. [PMID: 23810837 DOI: 10.1016/j.semcancer.2013.06.009] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Autophagy, a highly regulated self-degradation process of eukaryotic cells, is a context-dependent tumor-suppressing mechanism that can also promote tumor cell survival upon stress and treatment resistance. Because of this ambiguity, autophagy is considered as a double-edged sword in oncology, making anti-cancer therapeutic approaches highly challenging. In this review, we present how systems-level knowledge on autophagy regulation can help to develop new strategies and efficiently select novel anti-cancer drug targets. We focus on the protein interactors and transcriptional/post-transcriptional regulators of autophagy as the protein and regulatory networks significantly influence the activity of core autophagy proteins during tumor progression. We list several network resources to identify interactors and regulators of autophagy proteins. As in silico analysis of such networks often necessitates experimental validation, we briefly summarize tractable model organisms to examine the role of autophagy in cancer. We also discuss fluorescence techniques for high-throughput monitoring of autophagy in humans. Finally, the challenges of pharmacological modulation of autophagy are reviewed. We suggest network-based concepts to overcome these difficulties. We point out that a context-dependent modulation of autophagy would be favored in anti-cancer therapy, where autophagy is stimulated in normal cells, while inhibited only in stressed cancer cells. To achieve this goal, we introduce the concept of regulo-network drugs targeting specific transcription factors or miRNA families identified with network analysis. The effect of regulo-network drugs propagates indirectly through transcriptional or post-transcriptional regulation of autophagy proteins, and, as a multi-directional intervention tool, they can both activate and inhibit specific proteins in the same time. The future identification and validation of such regulo-network drug targets may serve as novel intervention points, where autophagy can be effectively modulated in cancer therapy.
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Affiliation(s)
- János Kubisch
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117 Budapest, Hungary
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 512] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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Fazekas D, Koltai M, Türei D, Módos D, Pálfy M, Dúl Z, Zsákai L, Szalay-Bekő M, Lenti K, Farkas IJ, Vellai T, Csermely P, Korcsmáros T. SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks. BMC SYSTEMS BIOLOGY 2013; 7:7. [PMID: 23331499 PMCID: PMC3599410 DOI: 10.1186/1752-0509-7-7] [Citation(s) in RCA: 134] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Accepted: 01/16/2013] [Indexed: 12/18/2022]
Abstract
BACKGROUND Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. DESCRIPTION We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org. CONCLUSIONS With SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses.
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Affiliation(s)
- Dávid Fazekas
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117, Budapest, Hungary
| | - Mihály Koltai
- Statistical and Biological Physics Group of the Hungarian Acad. of Sciences, Pázmány P. s. 1A, H-1117, Budapest, Hungary
- Department of Biological Physics, Eötvös Loránd University, Pázmány P. s. 1A, H-1117, Budapest, Hungary
| | - Dénes Türei
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117, Budapest, Hungary
- Department of Medical Chemistry, Semmelweis University, PO Box 260, H-1444, Budapest, Hungary
| | - Dezső Módos
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117, Budapest, Hungary
- Department of Medical Chemistry, Semmelweis University, PO Box 260, H-1444, Budapest, Hungary
- Department of Morphology and Physiology, Semmelweis University, Vas u. 17, H-1088, Budapest, Hungary
| | - Máté Pálfy
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117, Budapest, Hungary
| | - Zoltán Dúl
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117, Budapest, Hungary
- Department of Medical Chemistry, Semmelweis University, PO Box 260, H-1444, Budapest, Hungary
| | - Lilian Zsákai
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117, Budapest, Hungary
- Department of Medical Chemistry, Semmelweis University, PO Box 260, H-1444, Budapest, Hungary
| | - Máté Szalay-Bekő
- Department of Medical Chemistry, Semmelweis University, PO Box 260, H-1444, Budapest, Hungary
| | - Katalin Lenti
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117, Budapest, Hungary
- Department of Morphology and Physiology, Semmelweis University, Vas u. 17, H-1088, Budapest, Hungary
| | - Illés J Farkas
- Statistical and Biological Physics Group of the Hungarian Acad. of Sciences, Pázmány P. s. 1A, H-1117, Budapest, Hungary
| | - Tibor Vellai
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117, Budapest, Hungary
| | - Péter Csermely
- Department of Medical Chemistry, Semmelweis University, PO Box 260, H-1444, Budapest, Hungary
| | - Tamás Korcsmáros
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117, Budapest, Hungary
- Department of Medical Chemistry, Semmelweis University, PO Box 260, H-1444, Budapest, Hungary
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Abstract
In the last 30 years, many of the mechanisms behind signal transduction, the process by which the cell takes extracellular signals as an input and converts them to a specific cellular phenotype, have been experimentally determined. With these discoveries, however, has come the realization that the architecture of signal transduction, the signaling network, is incredibly complex. Although the main pathways between receptor and output are well-known, there is a complex net of regulatory features that include crosstalk between different pathways, spatial and temporal effects, and positive and negative feedbacks. Hence, modeling approaches have been used to try and unravel some of these complexities. We use the mitogen-activated protein kinase cascade to illustrate chemical kinetic and logic approaches to modeling signaling networks. By using a common well-known model, we illustrate here the assumptions and level of detail behind each modeling approach, which serves as an introduction to the more detailed discussions of each in the accompanying chapters in this book.
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126
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Blinov ML, Moraru II. Logic modeling and the ridiculome under the rug. BMC Biol 2012; 10:92. [PMID: 23171629 PMCID: PMC3503555 DOI: 10.1186/1741-7007-10-92] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2012] [Accepted: 11/21/2012] [Indexed: 11/10/2022] Open
Abstract
Logic-derived modeling has been used to map biological networks and to study arbitrary functional interactions, and fine-grained kinetic modeling can accurately predict the detailed behavior of well-characterized molecular systems; at present, however, neither approach comes close to unraveling the full complexity of a cell. The current data revolution offers significant promises and challenges to both approaches - and could bring them together as it has spurred the development of new methods and tools that may help to bridge the many gaps between data, models, and mechanistic understanding. Have you used logic modeling in your research? It would not be surprising if many biologists would answer no to this hypothetical question. And it would not be true. In high school biology we already became familiar with cartoon diagrams that illustrate basic mechanisms of the molecular machinery operating inside cells. These are nothing else but simple logic models. If receptor and ligand are present, then receptor-ligand complexes form; if a receptor-ligand complex exists, then an enzyme gets activated; if the enzyme is active, then a second messenger is being produced; and so on. Such chains of causality are the essence of logic models (Figure 1a). Arbitrary events and mechanisms are abstracted; relationships are simplified and usually involve just two possible conditions and three possible consequences. The presence or absence of one or more molecule, activity, or function, [some icons in the cartoon] will determine whether another one of them will be produced (created, up-regulated, stimulated) [a 'positive' link] or destroyed (degraded, down-regulated, inhibited) [a 'negative' link], or be unaffected [there is no link]. The icons and links often do not follow a standardized format, but when we look at such a cartoon diagram, we believe that we 'understand' how the system works. Because our brain is easily able to process these relationships, these diagrams allow us to answer two fundamental types of questions related to the system: why (are certain things happening)? What if (we make some changes)?
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Affiliation(s)
- Michael L Blinov
- Center for Cell Analysis and Modeling, University of Connecticut Health Center, Cell and Genome Sciences Building, 400 Farmington Ave, Farmington, CT 06030-6406, USA
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