Flexible informatics for linking experimental data to mathematical models via <i>DataRail</i>

Sáez-Rodríguez, Julio; Goldsipe, Arthur; Muhlich, Jeremy; Alexopoulos, Leonidas G.; Millard, Bjorn L.; Lauffenburger, Douglas A.; Sorger, Peter K.

doi:10.1093/bioinformatics/btn018

Cited by 68 publications

(77 citation statements)

References 12 publications

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“…As an illustrative example, Cyrface provides a simple version of the DataRail 12 workflow using methods implemented in R . DataRail is an open-source MATLAB toolbox that handles experimental data in a tabular format and provides methods to maximize and extract information using internal or external tools.…”

Section: Resultsmentioning

confidence: 99%

“…DataRail is an open-source MATLAB toolbox that handles experimental data in a tabular format and provides methods to maximize and extract information using internal or external tools. Saez-Rodriguez et al 12 also proposed an experimental data storing format termed Minimum Information for Data Analysis in Systems Biology ( MIDAS ).This is a tabular format based upon the minimum-information standards that specifies the layout of experimental data files. A typical use of DataRail is to import, store and process the input information from instruments using the MIDAS format, and export it to other MIDAS compliant software.…”

Section: Resultsmentioning

confidence: 99%

“…Then, to illustrate the applicability of Cyrface , we show two existing packages, CytoCopteR 10 and DrugVsDisease (DvD) 11 , that make use of Cyrface , and we create a simplified version of the DataRail 12 workflow to process and visualize experimental data using methods available in R . Finally, we discuss on-going and future developments.…”

Section: Introductionmentioning

confidence: 99%

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Cyrface: An interface from Cytoscape to R that provides a user interface to R packages

Gonçalvès

Sáez-Rodríguez

2013

F1000Res

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cyrface: An interface from Cytoscape to R that provides a user interface to R packages

Gonçalvès

Sáez-Rodríguez

2013

F1000Res

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“…Libraries in R can also be employed for statistical analysis, data visualization, and immediate rendering of images in SVG, PDF or other graphic formats for inclusion in manuscripts. Results can be exported in compatible formats for visualization or modeling tools such as Cytoscape [11] or DataRail [12].…”

mentioning

confidence: 99%

CoreFlow: A computational platform for integration, analysis and modeling of complex biological data

Pasculescu

Schoof

Creixell

et al. 2014

Journal of Proteomics

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“…Although much of the biochemistry about proteins that regulate the cell cycle is known and mathematical models of the cell cycle can be traced back to as early as the 1970s (8,9), even with recent mathematical models (10-12) the simulation of consecutive cell cycle traverse and cytokinesis cannot be achieved (13). The principal reason being the lack of effective validation against experimental data-a common bioinformatics challenge that impedes the growth of systems biology (14) as well as integrative cancer biology (4,15). Moreover, in the context of the cell cycle model the cross-validation approach (using the same data source as that used to develop the model for calibration) is not sufficient because of their inappropriateness to predict a system, where the structure of the system being studied evolves over time (16).…”

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confidence: 99%

Interoperability of time series cytometric data: A cross platform approach for modeling tumor heterogeneity

Khan¹,

Lupi²,

Campbell³

et al. 2011

Cytometry Pt A

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The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell proliferation-while abnormal control and modulation of the cell cycle are characteristic of tumor cells. The principle aim in cancer biology is to seek an understanding of the origin and nature of innate and acquired heterogeneity at the cellular level, driven principally by temporal and functional asynchrony. A major bottleneck when mathematically modeling these biological systems is the lack of interlinked structured experimental data. This often results in the in silico models failing to translate the specific hypothesis into parameterized terms that enable robust validation and hence would produce suitable prediction tools rather than just simulation tools. The focus has been on linking data originating from different cytometric platforms and cellbased event analysis to inform and constrain the input parameters of a compartmental cell cycle model, hence partly measuring and deconvolving cell cycle heterogeneity within a tumor population. Our work has addressed the concept that the interoperability of cytometric data, derived from different cytometry platforms, can complement as well as enhance cellular parameters space, thus providing a more broader and in-depth view of the cellular systems. The initial aim was to enable the cell cycle model to deliver an improved integrated simulation of the well-defined and constrained biological system. From a modeling perspective, such a cross platform approach has provided a paradigm shift from conventional cross-validation approaches, and from a bioinformatics perspective, novel computational methodology has been introduced for integrating and mapping continuous data with cross-sectional data. This establishes the foundation for developing predictive models and in silico tracking and prediction of tumor progression. ' International Society for Advancement of CytometryKey terms cell cycle; lineage; data interoperability; timelapse microscopy; flow cytometry; bioinformatics; mathematical modeling SIGNIFICANT challenges exist in obtaining the emergent features and patterns of cellular populations influenced by an external perturbation; particularly as there is a need to couple or account for the network of inter-relationships between cells in a proliferating system (1-4). The long-term systems biology requirement is to integrate single cell bifurcation maps (i.e., lineages) with functional information from molecular pathways that include the networks that describe the overall tissue system. There is a second pragmatic requirement to generate time-series experimental data that is suitable to parameterize, calibrate and validate mathematical models (5).Classically, the mammalian cell cycle engine acts as the primary descriptor for cellular dynamic responses, the large-scale acquisition and synthesis of multiparameter data from individual cells provide an understanding of the behavior of the system as a whole (6). The cell cycle represents a temporal sequenc...

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Flexible informatics for linking experimental data to mathematical models via DataRail

Abstract: DataRail is distributed under the GNU General Public License and available at http://code.google.com/p/sbpipeline/

Cited by 68 publications

References 12 publications

Cyrface: An interface from Cytoscape to R that provides a user interface to R packages

Cyrface: An interface from Cytoscape to R that provides a user interface to R packages

CoreFlow: A computational platform for integration, analysis and modeling of complex biological data

Interoperability of time series cytometric data: A cross platform approach for modeling tumor heterogeneity

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