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Multi-scale computational modeling is a major branch of computational biology as evidenced by the US federal interagency Multi-Scale Modeling Consortium and major international projects. It invariably involves specific and detailed sequences of data analysis and simulation, often with multiple tools and datasets, and the community recognizes improved modularity, reuse, reproducibility, portability and scalability as critical unmet needs in this area. Scientific workflows are a well-recognized strategy for addressing these needs in scientific computing. While there are good examples if the use of scientific workflows in bioinformatics, medical informatics, biomedical imaging and data analysis, there are fewer examples in multi-scale computational modeling in general and cardiac electrophysiology in particular. Cardiac electrophysiology simulation is a mature area of multi-scale computational biology that serves as an excellent use case for developing and testing new scientific workflows. In this article, we develop, describe and test a computational workflow that serves as a proof of concept of a platform for the robust integration and implementation of a reusable and reproducible multi-scale cardiac cell and tissue model that is expandable, modular and portable. The workflow described leverages Python and Kepler-Python actor for plotting and pre/post-processing. During all stages of the workflow design, we rely on freely available open-source tools, to make our workflow freely usable by scientists., Competing Interests: The authors have declared that no competing interests exist.

With the drive toward high throughput molecular dynamics (MD) simulations involving ever-greater numbers of simulation replicates run for longer, biologically relevant timescales (microseconds), the need for improved computational methods that facilitate fully automated MD workflows gains more importance. Here we report the development of an automated workflow tool to perform AMBER GPU MD simulations. Our workflow tool capitalizes on the capabilities of the Kepler platform to deliver a flexible, intuitive, and user-friendly environment and the AMBER GPU code for a robust and high-performance simulation engine. Additionally, the workflow tool reduces user input time by automating repetitive processes and facilitates access to GPU clusters, whose high-performance processing power makes simulations of large numerical scale possible. The presented workflow tool facilitates the management and deployment of large sets of MD simulations on heterogeneous computing resources. The workflow tool also performs systematic analysis on the simulation outputs and enhances simulation reproducibility, execution scalability, and MD method development including benchmarking and validation.

Scientific workflows are powerful tools for the management of scalable experiments, often composed of complex tasks running on distributed resources. Existing cyberinfrastructure provides components that can be utilized within repeatable workflows. However, data and computing advances continuously change the way scientific workflows get developed and executed, pushing the scientific activity to be more data-driven, heterogeneous, and collaborative. Workflow development today depends on the effective collaboration and communication of a cross-disciplinary team, not only with humans but also with analytical systems and infrastructure. This paper presents a collaboration-centered reference architecture to extend workflow systems with dynamic, predictable, and programmable interfaces to systems and infrastructure while bridging the exploratory and scalable activities in the scientific process. We present a conceptual design toward the development of methodologies and services for effective workflow-driven collaborations, namely the PPoDS methodology for collaborative workflow development and the SmartFlows Services for smart execution in a rapidly evolving cyberinfrastructure ecosystem. [ABSTRACT FROM AUTHOR]

We present a method to integrate multiple implementations of a machine learning algorithm in Kepler actors. This feature enables the user to compare accuracy and scalability of various implementations of a machine learning technique without having to change the workflow. These actors are based on the Execution Choice actor. They can be incorporated into any workflow to provide machine learning functionality. We describe a use case where actors that provide several implementations of k-means clustering can be used in a workflow to process sensor data from weather stations for predicting wildfire risks. [ABSTRACT FROM AUTHOR]

As more and more public and private Cloud resources are becoming available, it is common for a user to have access to multiple Cloud resources at the same time. Cloud federation dynamically aggregates multiple Cloud resources into a federated one. This paper explores how to build and run scientific workflows on top of a federated Cloud by integrating Kepler scientific workflow platform with CometCloud platform. Our integration can leverage capabilities of the two plat- forms: 1) dynamic resource federation, provisioning and allocation from CometCloud; 2) Easy workflow composition from Kepler; 3) Dynamic workflow scheduling and execution from the integration. We apply our integration to a bioinformatics workflow with three Cloud resources to evaluate its capabilities. We also discuss possible future directions from the integration. [ABSTRACT FROM AUTHOR]

Background: Mandatory deposit of raw microarray data files for public access, prior to study publication, provides significant opportunities to conduct new bioinformatics analyses within and across multiple datasets. Analysis of raw microarray data files (e.g. Affymetrix CEL files) can be time consuming, complex, and requires fundamental computational and bioinformatics skills. The development of analytical workflows to automate these tasks simplifies the processing of, improves the efficiency of, and serves to standardize multiple and sequential analyses. Once installed, workflows facilitate the tedious steps required to run rapid intra- and inter-dataset comparisons. Results: We developed a workflow to facilitate and standardize Meta-Analysis of Affymetrix Microarray Data analysis (MAAMD) in Kepler. Two freely available stand-alone software tools, R and AltAnalyze were embedded in MAAMD. The inputs of MAAMD are user-editable csv files, which contain sample information and parameters describing the locations of input files and required tools. MAAMD was tested by analyzing 4 different GEO datasets from mice and drosophila. MAAMD automates data downloading, data organization, data quality control assesment, differential gene expression analysis, clustering analysis, pathway visualization, gene-set enrichment analysis, and cross-species orthologous-gene comparisons. MAAMD was utilized to identify gene orthologues responding to hypoxia or hyperoxia in both mice and drosophila. The entire set of analyses for 4 datasets (34 total microarrays) finished in ~ one hour. Conclusions: MAAMD saves time, minimizes the required computer skills, and offers a standardized procedure for users to analyze microarray datasets and make new intra- and inter-dataset comparisons. [ABSTRACT FROM AUTHOR]

Abstract: Increasing numbers of genomic technologies are leading to massive amounts of genomic data, all of which requires complex analysis. More and more bioinformatics analysis tools are being developed by scientist to simplify these analyses. However, different pipelines have been developed using different software environments. This makes integrations of these diverse bioinformatics tools difficult. Kepler provides an open source environment to integrate these disparatepackages. Using Kepler, we integrated several external tools including Bioconductor packages, AltAnalyze, a python-based open source tool, and R-based comparison tool to build an automated workflow to meta-analyze both online and local microarray data. The automated workflow connects the integrated tools seamlessly, delivers data flow between the tools smoothly, and hence improves efficiency and accuracy of complex data analyses. Our workflow exemplifies the usage of Kepler as a scientific workflow platform for bioinformatics pipelines. [Copyright &y& Elsevier]

Abstract: With more and more workflow systems adopting cloud as their e xecution environment, it becomes increasingly challenging on how to effic iently manage various workflows, v irtual machines (VMs) and workflow e xecution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) a rchitecture with independent services. A core part of the architecture is how to efficiently respond continuous workflo w requests from users and schedule their e xecutions in the c loud. Based on diffe rent targets, we p ropose four heuristic workflo w scheduling a lgorith ms for the WFaaS architecture, and analy ze the differences and best usages of the algorith ms in terms of performance, cost and the price/performance ratio via experimental studies. [Copyright &y& Elsevier]

Abstract: With the increasing popularity of the Cloud computing, there are more and more requirements for scientific work–flows to utilize Cloud resources. In this paper, we present our preliminary work and experiences on enabling the interaction between the Kepler scientific workflow system and the Amazon Elastic Compute Cloud (EC2). A set of EC2 actors and Kepler Amazon Machine Images are introduced with the discussion on their different usage modes. Through two bioinformatics usecases, we demonstrate the capability of our work for both Cloud resource coordination and workflow execution on virtual Cloud resources. [Copyright &y& Elsevier]

Abstract: Distributed Data-Parallel (DDP) patterns such as MapReduce have become increasingly popular as solutions to facilitate data-intensive applications, resulting in a number of systems supporting DDP workflows. Yet, applications or workflows built using these patterns are usually tightly-coupled with the underlying DDP execution engine they select. We present a framework for distributed data-parallel execution in the Kepler scientific workflow system that enables users to easily switch between different DDP execution engines. We describe a set of DDP actors based on DDP patterns and directors for DDP workflow executions within the presented framework. We demonstrate how DDP workflows can be easily composed in the Kepler graphic user interface through the reuse of these DDP actors and directors and how the generated DDP workflows can be executed in different distributed environments. Via a bioinformatics usecase, we discuss the usability of the proposed framework and validate its execution scalability. [Copyright &y& Elsevier]

Emerging Grid technologies enable solving scientific problems that involve large datasets and complex analyses, which in the past were often considered difficult to solve. Coordinating distributed Grid resources and computational processes requires adaptable interfaces and tools that provide modularized and configurable environments for accessing Grid clusters and executing high performance computational tasks. Computationally intensive processes are also subject to a high risk of component failures and thus require close monitoring. In this paper we describe a scientific workflow approach to coordinate various resources via data analysis pipelines. We present a three tier architecture for LiDAR interpolation and analysis, a high performance processing of point intensive datasets, utilizing a portal, a scientific workflow engine and Grid technologies. Our proposed solution is available to the community in a unified framework through a shared cyberinfrastructure, the GEON portal, enabling scientists to focus on their scientific work and not be concerned with the implementation of the underlying infrastructure. [ABSTRACT FROM AUTHOR]

Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery ‘pipelines’. A related trend is that more and more scientific communities realize the benefits of sharing their data and computational services, and are thus contributing to a distributed data and computational community infrastructure (a.k.a. ‘the Grid’). However, this infrastructure is only a means to an end and ideally scientists should not be too concerned with its existence. The goal is for scientists to focus on development and use of what we call scientific workflows. These are networks of analytical steps that may involve, e.g., database access and querying steps, data analysis and mining steps, and many other steps including computationally intensive jobs on high-performance cluster computers. In this paper we describe characteristics of and requirements for scientific workflows as identified in a number of our application projects. We then elaborate on Kepler, a particular scientific workflow system, currently under development across a number of scientific data management projects. We describe some key features of Kepler and its underlying Ptolemy II system, planned extensions, and areas of future research. Kepler is a community-driven, open source project, and we always welcome related projects and new contributors to join. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Evolving technologies, as exemplified by computational grids and Web services, have made it possible to solve new scientific problems that would not have been feasible previously. In order to make such advances available to the community in general and to be able to solve new problems, not necessarily from the same discipline, it is imperative to build tools that provide a common user interface in order that application programmers and users do not have to be concerned with particulars of Web services and their underlying code, computational platforms, or with data file formats. We will describe our efforts in creating a Computational Chemistry Environment that encompasses a general scientific workflow environment, a domain specific example for quantum chemistry, our ongoing design of a workflow user interface, and our efforts at database integration. [ABSTRACT FROM AUTHOR]