SpiNNTools: The Execution Engine for the SpiNNaker Platform.

SpiNNaker is a massively parallel distributed architecture primarily focused on real time simulation of spiking neural networks. The largest realization of the architecture consists of one million general purpose processors, making it the largest neuromorphic computing platform in the world at the present time. Utilizing these processors efficiently requires expert knowledge of the architecture to generate executable code and to harness the potential of the unique inter-processor communications infra-structure that lies at the heart of the SpiNNaker architecture. This work introduces a software suite called SpiNNTools that can map a computational problem described as a graph into the required set of executables, application data and routing information necessary for simulation on this novel machine. The SpiNNaker architecture is highly scalable, giving rise to unique challenges in mapping the problem to the machines resources, loading the generated files to the machine and subsequently retrieving the results of simulation. In this paper we describe these challenges in detail and the solutions implemented.

sPyNNaker: A Software Package for Running PyNN Simulations on SpiNNaker.

This work presents sPyNNaker 4.0.0, the latest version of the software package for simulating PyNN-defined spiking neural networks (SNNs) on the SpiNNaker neuromorphic platform. Operations underpinning realtime SNN execution are presented, including an event-based operating system facilitating efficient time-driven neuron state updates and pipelined event-driven spike processing. Preprocessing, realtime execution, and neuron/synapse model implementations are discussed, all in the context of a simple example SNN. Simulation results are demonstrated, together with performance profiling providing insights into how software interacts with the underlying hardware to achieve realtime execution. System performance is shown to be within a factor of 2 of the original design target of 10,000 synaptic events per millisecond, however SNN topology is shown to influence performance considerably. A cost model is therefore developed characterizing the effect of network connectivity and SNN partitioning. This model enables users to estimate SNN simulation performance, allows the SpiNNaker team to make predictions on the impact of performance improvements, and helps demonstrate the continued potential of the SpiNNaker neuromorphic hardware.

An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals.

The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design-Build-Test-Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L-1. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology.

BACKGROUND: Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. RESULTS: BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on-line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. CONCLUSIONS: Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.

SYNBIOCHEM-a SynBio foundry for the biosynthesis and sustainable production of fine and speciality chemicals.

The Manchester Synthetic Biology Research Centre (SYNBIOCHEM) is a foundry for the biosynthesis and sustainable production of fine and speciality chemicals. The Centre's integrated technology platforms provide a unique capability to facilitate predictable engineering of microbial bio-factories for chemicals production. An overview of these capabilities is described.

The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud.

The Taverna workflow tool suite (http://www.taverna.org.uk) is designed to combine distributed Web Services and/or local tools into complex analysis pipelines. These pipelines can be executed on local desktop machines or through larger infrastructure (such as supercomputers, Grids or cloud environments), using the Taverna Server. In bioinformatics, Taverna workflows are typically used in the areas of high-throughput omics analyses (for example, proteomics or transcriptomics), or for evidence gathering methods involving text mining or data mining. Through Taverna, scientists have access to several thousand different tools and resources that are freely available from a large range of life science institutions. Once constructed, the workflows are reusable, executable bioinformatics protocols that can be shared, reused and repurposed. A repository of public workflows is available at http://www.myexperiment.org. This article provides an update to the Taverna tool suite, highlighting new features and developments in the workbench and the Taverna Server.