Integrated Informatics in Life and Materials Sciences: An Oxymoron?

Abstract

The pharmaceutical and chemical industries are facing significant internal and external pressure to boost the experimental efficiency and effectiveness by cutting the direct research costs and reducing the time to market for new sustainable products. Other key issues to secure a competitive return on investment are to enable the rescue of stalled product development projects, abort failing projects early, enhance collaborative multidisciplinary ventures, and ensure an effectual risk management and cost savings through safety testing and failure analysis. Typically, modern organizations rely on a variety of informatics solutions from well-established software vendors. These typically operate on different platforms and are controlled by different management systems using different data types and proprietary formats. Maintaining, integrating, updating and monitoring these powerful, but disparate, ensembles of tools, are an intricate and expensive operation. We present the concept of the Enterprise Discovery Planning (EDP) software platform design to facilitate the optimization of discovery activities at an enterprise level. We also briefly present how Inforsense addresses these issues.

Web services in the life sciences

“…The system enables end-users to connect to and use various proprietary dataanalysis software, data sources and software tools that are available online by third parties through a variety of methods, including web-service interfaces. The Discovery Net system has been used in a variety of projects, including real-time collaborative genome annotation [9], large-scale severe acute respiratory syndrome (SARS) evolution modelling [10], real-time high-throughput cheminformatics and integrative life-science analysis applications [11].…”

hITeQ: A new workflow-based computing environment for streamlining discovery. Application in materials science

“…Thus, the effectiveness of workflow technology to acquire, analyze, store and integrate disparate apparatus, software, and data in materials science is examined. Such a problematic turns into a real challenge when combined with high throughput (HT) experimentation due to i) the corresponding reliance on informatics, and the overwhelming amount of data, ii) the intricacy in describing a solid catalyst, 1 and the corresponding use of new computational methodologies since previously developed ones within the life sciences are not flawlessly transferable, iii) the necessity to cope with various data structure and exchange protocols because very few standards 2 powder X-ray diffraction data, inside the framework of the platform. The methodology is encapsulated into a so-called workflow, and we explore the benefits of such an environment for streamlining discovery research.…”

Catálisis combinatoria para procesos de química fina.

“…Computacionales La gran cantidad de información obtenida como consecuencia de experimentos en equipos de alto rendimiento requiere una serie actividades complementarias con el fin de extraer, almacenar y aplicar conocimiento de la manera más eficiente posible 70,71,72 . Por ejemplo, actividades tan rutinarias como el procesado de los resultados cromatográficos durante un experimento catalítico (adquisición de datos y cálculos elementales relacionados), puede convertirse en la etapa limitante del proceso cuando los equipos experimentales utilizados generan más de 300 análisis por día.…”