The R&D hub consists of a state-of-the-art facility for analytical challenges beyond lipids. This hub is centralized at the Centre for Life Sciences (CeLS), where SynCTI and SLING are housed. This setup allows for a world-class analytical laboratory space. Note the ‘hub’ feature, which represents the numerous collaborative efforts in and out of this iconic facility, with partners in Singapore and elsewhere in the world. The main activity of the R&D hub will be to remain at the forefront of analytical sciences based on MS and related technologies. This academic laboratory, with a generous footprint and diverse set of analytical equipment, will showcase the most updated technologies and applications required for challenging applications.
The Information Node is an informatics center exploring various aspects of data generation, quality, assurance, and mining. It is segmented into ‘process engineering’ (i.e. improved instrument drivers, automated data workflows, etc.); ‘iOMICS’, the sister program of SLING at LSI, with a mission to integrate datasets from different systems approaches (genomics, metabolomics, transcriptomics and proteomics); ‘data sciences’; and ‘cloud applications’; the latter two are new additions, and will be dependent on successful staff and PI recruitment. This node is physically located at the CeLS and the School of Public Health.
Next in sequence, is a setup for ‘translation’ of approaches (stemming from the blue and green units). In this ‘translation’ node, analytical approaches (experimental and computational) are (i) customized and adjusted for applications in programmatic efforts at NUHS, for example, those within the Cardiovascular Research Institute (CVRI) or the Singapore Centre for Nutritional Sciences, Metabolic Diseases and Human Development (SiNMED), or (ii) lined up for wider use in high priority areas, where these approaches will add considerable value to interrogation of biology. We have already made contact with several of these large-scale efforts in diabetes (Tai E Shyong), in addition to the CVD programs at NUS and Duke-NUS. These activities will ask for a reduction in modifications of suitable and selected tests toward robust pipelines in semi-clinical settings and based increasingly on automation rather than manual processing.
Finally (and increasingly important as an R&D deliverable), a specific effort towards value ‘capture’. We foresee three main areas already active for this ongoing work: (i) close collaboration with locally grown startups, (ii) fee-for-service, a rapidly growing area locally and globally, and (iii) support of local public sector efforts, which will benefit from these technologies; the latter will be hospitals (e.g. NUHS) and associated units (e.g., Health Sciences Authority).