View the complete Missouri Regional Life Sciences Summit supplement from the Kansas City and St. Louis Business Journals. Click here to view.
Experts will discuss existing bioinformatics infrastructure to support and grow the Animal to Human Health Corridor’s emerging computing and analytic needs.
Gary Allen, DVM, vice president for information technology, University of Missouri System and chief information officer, University of Missouri-Columbia
Mark Hoffman, vice president, life sciences solutions, Cerner Corporation
Care Drives Discovery, Discovery Drives Care: The delivery of patient care in settings enabled by electronic health record (EHR) systems creates many opportunities to facilitate and automate clinical research. The consistent capture of data during patient care processes in codified, machine-readable formats allow research efforts, such as population surveillance, clinical trial recruitment and continuous analysis to be performed in much more efficient manner. Through a combination of site-specific capabilities, such as advanced support for molecular diagnostic laboratories and clinical trials management systems, to distributed networks that facilitate collaborative research, Cerner has been at the forefront of the EHR community in seeking to connect that patient care and research communities. Cerner has been a leader in integrating molecular diagnostic and genomic information into the EHR through the release of the Clinical Biofinformatics Ontology, which is in use globally and the laboratory information system module, Millennium Helix. Cerner has also demonstrated leadership in proving new models of data sharing and collaboration. For example, during the influenza season of 2009-2010, Cerner connected approximately 800 health care facilities to monitor data for trends in positive influenza test results or syndromic indicators. Cerner has also launched initiatives to demonstrate how advanced clinical decision support capabilities can accelerate the process of moving new biological knowledge, such as pharmacogenetics, closer to the patient care setting. Collectively these efforts contribute to building new infrastructure that will accelerate the pace of clinical and biomedical research as well as promote the adoption of new knowledge.
Chi-Ren Shyu, director, MU Informatics Institute, University of Missouri-Columbia
Interdisciplinary collaboration between computational sciences and life/health sciences is a hallmark of the MU Informatics Institute (MUII) and its new Informatics Ph.D. program. The Institute was established to foster synergy and interdisciplinary research applications in animal, plant, human health, geospatial and microbial sciences. Creative faculty and modern computation-based research facilities combine to enable groundbreaking collaborative research that relies heavily on informatics tools and expertise. In this talk, I will briefly introduce the informatics expertise of MUII core faculty in supporting experimental scientist’s R&D activities with commercialization potentials by using an example scenario in personalized medicine. There are six signature research areas that are underpinning components: (1) high-throughput sequence assembly and analysis, (2) structural bioinformatics – prediction, retrievals, and interactions, (3) large-scale and high-throughput phenotype analysis, (4) data mining and knowledge discovery from large-scale omics databases and electronic health records (5) visualization and parallelism of informatics data, and (6) geospatial informatics.
Jerry Taylor, professor and Wurdack Chair of Animal Genomics, University of Missouri-Columbia
High-Throughput Genomics and the Sword of Damocles: Next-generation sequencing and high-throughput assaying technologies have dichotomized entire research communities into those individuals who have rapidly evolved in response to the technological selection pressure and those destined to become evolutionary dead-ends. The problem lies not with the ease with which these technologies may be applied to address a breadth of issues from organismal evolution to variant detection or from quantitation of gene expression and DNA methylation, but the ease with which they generate volumes of data which have never before been experienced by individual investigators. Unfortunately, the pandemic has only just begun and the rate at which data are being generated appears to exceed the exponential increases in computer hardware capabilities described by Moore’s law. As a consequence, fewer and fewer investigators will have access to these technologies until user friendly software becomes available at modest price points. However, the problem really is institutional in nature since no institution can afford the disenfranchisement of a large proportion of its life sciences faculty whom are not bioinformaticians or computer systems analysts. Unless universities and corporate research entities rapidly invest in the computational processing and data storage infrastructures and bioinformatics support (personnel and software), their ability to compete within competitive funding and intellectual property generation domains will rapidly wither.
In silico compound screening for drug discovery in the cloud: The need to identify new small molecules and novel binding partners for known bioactive sites remains a constant in drug development. We are aiding VaSSA Informatics in creating informatics methods used to identify functionally similar compounds ab initio, regardless of structural concerns. To achieve this goal, we implemented an information content algorithm as the primary screening parameter. The results of ChemVaSSA’s validation cycle suggest that it can, in fact, detect functionally similar molecules that interact with known bioactive sites ab initio. Validation of ChemVaSSA’s results was performed using in silico modeling. First, we developed a ligand library that contained the information content signature of 600,000 ligand/compound complexes for which structure was available. We then utilized atorvastatin (Lipitor) as a test compound to search for molecules with similar functional roles. In addition to returning structurally expected results (other statins), we identified several compounds that, via modeling, appear to bind Hmg-CoA reductase at the site of Lipitor binding but that are NOT structurally similar to Lipitor or other statins. We are structuring this screening to utilize the Amazon EC2 resource and show the cost model associated with this.