“Keeping up with Bioinformatics and Computational Biology as applied to biomedicine—Where has it been? Where is it going?”

摘要

Bioinformatics as it applied to medicine has changed over the years from its origins in sequence analysis and data management. It has moved from its computer science roots to interdisciplinary applications. Iterative modeling, analysis, and re-synthesis driven by data and information integration and fed ldquonext-generationrdquo high through-put measurement technologies as inputs, and carefully applied to dasiadriving biological problemspsila, is the new way forward. In addition, we now know that the field is interdisciplinary and also translational. At its core, the dasianew bioinformaticspsila, now called dasiasystems biologypsila, is conceived of as a set of multi-scale theories enabled by integration of tightly-coupled datasets ranging from the genome, to transcriptome, to epigenome, to micro- and si-RNAs, to the proteome, to the lipome and metabolome. When this nested hierarchy crosses from the cell level to the tissue, organ, and begin to interact with one another, computational biology approaches begin to dominate and a new field of computational human (or organismal) systems biology emerges. These macroscopic levels are informed by the biologic elements of developmental state, physiology, and structural/functional relationships; similarities exist at the at the micro- (cellular systems biology) and nano- (bioinformatics) levels. The overarching problem at all scales is how we handle the enormous complexity in these multidimensional systems. To address this issue, an important new thrust in bioinformatics and computational biology involves appropriately reducing apparent complexity in the system one is studying by the application of modeling and network theory analytics and methods. Interestingly, striking a balance between dasiareductionistpsila and dasiasyntheticpsila approaches are likely most appropriate to gain new insights. Extending these methods to populations and communities, from metagenomics to large-scale clinical trials, bring probability and sta-ntistics to the forefront-both Frequentist and Bayesian. Additionally, working with human participants in clinical studies and trials has spawned a whole new field of clinical and translation informatics and Information Technology (IT) integration. In addition, the talk will give status updates and set up discussion(s) related to the following topics: The Virtual Physiological Human-the ongoing saga; Lessons from the Clinical and Translational Sciences Awards (CTSA): How is informatics dasiatransformingpsila academic health centers? Where are the bottlenecks? Can biomedicine use Petascale computing?-Ideas we should discuss; The dasiaotherpsila Petascale issue we face-data deluge; Personal ruminations on dasiaNIH Roadmap #2psila, and the central role of computational science methods and infrastructures; Lessons beyond biomedicine to applications of DoE interest: Where are the synergies and points of leverage with NIH?