NDLI: “Keeping up with Bioinformatics and Computational Biology as applied to biomedicine

Content Provider	IEEE Xplore Digital Library
Author	Athey, B.
Copyright Year	2009
Description	Author affiliation: Center for Computational Medicine and Bioinformatics (CCMB), USA (Athey, B.)
Abstract	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 “next-generation” high through-put measurement technologies as inputs, and carefully applied to ‘driving biological problems’, is the new way forward. In addition, we now know that the field is interdisciplinary and also translational. At its core, the ‘new bioinformatics’, now called ‘systems biology’, 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 ‘reductionist’ and ‘synthetic’ 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 statistics 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 ‘transforming’ academic health centers? Where are the bottlenecks? • Can biomedicine use Petascale computing?—Ideas we should discuss • The ‘other’ Petascale issue we face—data deluge • Personal ruminations on ‘NIH Roadmap #2’, 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?
Starting Page	1
Ending Page	2
File Size	56526
Page Count	2
File Format	PDF
ISBN	9781424438372
DOI	10.1109/BSEC.2009.5090444
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sequences Data analysis Biological system modeling Systems biology Humans Biomedical informatics Biomedical computing Petascale computing Bioinformatics Computational biology
Content Type	Text
Resource Type	Article

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