Geoscience Reference
In-Depth Information
through modeling of biologic, physiologic, and biochemical processes (Deville
et al. 2003; Ng et al. 2006; Viswanathan et al. 2008;), including gene-gene and
protein-protein interactions (Tong et al. 2004; Rual et al. 2005); pathway analy-
sis (Schilling et al. 2000; Wishart 2007; Viswanathan et al. 2008); and network
mapping (Lee and Tzou 2009.).
An integrative approach is needed to use different types of databases to
identify distinct system components (organized in modules and subnetworks)
and to understand their relationships and thereby reduce the complexity of a
biologic system as a whole (Lee and Tzou 2009). There are outstanding chal-
lenges to the integrative modeling of biologic systems, some of which are sum-
marized in a recent report from the SYSGENET Bioinformatics Working Group
(Durrant et al. 2011). Because integrative systems modeling requires synthesiz-
ing and harmonizing the analyses of transcriptome, proteome, interactome, me-
tabolome, and phenome data, which are likely to be held in numerous heteroge-
neous databases, it is critical to improve the interoperability, compatibility, and
exchange of software modules that are the foundation of data-processing plat-
forms (such as TIQS and xQTL), database platforms (such as GeneNetwork and
XGAP), and data-analysis toolboxes (such as HAPPY and R/QTL). A standard
computer language for software development and cloud sourcing would facili-
tate efficient software dissemination to the bioinformatics community. In addi-
tion, further development of public repositories for data models and software
source code would promote the use of common data structures and file formats.
To stay at the cutting edge of bioinformatics and take full advantage of its
rapid advance, EPA will need a highly skilled bioinformatics workforce that can
closely follow the development of trends in bioinformatics tools and software
closely. As discussed in Chapter 3, EPA already has a leadership role in bioin-
formatics as applied to toxicity assessment and is well positioned to contribute
to standardization and harmonization processes in the field.
REFERENCES
Attwood, T.K., A. Gisel, N.E. Eriksson, and E. Bongcam-Rudloff. 2011. Concepts,
historical milestones and the central place of bioinformatics in modern biology: A
European perspective. Chapter 1 in Bioinformatics - Trends and Methodologies,
M.A. Mahdavi, ed. InTech-Open Access [online]. Available: http://www.intechop
en.com/books/bioinformatics-trends-and-methodologies [accessed Mar. 30, 2012].
Baumgartner, C., M. Osl, M. Netzer, and D. Baumgartner. 2011. Bioinformatic-driven
search for metabolic biomarkers in disease. J. Clin. Bioinform. 1:2, doi:10.1186/
2043-9113-1-2.
Bennett, D. 2005. Growing pains for metabolomics. The Scientist 19(8):25-28.
Chao, T.C., and N. Hansmeier. 2012. The current state of microbial proteomics: Where
we are and where we want to go. Proteomics 12(4-5):638-650.
DeAngelo, A.B., F.B. Daniel, D.M. Wong, and M.H. George. 2008. The induction of
hepatocellular neoplasia by trichloroacetic acid administered in the drinking water
of the male B6C3F1 mouse. J. Toxicol. Environ. Health A 71(16):1056-1068.
Search WWH ::
Custom Search