BLACKSBURG, Va.,  June 21, 2011 –  SysGenSim, a simulation software for Genetical Systems Biology (i.e. Systems Genetics), has been released.  Developed in a collaboration between the research groups of Alberto de la Fuente, Ph.D., of CRS4 Bioinformatica and Ina Hoeschele, Ph.D., of VBI, the software is available at the SysGenSim website.

SysGenSim allows researchers to select a variety of parameters and use these to simulate systems genetics experiments in model organisms including mouse models of human diseases.  Unlike traditional genome-wide association (GWAS) and linkage studies, systems genetics allows researchers to understand the underlying molecular mechanisms of disease by analyzing a wide variety of data at the organismal and molecular levels simultaneously.  Data including disease phenotypes, genome-wide DNA variant genotyping, genome-wide expression profiling and genome-wide DNA methylation profiling all provide information about the pathways and networks responsible for the disease or biomedical trait of interest.  Such research is essential to producing new and improved personalized treatments and therapies for complex human diseases.

Developing this software was made possible only by combining the expertise of de la Fuente in biological network modeling with the expertise of Hoeschele in statistical genetics. “Analysis of data generated in Genetical Systems Biology experiments and studies is highly complex”, said de la Fuente, “and while many methods have been proposed, it is unclear which methods to choose, how effective they are, and how to interpret the results.” SysGenSIM now enables researchers to perform thorough evaluations of and comparisons among analysis methods, under controlled conditions and relative to a known truth. Methods that perform well are excellent candidates for real data analysis, while poorly performing methods should be discarded or interpreted with great caution. “The Systems Biology community increasingly realizes that thorough algorithm verification is necessary before blindly applying them to biological data,” de la Fuente added, emphasizing the need for this type of data simulation software. SysGenSIM has already been used to provide a challenge to the 5th DREAM, an annually held international  Systems Biology network inference competition.

Hoeschele and de la Fuente also collaborate on an NIH funded project developing new multivariate analysis methods for Genetical Systems Biology, which are being evaluated using SysGenSIM, and Hoeschele collaborates with researchers at Wake Forest University in Winston-Salem (NC) on a very large-scale human Genetical Systems Biology study of atherosclerosis.

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About Virginia Bioinformatics Institute
The Virginia Bioinformatics Institute at Virginia Tech is a premier bioinformatics, computational biology, and systems biology research facility that uses transdisciplinary approaches to science combining information technology, biology, and medicine. These approaches are used to interpret and apply vast amounts of biological data generated from basic research to some of today’s key challenges in the biomedical, environmental, and agricultural sciences. With more than 240 highly trained multidisciplinary, international personnel, research at the institute involves collaboration in diverse disciplines such as mathematics, computer science, biology, plant pathology, biochemistry, systems biology, statistics, economics, synthetic biology, and medicine. The large amounts of data generated by this approach are analyzed and interpreted to create new knowledge that is disseminated to the world’s scientific, governmental, and wider communities.

About CRS4
CRS4 Bioinformatics is the computational biology laboratory of the Center for Advanced Studies, Research and Development in Sardinia. The research is directed towards the development of new computational models for the identification of disease-associated markers using functional genomic and systems biology approaches. Relevant examples of the current research include: data management and integration of high-throughput datasets for the development of models for personalized-medicine; identification of biomarker candidates associated with sample stratification, disease susceptibility or clinical outcome and systems biology: inference of gene and protein networks in complex biological systems of biomedical interest and design and implementation of mathematical algorithms for the modeling of biological processes.



Tiffany Trent

Published by Tiffany Trent, June 21, 2011