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Australian researchers: G'day, pathways
November 2009
by Amy Swinderman  |  Email the author


BRISBANE, Australia—The Queensland Facility for Advanced Bioinformatics (QFAB), an interdisciplinary research consortium established to help scientists with their bioinformatics requirements, announced in October that it has entered into a multi-year deal to use Ingenuity Pathway Analysis (IPA), a biological and chemical systems modeling software solution developed by Ingenuity Systems, to enhance its data analysis and interpretation offerings.
Under the agreement, announced Oct. 6, IPA will be integrated into QFAB's Integrated Systems Biology platform, which has been funded by the Australian Research Council, the Australian Stem Cell Centre and other institutions, and play a major part in QFAB's initiative to enable the global efforts of biotechnology, research biology, drug discovery and translational medicine. Financial details of the partnership were not disclosed.  
According to Jeremy Barker, CEO of QFAB, the consortium was established to ease the data capture and management, experimental design and analysis challenges experienced by a broad range of collaborators, including groups from the agricultural, biotechnology and biomedical sectors. The Systems Biology Platform that QFAB is developing aims to characterize and understand the complex biomolecular networks that control biological processes in cells, tissues or whole organisms. All are based on high-throughput biological data arising from genomics, transcriptomics and/or proteomics, and all require the discovery, inference, analysis, modeling and/or simulation of biomolecular networks, including those involved in gene expression, regulation, intracellular trafficking, development and disease.  
As part of the bioinformatics platform, IPA will serve as the primary solution to model, analyze and identify key insights from high-throughput biomolecular data and curated datasets relating to health, biotechnology and environmental processes. IPA will also help explore biomolecular network models and facilitate experimental validation, Barker says.  
"Some of our collaborators have been using IPA for a number of years and requested that we include this in the development of our systems biology platform," he says. "It also fits into our philosophy of using 'best-of-breed' and relevant technology, whether that be open source or commercial software.  We choose to use software that is relevant to the research work that we are engaged in and where necessary also develop our own."
IPA will be provided by Ingenuity Systems and Millennium Science, the company's exclusive distributor for Australia and New Zealand. IPA is an all-in-one software application that enables researchers to model, analyze and understand the complex biological and chemical systems at the core of life science research. The software's search capabilities provide users with access to the highest quality detail-rich knowledge available on genes, drugs, chemicals, protein families, cellular and disease processes, and signaling and metabolic pathways. IPA supports analysis of data from all experimental platforms, and is used at all stages of the drug discovery and development process, including target identification and validation, biomarker discovery, molecular toxicology, metabolomics and pharmacogenomics. IPA has been broadly adopted and cited in hundreds of peer-reviewed journals.
"IPA is unique because it draws upon the Ingenuity Knowledge Base, a database of manually curated and structured biological and chemical relationships, which we call Findings," explains Heidi Bullock, director of marketing for Ingenuity Systems. "Ingenuity Findings, unlike flat text records or other databases, are highly structured to capture detailed biological context and relationships (such as sites of post-translational modifications, direction of change, experimental method, etc.). This allows for more accuracy and the ability to drill down into the exact nature of a biological relationship—details which are crucial for building relevant models and accelerating discovery research."  
Additionally, highly structured findings allow for computation and semantic consistency—which means researchers can quickly find better, more relevant information, Bullock adds.   
"In addition to these structured literature findings, IPA integrates quality-controlled information from select publicly available databases, such as DrugBank,, HMDB, TarBase and many others," she says.  
Bullock acknowledges that there is still a significant hurdle for researchers when it comes to interpreting data, accessing high-quality scientific information, and sharing that information. IPA will be used to analyze data and help researchers access relevant, detailed and accurate knowledge that can be leveraged throughout the experimental life cycle, she points out.
"For example, IPA can be used to create a testable model that will inform assay design," Bullock says. "Once data is generated, IPA will enhance QFAB's existing platform by helping researchers uncover novel insights from their data by quickly identifying relationships, mechanisms, functions and pathways of relevance. QFAB researchers will now be able to quickly integrate and understand multiple lines of experimental evidence—either publicly available or proprietary data—such as gene expression signatures, miRNAs and SNPs to identify common pathways or molecular mechanisms at the core of the respective experimental model."  
Ultimately, the partnership will enable the research teams supported by QFAB to use IPA to make better, knowledge-based decisions, Bullock says.  
"Specifically, with regard to translational medicine efforts, IPA can help researchers understand mechanisms of disease, identify genes and proteins associated with the etiology of a specific disease and predict and validate biomarkers," she says.  
Barker says that while the partnership may not help data management challenges, it will provide the opportunity for research programs to access an important analytical tool in the process of understanding the biological pathways involved in their specific areas of interest.     
"Visualization of these pathways is a key opportunity for the scientist in identifying areas of interest," Barker concludes. "Multidisciplinary research of this type relies heavily on the availability of computational systems to mine high-throughput datasets collected from disparate chemistry and genomics platforms, advanced visualization of pathways and networks, and systems to share results. All these functions are to be provided by the proposed platform. IPA is a key part of that functionality."

IPA to support multiple QFAB projects

Jeremy Barker, CEO of the Queensland Facility for Advanced Bioinformatics (QFAB), says Ingenuity Systems' IPA will support a number of research programs that the consortium in engaged in which seek which to characterize and understand the complex biomolecular networks that control biological processes in cells, tissues or whole organisms.
For example, inference and computational analysis of biomolecular networks and systems in mammalian cells is a core objective of one of QFAB's collaborators, Barker says. Two of their four research programs have as primary aims the inference, analysis, modeling and visualization of cellular regulatory networks.   
Another research project that QFAB is working closely in involves the investigation of large-scale expression data from normal breast and cancer cell lines for inference of nuclear receptor networks, with the goal of identifying and validating new potential targets for cancer treatment, Barker says.
In addition, a key collaborator at the Institute for Molecular Bioscience at the University of Queensland uses IPA and is looking to extend his research through access to the systems biology platform by allowing the systematic linkage and display of expression patterns of all components of the regulatory network in normal and transformed cell types throughout the cell cycle, including canonical and variant phosphoregulator genes generated by alternative transcription events to identify candidate gene products that drive key phenotypes in model systems.  
"Capturing the information contained within the transcriptome and modeling transcriptome dynamics to identify key genes and transcriptional programs is a central research theme of this work," Barker adds.  
The Systems Biology Platform will also enable the research conducted by a collaborator based at the Mater Hospital to systematically integrate a broad range of data in the context of ligand-receptor function and signal transduction pathways in metabolism, leading to the identification of molecular and physiological factors involved in obesity and related diseases, Barker adds.  
Finally, researchers at the Eskitis Institute intend to use the platform to investigate the natural variation of genetic networks in the human populations, elucidate associations between genotype and genetic networks and identify leads into complex diseases such as schizophrenia and Parkinson's disease, Barker says.
Code: E110904



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