The goal of this award is to help accelerate breakthroughs in disease research by facilitating integrative systems biology approaches.
The Evelo Lab helped develop WikiPathways.org as a community-curated platform for structuring multi-omics data and the associated open-access pathway analysis tool PathVisio. The Agilent Thought Leader Award will fund development of tools for visualizing metabolite fluxes using PathVisio. The goal is to make modeling results much more accessible to biologists and easier for them to interpret.
“Our work is aimed at achieving better understanding of large biological datasets,” Dr. Evelo said.
“For this we need the context of biology that we already know and the integration of many types of data. This grant allows us to use existing representation of biological pathways to view flux data from models. That will model results which are much easier to interpret than abstract graphs.”
“We’re very pleased to help fund the work at the Evelo Lab, as it closely aligns with a major initiative at Agilent: pursuing breakthroughs in biology and medicine through the ability to integrate diverse life science datasets,” said John Fjeldsted, Ph.D., vice president and general manager of Agilent’s LC/MS business. “Bioinformatics software is still catching up to researchers’ abilities to generate data, and we feel that a wide segment of the science community will benefit from this work.”
The tools that will be developed in Dr. Evelo’s lab will also help researchers who develop flux modeling approaches to improve their models and facilitate their distribution. Basically, these efforts will build a bridge between biologists and metabolic modelers, enabling both communities to better leverage each others’ insights.
Development of a new application for gene ontologies is also part of the project. Presently, pathway analysis and gene ontology analysis are complementary but separate approaches. The new application will enrich integrated biology research by using gene ontology data currently available only for genes and extending it to pathway and metabolite analysis.