Computational and Predictive Biology

DOE national laboratory scientists led by Oak Ridge National Laboratory have developed the first tree dataset of its kind, bridging molecular information about the poplar tree microbiome to ecosystem-level processes. Credit: Andy Sproles, ORNL/U.S. Dept. of Energy
An ORNL-led research team developed the first dataset bridging molecular information about the poplar tree microbiome to ecosystem-level processes, informing research regarding how natural systems function. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

 

Analyzing Biological Systems Using Advanced Computational Techniques

Modern biological research is increasingly dependent on, and driven by, evolving tools and methods in computational analysis and prediction. By leveraging emerging technologies such as artificial intelligence, researchers are accelerating the integration and analysis of progressively vast and more complex experimentally-generated datasets and advancing scientific understanding of complex biological systems.  

The Computational and Predictive Biology Group at Oak Ridge National Laboratory brings together experimental biologists, computational scientists, and technical specialists to develop sophisticated computational models capable of accurately simulating biological processes. These models are crucial for interpreting large datasets, improving the accuracy of scientific predictions, and enabling researchers to test hypotheses rapidly. By providing detailed computational insights, the models inform experimental design, streamline discovery processes, and inspire innovative research directions that maximize scientific progress.

The team investigates bioenergy plants, focusing on their potential as advanced feedstocks for biofuel production, and models plant genetics and environmental interactions to enhance plant productivity. Researchers explore plant-microbe interactions, examining how microbes influence plant health and growth in both natural ecosystems and engineered environments. 

The team also develops new technical methods and systems such as KBase (the Department of Energy’s Systems Biology Knowledgebase), and works with researchers at the Oak Ridge Leadership Computing Facility to develop exascale applications that run on some of the world’s fastest supercomputers.

Some of the group’s innovative analysis methods also apply to human genetics, with researchers studying how genetic variations affect susceptibility to chronic pain conditions, opioid addiction, and other diseases. These studies have implications for personalized medicine and targeted therapeutic interventions.

Research is supported by the DOE Office of Science Biological and Environmental Research program through the Center for Bioenergy Innovation, the Plant-Microbe Interfaces Science Focus Area, and individual investigator-driven projects. Health-related research is supported by other federal agencies such as the National Institutes of Health and the Veterans Administration.