Like traffic signals controlling cars through the center of town, microorganisms use metabolic signals to control the flow of energy and nutrients. Adjusting the timing between a red light and a green light could help ease heavy traffic—increasing the efficiency of a specific biological process. But tweaking a metabolic signal could also lead to traffic jams, bottlenecks, and even gridlock elsewhere in the cell if done haphazardly.
Now, using a new method developed by the National Renewable Energy Laboratory (NREL), scientists could have the information they need to make targeted changes to microbes for system-wide benefits. In an ACS Synthetic Biology paper, “Computational Framework for Machine-Learning-Enabled 13C Fluxomics,” NREL scientists Chao Wu, Jianping Yu, Michael Guarnieri, and Wei Xiong describe how machine learning can make it easier and quicker to draft metabolic maps of biological systems.
The work was funded by an NREL Laboratory Directed Research and Development program and the U.S. Department of Energy Office of Science’s Biological and Environmental Research program.