PNNL

The Science                      

Researchers at PNNL are now able to predict cellular metabolite concentrations, rate constants, and regulation of enzymes using the same physical principles as those applied to thermodynamics.

The Impact

Biologists’ understanding of cells has been hampered by the inability to measure critical parameters that control dynamics within each cell. Now, with new physics-based data analysis and simulations, researchers are able to predict these parameters and use them to estimate the concentrations of metabolites within cells, as well as identify which enzymes require regulation. 

In this study, researchers used Neurospora crassato demonstrate that a key regulatory point in glycolysis is critical to preventing the cell’s interior from becoming thick like molasses, thus slowing metabolism, energy production, and growth. Armed with this information, researchers are a step closer to designing pathways for synthetic biology.

Summary

The research combined a new approach for modeling and data inference using a discarded, 110 year-old physics equation.  It helped scientists determine estimates of the concentrations of chemicals and their reaction rates inside a cell by selecting the set of reactions and metabolite concentrations that would produce the most thermodynamically efficient set of pathways to use. In other words, the chosen pathways waste the least amount of heat. The resulting metabolite concentrations are then used to determine the reaction rates and rate constants. Comparison of the metabolite concentrations with experimental estimates of metabolite concentrations allows researchers to determine which reactions are regulated in central metabolism. The rate parameters and enzyme activities are then used in a simulation to predict the energetics, power requirements, resistance, and flux of individual reactions and pathways. The next step is to scale-up the method to model all of metabolism and the interactions of metabolites with the proteins that control the circadian clock within the cell. 

Funding

The work was jointly funded by the National Institute of Biomedical Imaging and Bioengineering and the U.S. Department of Energy, Office of Biological and Environmental Research.

Citation

W. R. Cannon, J. D. Zucker, D. J. Baxter, N. Kumar, J. M. Hurley, and J. C. Dunlap, "Prediction of metabolite concentrations, rate constants and post-translational regulation using maximum entropy-based simulations with application to central metabolism of Neurospora crassa," Processes 2018, 6(6), 63; https://doi.org/10.3390/pr6060063