Cellular metabolism is the series of chemical reactions that maintain life in living things. During this process, substances called enzymes break down and build fats, proteins, energy carriers, and genetic information. These processes happen through a complex, interconnected network of reactions. Until now, studies to identify specific reactions that regulate the overall flow through a network were too complex to do regularly. Now scientists have developed new methods that combine cutting-edge techniques to predict which enzymes control common biochemical pathways. Previously, scientists believed that the reactions that regulate flow through the network make other reactions less intense. The new study found instead that these regulating reactions actually make other reactions more intense.
Scientists must understand how cells regulate and control themselves to continue their advances in important research areas. This knowledge will help researchers understand the fundamental science of biology. It will also help researchers design new organisms for specific purposes and develop new strategies to target and control diseases. With this new research, scientists have shown a way to identify points of metabolic regulation easily and rapidly.
Cells produce small molecules called metabolites during their regular biochemical functions. As the molecules accumulate in a cell, the inner liquid becomes thick and glassy. This makes it difficult for metabolites and biomolecules to diffuse through a cell to their target such that regulation to control these metabolites is required. However, researchers have previously faced challenges in using experiments or simulations to decipher the principles behind how cells regulate enzymatic reactions to control metabolite concentrations.
Scientists developed new methods to predict which enzymes in biochemical pathways regulate metabolite flow through organized network reactions. First, they reformulated mass action kinetics to determine reasonable rate parameters. Then, the scientists used a combination of statistical thermodynamics, control theory, and reinforcement learning (a type of machine learning) to predict which enzymes needed to be controlled to match physiological levels of metabolite concentrations. They found that metabolic regulation acts to restrict flow at the regulated reactions, pushing them further away from equilibrium. Their results also supported an often-overlooked organizational principle of biology proposed a century ago: the minimal amount of regulation needed corresponded to the maximization of entropy production.
Pacific Northwest National Laboratory
This project was supported by the Department of Energy (DOE) Office of Science Graduate Student Research award, funding from the DOE Office of Science, Biological and Environmental Research program, the National Institute of Biomedical Imaging and Bioengineering, and the National Science Foundation.
Britton, S., Alber, M., and Cannon, W.R., Enzyme Activities Predicted by Metabolite Concentrations and Solvent Capacity in the Cell. Journal of the Royal Society-Interfaces 17, 171 (2020). [DOI: 10.1098/rsif.2020.0656]