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Understanding bacteria’s metabolism could improve biofuel production

Researchers at UC Riverside and Pacific Northwest National Laboratory have used mathematical and computational modeling, artificial intelligence algorithms and experiments showing that bacteria have failsafe mechanisms preventing them from producing too many metabolic intermediates.

Metabolic intermediates are the chemicals that couple each reaction to one another in metabolism. Key to these control mechanisms are enzymes, which speed up chemical reactions involved in biological functions such as growth and energy production. The insight could lead to organisms that are more efficient at converting plants into biofuels. The open-access study appears in the Journal of the Royal Society Interface.

Cellular metabolism consists of a bunch of enzymes. When the cell encounters food, an enzyme breaks it down into a molecule that can be used by the next enzyme and the next, ultimately generating energy.

— William Cannon, co-author, UCR adjunct math professor and PNNL computational scientist

The enzymes cannot produce an excessive amount of metabolic intermediates. They produce an amount that is controlled by how much of that product is already present in the cell.

This way the metabolite concentrations don’t get so high that the liquid inside the cell becomes thick and gooey like molasses, which could cause cell death.

—William Cannon

One of the barriers to creating biofuels that are cost-competitive with petroleum is the inefficiency of converting plant material. Typically, E. coli bacteria are engineered to break down lignin, the tough part of plant cell walls, so it can be fermented into fuel.

One of the problems with engineering bacteria for biofuels is that most of the time the process just makes the bacteria sick. We push them to overproduce proteins, and it becomes uncomfortable—they could die. What we learned in this research could help us engineer them more intelligently.

—William Cannon

Knowing which enzymes need to be prevented from overproducing can help scientists design cells that produce more of what they want and less of what they don’t.

Mark Alber, study co-author and UCR distinguished math professor, said that the study is part of a project to understand the ways bacteria and fungi work together to affect the roots of plants grown for biofuels.

In this study, we use two approaches to predict enzyme regulation policies and investigate the hypothesis that regulation is driven by the need to maintain the solvent capacity in the cell. The first predictive method uses a statistical thermodynamics and metabolic control theory framework while the second method is performed using a hybrid optimization–reinforcement learning approach. Efficient regulation schemes were learned from experimental data that either agree with theoretical calculations or result in a higher cell fitness using maximum useful work as a metric.

As previously hypothesized, regulation is herein shown to control the concentrations of both immediate and downstream product concentrations at physiological levels. Model predictions provide the following two novel general principles: (1) the regulation itself causes the reactions to be much further from equilibrium instead of the common assumption that highly non-equilibrium reactions are the targets for regulation; and (2) the minimal regulation needed to maintain metabolite levels at physiological concentrations maximizes the free energy dissipation rate instead of preserving a specific energy charge.

The resulting energy dissipation rate is an emergent property of regulation which may be represented by a high value of the adenylate energy charge. In addition, the predictions demonstrate that the amount of regulation needed can be minimized if it is applied at the beginning or branch point of a pathway, in agreement with common notions.

—Samuel et al.

The US Department of Energy funded this three-year research project with a $2.1-million grant.


  • Britton Samuel, Alber Mark and Cannon William R. (2020) “Enzyme activities predicted by metabolite concentrations and solvent capacity in the cell”, J. R. Soc. Interface. doi: 10.1098/rsif.2020.0656


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