Qusheng Jin¹ and Qiong Wu

¹Department of Earth Sciences, University of Oregon, Eugene, Oregon 97403-1272, USA

(*correspondence: qjin@uoregon.edu)

Microbial reaction rates are key for predicting the progress of microbially-driven processes, such as element cycling and contaminant remediation. Current predictions of microbial rates build on the Monod equation and the kinetic parameters determined in laboratory bioreactors. This method is easy to implement, but the results often deviate from field observations. Previous studies merged the gap between the model predictions and field observations by considering the energy available in the environment and how nutrient availability influences cell metabolism. Here we seek to improve the predictions by accounting for the adaptation of microbial metabolisms.

Microbes adapt to the environment by employing different metabolic strategies, including trading-off functional traits and regulating the thermodynamic efficiencies of metabolisms. We describe these strategies as two separate optimization problems of microbial growth. The first problem maximizes growth rates at different substrate concentrations by taking microbial kinetic parameters as decision variables. The optimization solution shows that, in response to increases in substrate concentrations, the rate constant and the half-saturation constant increase simultaneously, but to different extents. The second problem takes the efficiency of microbial energy conservation as the decision variable. The results show that microbes make more ATPs where more energy is available in the environment. By combining the two solutions with the Monod equation, we integrate metabolic acclimation and physiological adaption into the prediction of microbial kinetics.

We applied the optimality-based method to acetoclastic methanogenesis – a key process in wastewater treatment and in situ bioremediation. The predictions match field observations and show that Methanosaeta dominates acetoclastic methanogenesis in lake sediments and aquifers. Without considering the metabolic adaptation, only Methanosarcina would survive in the environment. These results demonstrate the improvements brought by the new method and highlight the importance of microbial metabolisms in predicting the rates of microbial reactions. Similar methods can also be applied to iron reduction, sulfate reduction, and other microbial processes of geochemical significance.