The view of microbes as energy converters illustrates the trade-off between growth rate and yield

Abstract

AbstractThe application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy, but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models provide us with unprecedented detail about metabolic processes inside the cell. However, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black box models and modern approaches that integrate thermodynamics into genome-scale metabolic models. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield.PerspectiveMicrobial growth is the foundation of many biotechnological applications. The key to optimizing microbial growth lies in thermodynamics, similar to how classical thermodynamics helped optimize steam engines in the 19th century.Genome-scale metabolic models have become widely available, and are used to predict microbial growth. These predictions often fail because these models do not distinguish between growth rate and yield.Classical black box models present a sound thermodynamic theory, by viewing microbes as energy converters. Incorporating such concepts into genome-scale metabolic models has the promise to advance our fundamental understanding of microbial growth, and thus to improve the predictive power of these models.