Bacterial growth laws reflect the evolutionary importance of energy efficiency

Abstract

We are interested in the balance of energy and protein synthesis in bacterial growth. How has evolution optimized this balance? We describe an analytical model that leverages extensive literature data on growth laws to infer the underlying fitness landscape and to draw inferences about what evolution has optimized in Escherichia coli. Is E. coli optimized for growth speed, energy efficiency, or some other property? Experimental data show that at its replication speed limit, E. coli produces about four mass equivalents of nonribosomal proteins for every mass equivalent of ribosomes. This ratio can be explained if the cell's fitness function is the the energy efficiency of cells under fast growth conditions, indicating a tradeoff between the high energy costs of ribosomes under fast growth and the high energy costs of turning over nonribosomal proteins under slow growth. This model gives insight into some of the complex nonlinear relationships between energy utilization and ribosomal and nonribosomal production as a function of cell growth conditions. Monod observed that increasing glucose increases Escherichia coli's growth rate, up to a maximum rate beyond which the cells cannot replicate any faster (1). On the one hand, such growth laws are experimentally observable. On the other hand, growth laws, per se, do not give insight into the evolutionary driving forces that lead to them.Evolutionary principles are expressed by fitness landscapes (13), which are mathematical surfaces that represent how the organism's fitness depends on some cellular property that can be altered by evolution over time. Peaks on fitness landscapes represent states of maximal fitness. To understand why a cell has a particular growth law, we need a mathematical model that relates its growth law (how the growth rate of the cellular population depends on food concentration) to its underlying fitness landscape (how the cell's growth parameters can be altered through evolution). Thus far, this is relatively uncharted territory for cellular modeling. Here, we develop a model to explore how bacteria balance their fluxes of energy and ribosomal (RPs) and nonribosomal proteins (NRPs). By comparing the model with data, we can explore possible fitness objectives for bacterial replication. Are bacteria evolutionarily optimized to maximize their duplication speed? Or, are bacteria evolutionarily optimized to maximize the energy efficiency of their duplication processes? Or, something else? By "evolutionarily optimized," we mean the tradeoffs that a cell must make. By evaluating extensive growth data on E. coli through the lens of the present model, which relates growth observables to fitness landscapes, we conclude that a principal evolutionary driving force for bacteria is the energy efficiency of the fastest-growing cells. Fig. 1 shows our kinetic model of bacteria growing in the exponential phase. This model defines relationships among four dynamical quantities: the rate of synthesis of ribosomal proteins, the synthesis and degradation rates of NRP...

Benefit of transferred mutations is better predicted by the fitness of recipients than by their ecological or genetic relatedness

“…A candidate target process is translation, which constitutes a major energetic investment for bacterial cells. Cells must balance the need for sufficient translational capacity to allow expression of necessary genes, while minimizing wasteful investment in unused capacity (41). Translation provides a means to mediate interactions between seemingly disparate mutations, for example, considering three of the mutations examined in this work: Deletion of rbs genes might provide an advantage by reducing energy expenditure and ribosome allocation to unnecessary gene expression; topA impacts gene expression by altering DNA supercoiling, thereby altering promoter activity and reallocating translational resources; and spoT changes gene expression patterns in a way consistent with an effect on translational activity (42).…”

Evolution of initiator tRNAs and selection of methionine as the initiating amino acid

“…Evolutionary importance of changes in the tRNA gene and energy efficiency with bacterial growth has already been well characterized. 20,54,55 The occurrence of Met in proteins is very infrequent (Fig. 6a), its internal occurrence would mean a higher metabolic cost.…”

Bacterial Relay for Energy-Efficient Molecular Communications

“…In fact, this is gaining increasing attention in research, such as a recent paper on joint energy harvesting and data transfer from information molecules [5], [26]. Alternatively, the nanomachines need to be able to covert various energy forms (e.g., electric, chemical energy) into mechanical work [27] and the bacteria need to consume food (e.g., chemical compounds) [28] for the information relay and delivery. In the state-of-art of molecular communication and nanotechnology, targeted drug delivery is one of the applications where energy supply is a fundamental limit to how effectively nano-machines can operate, and many complex fuel systems have been proposed [29], which further highlights the importance of energy consumption in nano-machines.…”