An analytical theory of balanced cellular growth

Abstract

The biological fitness of unicellular organisms is largely determined by their balanced growth rate, i.e., by the rate with which they replicate their biomass composition. Natural selection on this growth rate occurred under a set of physicochemical constraints, including mass conservation, reaction kinetics, and limits on dry mass per volume; mathematical models that maximize the balanced growth rate while accounting explicitly for these constraints are inevitably nonlinear and have been restricted to small, non-realistic systems. Here, we lay down a general theory of balanced growth states, providing explicit expressions for protein concentrations, fluxes, and the growth rate. These variables are functions of the concentrations of cellular components, for which we calculate marginal fitness costs and benefits that can be related to metabolic control coefficients. At maximal growth rate, the net benefits of all concentrations are equal. Based solely on physicochemical constraints, the growth balance analysis (GBA) framework introduced here unveils fundamental quantitative principles of cellular growth and leads to experimentally testable predictions.

The protein translation machinery is expressed for maximal efficiency in Escherichia coli

“…Overall, the largest deviations between observed concentrations and predictions are seen in the two non-minimal conditions, which also exhibit the fastest growth (µ > 1 h −1 ). A recent analytical study of balanced cellular growth indicates that these deviations may result from the influence of an increased growth-related dilution of cofactors and other intermediate metabolites, a phenomenon not included in our simulations 12 .…”

“…In principle, these questions would best be addressed in the context of a whole-cell model of balanced growth that combines mechanistic descriptions of metabolism and protein production. However, while such models have been described conceptually 11,12 , kinetic parameterizations are unavailable for a majority of the relevant enzymatic reactions 13 , preventing a truly mechanistic description that combines metabolism and protein translation. Thus, we here focus on protein production alone, taking the experimentally observed output of translation (proteome production rate and composition in a given growth condition), the corresponding input (charged tRNAs), and the kinetics of individual translation reactions as given.…”

“…The upper bound for the cytosolic mass concentration, beyond which diffusion becomes inefficient, is a fundamental constraint on cellular growth [16][17][18] , and we thus use the cytosolic mass concentration of a particular molecule type as an approximation to its cost. Theoretical models of cellular growth that account for all major biochemical and biophysical constraints indicate that the limit on cellular dry mass indeed represents a dominant constraint on bacterial growth rates 12 .…”

Robust surface-to-mass coupling and turgor-dependent cell width determine bacterial dry-mass density

“…Crowding, in turn, impacts processes comprising macromolecular diffusion (7,8), bacterial nucleoid organization (9), and protein-DNA interactions (10). Accordingly, it was suggested that biomass growth rate depends on dry-mass density and crowding (11)(12)(13)). Yet, the connection of cell-volume growth, biomass growth, and to the crowded state of the cytoplasm remain largely unknown (5).…”

An optimal growth law for RNA composition and its partial implementation through ribosomal and tRNA gene locations in bacterial genomes

“…rRNA forms the central part of the catalyst of peptide elongation, while tRNA forms the core of the substrate; together, they account for the bulk of cellular RNA [2]. Their cytosolic concentrations at different growth rates in E. coli are well described by an optimality assumption [9,36,37]. Moreover, chromosomal gene positions in E. coli are known to affect the expression of both tRNA and rRNA genes [38,39]; both types of genes are located closer to oriC in fast compared to slowly growing bacteria, with rRNA genes positioned closer to oriC than tRNA genes in most examined fast-growing bacteria [27].…”