Understanding global resource allocation in fission yeast through data analysis and coarse-grained mathematical modelling

Unicellular organisms can grow in a large variety of environments. Even in those supporting robust growth, cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate and the cell size. I have approached this topic from two angles, namely a comprehensive analysis of a gene expression data set and the construction of coarse-grained resource allocation models (C-GRAMs). First, I studied a combined data set of protein and transcript abundances during growth of the fission yeast Schizosaccharomyces pombe on various abundant nitrogen sources. Approximately half of gene expression was significantly correlated with the growth rate, and this came alongside wide-spread nutrient-specific expression. Genes positively correlated with the growth rate participated in protein production, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, the expression of metabolic enzymes was mainly condition specific. Second, C-GRAMs are simple models of single cells, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models can then be easily explored. A minimal C-GRAM with nitrogen and carbon pathways converging on biomass production described the effects of the uptake of sugars, ammonium, and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximised the growth rate. Prompted by new observations that the relation between cell volume and the growth rate was identical for both carbon and nitrogen perturbations, but that the surface-to-volume ratio was elevated in low-nitrogen conditions, I extended this to a C-GRAM that additionally accounted for the cell cycle, cell division, cell wall biosynthesis, and the effect of molecular crowding on the ribosomal efficiency.Open Acces

Fission yeast obeys a linear size law under nutrient titration

Steady-state cell size and geometry depend on growth conditions. Here, we use an experimental setup based on continuous culture and single-cell imaging to study how cell volume, length, width and surface-to-volume ratio vary across a range of growth conditions including nitrogen and carbon titration, the choice of nitrogen source, and translation inhibition. Overall, we find cell geometry is not fully determined by growth rate and depends on the specific mode of growth rate modulation. However, under nitrogen and carbon titrations, we observe that the cell volume and the growth rate follow the same linear scaling

GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

Motivation Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. Results We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using i) standard rejection ABC or ABC-SMC, or ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost. Availability and Implementation https://github.com/tanhevg/GpABC.jl Supplementary information Supplementary data are available at Bioinformatics online

Growth-rate-dependent and nutrient-specific gene expression resource allocation in fission yeast

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determined the importance of the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombe grown on non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart from RNA polymerase II-dependent transcription. Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ∼55-70% of the proteome by mass, showed mostly condition-specific regulation. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate-dependent and nutrient-specific gene expression

Software: Fission yeast obeys a linear size law under nutrient titration

R package containing single-cell size and geometry data, including culture conditions and summary statistics, and code used to generate the figure panels

Noise propagation in an integrated model of bacterial gene expression and growth

In bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing >1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements

Noise propagation in an integrated model of bacterial gene expression and growth.

In bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing >1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements

Noise propagation in an integrated model of bacterial gene expression and growth

In bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing >1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements