Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that, because of limitations in levels of cellular energy, free ribosomes, and proteins, are faced by all living cells and we construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth-related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modeling framework has potentially wide application, including in both biotechnology and medicine.systems biology | synthetic biology | mathematical cell model | host-circuit interactions | evolutionarily stable strategy I ntracellular processes rarely work in isolation but continually interact with the rest of the cell. Yet often we study cellular processes with the implicit assumption that the remainder of the cell can either be ignored or provides a constant, background environment. Work in both systems and synthetic biology is, however, showing that this assumption is weak, at best. In microbes, growth rate can affect the expression both of single genes (1, 2) and across the entire genome (3-6). Specific control by transcription factors seems to be complemented by global, unspecific regulation that reflects the physiological state of the cell (5-7). Correspondingly, progress in synthetic biology is limited by two-way interactions between synthetic circuits and the host cell that cannot be designed away (8, 9).These phenomena are thought to arise from trade-offs where commitment of a finite intracellular resource to one response necessarily reduces the commitment of that resource to another response. A trade-off in the allocation of ribosomes has been suggested to underlie global gene regulation (2, 5). Similarly, depletion of finite resources and competition for cellular processes is thought to explain the failure of some synthetic circuits (8). Such circuits "load" the host cell, which can induce physiological responses that further degrade the function of the circuit (10). Our understanding of such trade-offs, however, is mostly phenomenological.Here we take an alternative approach and ask what new insi...

SummaryEmbryonic stem cell (ESC) pluripotency is governed by a gene regulatory network centered on the transcription factors Oct4 and Nanog. To date, robust self-renewing ESC states have only been obtained through the chemical inhibition of signaling pathways or enforced transgene expression. Here, we show that ESCs with reduced Oct4 expression resulting from heterozygosity also exhibit a stabilized pluripotent state. Despite having reduced Oct4 expression, Oct4+/− ESCs show increased genome-wide binding of Oct4, particularly at pluripotency-associated enhancers, homogeneous expression of pluripotency transcription factors, enhanced self-renewal efficiency, and delayed differentiation kinetics. Cells also exhibit increased Wnt expression, enhanced leukemia inhibitory factor (LIF) sensitivity, and reduced responsiveness to fibroblast growth factor. Although they are able to maintain pluripotency in the absence of bone morphogenetic protein, removal of LIF destabilizes pluripotency. Our findings suggest that cells with a reduced Oct4 concentration range are maintained in a robust pluripotent state and that the wild-type Oct4 concentration range enables effective differentiation.

Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. Alongside we provide a theory to analyse stochastic chemical reactions coupled with cell divisions, enabling efficient parameter estimation, sensitivity analysis and hypothesis testing. The cell model recovers population-averaged data on growth-dependence of bacterial physiology and how growth variations in single cells change across conditions. We identify processes responsible for this variation and reconstruct the propagation of initial fluctuations to growth and other processes. Finally, we study drug-nutrient interactions and find that antibiotics can both enhance and suppress growth heterogeneity. Our results provide a predictive framework to integrate heterogeneous data and draw testable predictions with implications for antibiotic tolerance, evolutionary and synthetic biology.