Modularity and predictability in cell signaling and decision making.
Bottom Line: Even within a genetically identical population of cells grown in the same environment, cell-to-cell variations in mRNA and protein concentrations can be as high as 50% in yeast and even higher in mammalian cells.Here we discuss the implications of recent advances in genomics, single-cell, and single-cell genomics technology for network modularity and cellular decisions.On the basis of these recent advances, we argue that most gene expression stochasticity and pathway interconnectivity is nonfunctional and that cellular decisions are likely much more predictable than previously expected.
Affiliation: Department of Biology, Stanford University, Stanford, CA 94305.Show MeSH
Related in: MedlinePlus
Mentions: Perhaps one of the most fundamental state transitions in yeast is the commitment to cell division. After commitment, yeast is insensitive to even high levels of mating pheromone (Hartwell et al., 1974). When this commitment point was investigated quantitatively, it was found that the measurement of cell size or time after the previous cell division is a poor predictor of whether a cell has committed to cell division, that is, whether it would not arrest upon pheromone exposure. However, the measurement of the nuclear localization of a single protein, Whi5, could predict the subsequent divide-or-arrest decision in >97% of cells (Doncic et al., 2011; Figure 2). That is, almost all cells that have inactivated more than a threshold level of Whi5 do not arrest upon pheromone exposure, whereas cells that have inactivated less Whi5 arrest immediately. Of interest, the corresponding cell division commitment decision in mammalian cells could be similarly predicted by the measurement of only Cdk2 activity (Johnson, 2014). Note that in both cases, the prediction relies only on information gathered before the step change in inputs. We note that the use of measurements after a step change to “predict” previous cell state is not really a prediction, as is commonly done (e.g., Spencer et al., 2013), but rather a postdiction.
Affiliation: Department of Biology, Stanford University, Stanford, CA 94305.