Modularity and predictability in cell signaling and decision making

Abstract

Cells make decisions to differentiate, divide, or apoptose based on multiple signals of internal and external origin. These decisions are discrete outputs from dynamic networks comprised of signaling pathways. Yet the validity of this decomposition of regulatory proteins into distinct pathways is unclear because many regulatory proteins are pleiotropic and interact through cross-talk with components of other pathways. In addition to the deterministic complexity of interconnected networks, there is stochastic complexity arising from the fluctuations in concentrations of regulatory molecules. 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. Thus, if everything is connected and stochastic, what hope could we have for a quantitative understanding of cellular decisions? 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.

FIGURE 1:

Hierarchies of organization from network to pathway to motifs. (A) In the network view, pathways are usually not readily discernible because of many apparent interactions between pathways from genomic studies (the pathway in question similarly colored in all panels). We note that in many network analyses, there is no information about the functional significance of interactions. (B) Even though the identity of components (receptors, kinases, transcription factors, downstream targets) is usually indicated in the pathway view, it is a static description of a dynamic system. (C) The separation of time scales may allow the analysis of groups of network components as functionally distinct motifs. The example shown here is idealized because for most pathways; not all components can be so easily broken down into motifs. In this example, “fast” indicates the phosphorylation time scale (∼1 min), and “slow” signifies transcription time scale (∼15 min to 1 h) typical of yeast.

FIGURE 2:

Predictability of the proliferation decision in budding yeast. (A) Cells that are committed to the cell cycle do not arrest upon exposure to mating pheromone. Whi5 activity was measured up until pheromone addition, and this information was used to predict whether a cell would arrest or divide when exposed to pheromone. (B) Histograms and logistic regression curve showing the ability of a single Whi5 measurement at the time of pheromone exposure to predict cell state. The shaded region in the logistic regression indicates 95% confidence interval (by bootstrapping). Data from Doncic et al. (2011).