Robust hematopoietic progenitor cell commitment in the presence of a conflicting cue

Trajectory-based energy landscapes of gene regulatory networks

Multistability and natural biological variability can result in significant heterogeneity within a cell population, leading to challenges in understanding and modulating cell behavior. Energy landscapes can offer qualitatively intuitive visualizations of cell phenotype and facilitate a more quantitative understanding of cellular dynamics, but current methods for landscape generation are mathematically involved and often require specific system properties (e.g., ergodicity or independent gene/protein probability distributions) that do not always hold. Here, we present a simple kinetic Monte Carlo-based method for landscape generation from a system of ordinary differential equations using only simulation trajectories initialized throughout the phase space of interest. The resulting landscape produces three quantitative features relevant to understanding cell behavior: stability (reflected by the depth or potential of landscape valleys), velocity (representing average directional movement on the landscape), and variance in velocity (indicative of landscape positions with heterogeneous movements). We applied this method to a genetic toggle switch, a core decision-making network in binary cellular responses, to elucidate effects of biologically relevant intrinsic and extrinsic cues. Intrinsic noise, such as stochasticity in transcription-translation and differences in cell cycle position, manifests through changes in valley width and position, reflecting increased population heterogeneity and more probabilistic cell fate transitions. The landscapes also capture the effect of an external inducer, revealing a quantitative correlation between the rate of cell fate transition and the energy barrier above a threshold inducer concentration determined by the permissivity of the valley. Further, in tracking dynamically changing landscapes under time-varying external cues, we unexpectedly found that an oscillatory inducer input can modulate cell fate heterogeneity and lead to periodic cell fate transitions entrained to the input frequency, depending on the intrinsic degradation rate of the switch. The landscape generation approach outlined herein is generalizable to other network topologies and may provide new quantitative insights into their dynamics.

Variable cellular decision-making behavior in a constant synthetic network topology

Background

Modules of interacting components arranged in specific network topologies have evolved to perform a diverse array of cellular functions. For a network with a constant topological structure, its function within a cell may still be tuned by changing the number of instances of a particular component (e.g., gene copy number) or by modulating the intrinsic biochemical properties of a component (e.g., binding strength or catalytic efficiency). How such perturbations affect cellular response dynamics remains poorly understood. Here, we explored these effects in a common decision-making motif, cross-antagonism with autoregulation, by synthetically constructing this network in yeast.

Results

We employed the engineering design strategy of reuse to build this topology with a single protein building block, TetR, creating necessary components through TetR mutations and fusion partners. We then studied the impact of several topology-preserving perturbations – strength of cross-antagonism, number of operator sites in a promoter, and gene dosage – on decision-making behavior. We found that reducing TetR repression strength, which hinders cross-antagonism, resulted in a loss of mutually exclusive cell responses. Unexpectedly, increasing the number of operator sites also impeded decision-making exclusivity, which may be a consequence of the averaging effect that arises when multiple transcriptional activators and repressors are accommodated at a given locus. Stochastic simulations of this topology revealed that, even for networks with high TetR repression strength and a low number of operator sites, increasing gene dosage can reduce exclusivity in response dynamics. We further demonstrated this result experimentally by quantifying gene copy numbers in selected yeast clones with differing phenotypic responses.

Conclusions

Our study illustrates how parameters that do not change the topological structure of a decision-making network can nonetheless exert significant influence on its response dynamics. These findings should further inform the study of native motifs, including the effects of topology-preserving mutations, and the robust engineering of synthetic networks.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2866-6) contains supplementary material, which is available to authorized users.

Towards a Quantitative Understanding of Cell Identity

Cells have traditionally been characterized using expression levels of a few proteins that are thought to specify phenotype. This requires a priori selection of proteins, which can introduce descriptor bias, and neglects the wealth of additional molecular information nested within each cell in a population, which often makes these sparse descriptors qualitative. Recently, more unbiased and quantitative cell characterization has been made possible by new high-throughput, information-dense experimental approaches and data-driven computational methods. This review discusses such quantitative descriptors in the context of three central concepts of cell identity: definition, creation, and stability. Collectively, these concepts are essential for constructing quantitative phenotypic landscapes, which will enhance our understanding of cell biology and facilitate cell engineering for research and clinical applications.