Dose-to-Duration Encoding and Signaling beyond Saturation in Intracellular Signaling Networks

The cellular response elicited by an environmental cue typically varies with the strength of the stimulus. For example, in the yeast Saccharomyces cerevisiae, the concentration of mating pheromone determines whether cells undergo vegetative growth, chemotropic growth, or mating. This implies that the signaling pathways responsible for detecting the stimulus and initiating a response must transmit quantitative information about the intensity of the signal. Our previous experimental results suggest that yeast encode pheromone concentration as the duration of the transmitted signal. Here we use mathematical modeling to analyze possible biochemical mechanisms for performing this “dose-to-duration” conversion. We demonstrate that modulation of signal duration increases the range of stimulus concentrations for which dose-dependent responses are possible; this increased dynamic range produces the counterintuitive result of “signaling beyond saturation” in which dose-dependent responses are still possible after apparent saturation of the receptors. We propose a mechanism for dose-to-duration encoding in the yeast pheromone pathway that is consistent with current experimental observations. Most previous investigations of information processing by signaling pathways have focused on amplitude encoding without considering temporal aspects of signal transduction. Here we demonstrate that dose-to-duration encoding provides cells with an alternative mechanism for processing and transmitting quantitative information about their surrounding environment. The ability of signaling pathways to convert stimulus strength into signal duration results directly from the nonlinear nature of these systems and emphasizes the importance of considering the dynamic properties of signaling pathways when characterizing their behavior. Understanding how signaling pathways encode and transmit quantitative information about the external environment will not only deepen our understanding of these systems but also provide insight into how to reestablish proper function of pathways that have become dysregulated by disease

Information processing and signal integration in bacterial quorum sensing

Bacteria communicate using secreted chemical signaling molecules called autoinducers in a process known as quorum sensing. The quorum-sensing network of the marine bacterium {\it Vibrio harveyi} employs three autoinducers, each known to encode distinct ecological information. Yet how cells integrate and interpret the information contained within the three autoinducer signals remains a mystery. Here, we develop a new framework for analyzing signal integration based on Information Theory and use it to analyze quorum sensing in {\it V. harveyi}. We quantify how much the cells can learn about individual autoinducers and explain the experimentally observed input-output relation of the {\it V. harveyi} quorum-sensing circuit. Our results suggest that the need to limit interference between input signals places strong constraints on the architecture of bacterial signal-integration networks, and that bacteria likely have evolved active strategies for minimizing this interference. Here we analyze two such strategies: manipulation of autoinducer production and feedback on receptor number ratios.Comment: Supporting information is in appendi

Trade-offs and Noise Tolerance in Signal Detection by Genetic Circuits

Genetic circuits can implement elaborated tasks of amplitude or frequency signal detection. What type of constraints could circuits experience in the performance of these tasks, and how are they affected by molecular noise? Here, we consider a simple detection process–a signal acting on a two-component module–to analyze these issues. We show that the presence of a feedback interaction in the detection module imposes a trade-off on amplitude and frequency detection, whose intensity depends on feedback strength. A direct interaction between the signal and the output species, in a type of feed-forward loop architecture, greatly modifies these trade-offs. Indeed, we observe that coherent feed-forward loops can act simultaneously as good frequency and amplitude noise-tolerant detectors. Alternatively, incoherent feed-forward loop structures can work as high-pass filters improving high frequency detection, and reaching noise tolerance by means of noise filtering. Analysis of experimental data from several specific coherent and incoherent feed-forward loops shows that these properties can be realized in a natural context. Overall, our results emphasize the limits imposed by circuit structure on its characteristic stimulus response, the functional plasticity of coherent feed-forward loops, and the seemingly paradoxical advantage of improving signal detection with noisy circuit components

Compartmentalization of a Bistable Switch Enables Memory to Cross a Feedback-Driven Transition

SummaryCells make accurate decisions in the face of molecular noise and environmental fluctuations by relying not only on present pathway activity, but also on their memory of past signaling dynamics. Once a decision is made, cellular transitions are often rapid and switch-like due to positive feedback loops in the regulatory network. While positive feedback loops are good at promoting switch-like transitions, they are not expected to retain information to inform subsequent decisions. However, this expectation is based on our current understanding of network motifs that accounts for temporal, but not spatial, dynamics. Here, we show how spatial organization of the feedback-driven yeast G1/S switch enables the transmission of memory of past pheromone exposure across this transition. We expect this to be one of many examples where the exquisite spatial organization of the eukaryotic cell enables previously well-characterized network motifs to perform new and unexpected signal processing functions

BiP Binding to the ER-Stress Sensor Ire1 Tunes the Homeostatic Behavior of the Unfolded Protein Response

Computational modeling and experimentation in the unfolded protein response reveals a role for the ER-resident chaperone protein BiP in fine-tuning the system's response dynamics

Identifying signaling pathway architectures using model-driven experimental design

All cells rely on intracellular signaling pathways to respond to environmental cues. These pathwas are comprised of tiers of signaling molecules, such as enzymes or metabolites, that direct specific subcellular events. How information is transmitted throughout can be complex and can depend on many tightly-controlled non-linear interactions. In this work, we combine computational modeling and bench experimentation to understand and identify signaling mechanisms driving complex behavior. We apply “model-driven experimental design” in which we use competing models trained on our experimental data to predict new signaing behavior that we then use to validate the models with experimental results. Using this approach, we identified a novel positive feedback mechanism in the High Osmolarity Glycerol (HOG) pathway in yeast. We then use a similar model to compare the difference between activation and nuclear translocation behaviors of the key HOG pathway Mitogen-Activated Protein Kinase (MAPK). In contrast to the current literature, we find that these two dynamics are indeed distinct. We suspect that these dynamics are responsible for driving differential cytosolic and nuclear responses and propose computational and bench experiments for follow up. Finally, we discuss the importance of this work in both the contexts of understanding MAPK signaling regulation and using model-driven experimental design.Doctor of Philosoph

Computational analysis of signaling patterns in single cells

Signaling proteins are flexible in both form and function. They can bind to multiple molecular partners and integrate diverse types of cellular information. When imaged by time-lapse microscopy, many signaling proteins show complex patterns of activity or localization that vary from cell to cell. This heterogeneity is so prevalent that it has spurred the development of new computational strategies to analyze single-cell signaling patterns. A collective observation from these analyses is that cells appear less heterogeneous when their responses are normalized to, or synchronized with, other single-cell measurements. In many cases, these transformed signaling patterns show distinct dynamical trends that correspond with predictable phenotypic outcomes. When signaling mechanisms are unclear, computational models can suggest putative molecular interactions that are experimentally testable. Thus, computational analysis of single-cell signaling has not only provided new ways to quantify the responses of individual cells, but has helped resolve longstanding questions surrounding many well-studied human signaling proteins including NF-κB, p53, ERK1/2, and CDK2. A number of specific challenges lie ahead for single-cell analysis such as quantifying the contribution of non-cell autonomous signaling as well as the characterization of protein signaling dynamics in vivo

MAPK feedback encodes a switch and timer for tunable stress adaptation in yeast

Signaling pathways can behave as switches or rheostats, generating binary or graded responses to a given cell stimulus. We evaluated whether a single signaling pathway can simultaneously encode a switch and a rheostat. We found that the kinase Hog1 mediated a bifurcated cellular response: Activation and commitment to adaptation to osmotic stress are switch-like, whereas protein induction and the resolution of this commitment are graded. Through experimentation, bioinformatics analysis, and computational modeling, we determined that graded recovery is encoded through feedback phosphorylation and a gene induction program that is both temporally staggered and variable across the population. This switch-to-rheostat signaling mechanism represents a versatile stress adaptation system, wherein a broad range of inputs generate an “all-in” response that is later tuned to allow graded recovery of individual cells over time