Multiplexing information flow through dynamic signalling systems

We consider how a signalling system can act as an information hub by multiplexing information arising from multiple signals. We formally define multiplexing, mathematically characterise which systems can multiplex and how well they can do it. While the results of this paper are theoretical, to motivate the idea of multiplexing, we provide experimental evidence that tentatively suggests that the NF-κB transcription factor can multiplex information about changes in multiple signals. We believe that our theoretical results may resolve the apparent paradox of how a system like NF-κB that regulates cell fate and inflammatory signalling in response to diverse stimuli can appear to have the low information carrying capacity suggested by recent studies on scalar signals. In carrying out our study, we introduce new methods for the analysis of large, nonlinear stochastic dynamic models, and develop computational algorithms that facilitate the calculation of fundamental constructs of information theory such as Kullback–Leibler divergences and sensitivity matrices, and link these methods to a new theory about multiplexing information. We show that many current models such as those of the NF-κB system cannot multiplex effectively and provide models that overcome this limitation using post-transcriptional modifications

Genetic redundancies enhance information transfer in noisy regulatory circuits

[EN] Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions. This transmission of information is subjected to molecular noise what can decrease its fidelity. Here, we show instead how such intrinsic noise enhances information transfer in the presence of multiple circuit copies. The result is due to the contribution of noise to the generation of autonomous responses by each copy, which are altogether associated with a common decision. Moreover, factors that correlate the responses of the redundant units (extrinsic noise or regulatory cross-talk) contribute to reduce fidelity, while those that further uncouple them (heterogeneity within the copies) can lead to stronger information gain. Overall, our study emphasizes how the interplay of signal thresholding, redundancy, and noise influences the accuracy of cellular decision making. Understanding this interplay provides a basis to explain collective cell signaling mechanisms, and to engineer robust decisions with noisy genetic circuits.This work has been supported by BFU2015-66894-P (MINECO/FEDER) and GV/2016/079 (GVA) Grants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Rodrigo Tarrega, G.; Poyatos, JF. (2016). Genetic redundancies enhance information transfer in noisy regulatory circuits. PLoS Computational Biology. 12(10). https://doi.org/10.1371/journal.pcbi.1005156S121

Dynamic Localization of the Cyanobacterial Circadian Clock Proteins

Background: The cyanobacterial circadian clock system has been extensively studied, and the structures, interactions, and biochemical activities of the central oscillator proteins (KaiA, KaiB, and KaiC) have been well elucidated. Despite this rich repository of information, little is known about the distribution of these proteins within the cell. Results: Here we report that KaiA and KaiC localize as discrete foci near a single pole of cells in a clock-dependent fashion, with enhanced polar localization observed at night. KaiA localization is dependent on KaiC; consistent with this notion, KaiA and KaiC colocalize with each other, as well as with CikA, a key input and output factor previously reported to display unipolar localization. The molecular mechanism that localizes KaiC to the poles is conserved in Escherichia coli, another Gram-negative rod-shaped bacterium, suggesting that KaiC localization is not dependent on other clock- or cyanobacterial-specific factors. Moreover, expression of CikA mutant variants that distribute diffusely results in the striking delocalization of KaiC. Conclusions: This work shows that the cyanobacterial circadian system undergoes a circadian orchestration of subcellular organization. We propose that the observed spatiotemporal localization pattern represents a novel layer of regulation that contributes to the robustness of the clock by facilitating protein complex formation and synchronizing the clock with environmental stimuli. Cohen etal. elucidate the subcellular localization patterns of the cyanobacterial circadian clock proteins. They demonstrate that KaiA and KaiC localize to the poles of cells at night in a circadian manner. This spatiotemporal pattern of localization is proposed to contribute to the synchronization and robustness of the circadian clock. © 2014 Elsevier Ltd

Evaluation of a Mathematical Model of Rat Body Weight Regulation in Application to Caloric Restriction and Drug Treatment Studies.

The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology

Accurate information transmission through dynamic biochemical signaling networks

Stochasticity inherent to biochemical reactions (intrinsic noise) and variability in cellular states (extrinsic noise) degrade information transmitted through signaling networks. We analyze the ability of temporal signal modulation, that is dynamics, to reduce noise-induced information loss. In the extracellular signal-regulated kinase (ERK), calcium (Ca(2+)), and nuclear factor kappa-B (NFκB) pathways, response dynamics resulted in significantly greater information transmission capacities compared to non-dynamic responses. Theoretical analysis demonstrated that signaling dynamics has a key role in overcoming extrinsic noise. Experimental measurements of information transmission in the ERK network under varying signal-to-noise confirmed our predictions and showed that signaling dynamics mitigate, and can potentially eliminate, extrinsic noise induced information loss. By curbing the information-degrading effects of cell-to-cell variability, dynamic responses substantially increase the accuracy of biochemical signaling networks

Model calibration against CR data.

<p>The calibrated model shows good agreement between model simulations (black) and experimental BW (A), FFM (B), and FM (C) measurements at all caloric restriction levels. Gray region indicates the intervention phase in each study. Error bars represent SEM (9-10 rats).</p

The α-free model allows for estimation of body composition changes compared to the two-dimensional model.

<p>Body composition simulations of the <i>α</i>-free model (blue) can be used to estimate FM and FFM outside of measured time points, black. The two-dimensional model simulation based on the <i>α</i> function (red) is unable to accurately capture changes in FM and FFM (purple). Only data for the 30 mg/kg dose is shown, with the purple line segments meant to guide the reader’s eye. Error bars represent SEM (9-10 rats).</p

The α-free model fits BC in CR and CB1Ra BC studies.

<p>Simulations (black) of the <i>α</i>-free model show agreement with FFM (red) and FM (yellow) measurements in CR (A) and CB1Ra (B) intervention studies. Gray region indicates treatment phases in the two studies. Error bars represent SEM (9-10 rats).</p

Model calibration against CB1Ra data.

<p>The calibrated model shows good agreement between model simulations (black) and experimental BW (A). The fitted model trajectories show poor agreement with FFM (B) and FM (C) measurements at the three drug dose levels. Gray region indicates the intervention phase in each study. Error bars represent SEM (9-10 rats).</p