Modular Response Analysis (MRA) is a method to reconstruct signalling networks from steady-state perturbation data which has frequently been used in different settings. Since these data are usually noisy due to multi-step measurement procedures and biological variability, it is important to investigate the effect of this noise onto network reconstruction. Here we present a systematic study to investigate propagation of noise from concentration measurements to network structures. Therefore, we design an in silico study of the MAPK and the p53 signalling pathways with realistic noise settings. We make use of statistical concepts and measures to evaluate accuracy and precision of individual inferred interactions and resulting network structures. Our results allow to derive clear recommendations to optimize the performance of MRA based network reconstruction: First, large perturbations are favorable in terms of accuracy even for models with non-linear steady-state response curves. Second, a single control measurement for different perturbation experiments seems to be sufficient for network reconstruction, and third, we recommend to execute the MRA workflow with the mean of different replicates for concentration measurements rather than using computationally more involved regression strategies.
Here we present a minimal mathematical model for the sphingomyelin synthase 1 (SMS1) driven conversion of ceramide to sphingomyelin based on chemical reaction kinetics. We demonstrate via mathematical analysis that this model is not able to qualitatively reproduce experimental measurements on lipid compositions after altering SMS1 activity. We prove that a positive feedback mechanism from the products to the reactants of the reaction is one possible model extension to explain these specific experimental data. The proposed mechanism in fact exists in vivo via protein kinase D and the ceramide transfer protein CERT. The model is further evaluated by additional observations from the literature.
BackgroundPositive and negative feedback loops are ubiquitous motifs in biochemical signaling pathways. The mitogen-activated protein kinase (MAPK) pathway module is part of many distinct signaling networks and comprises several of these motifs, whose functioning depends on the cell line at hand and on the particular context.The maintainance of specificity of the response of the MAPK module to distinct stimuli has become a key paradigm especially in PC-12 cells, where the same module leads to different cell fates, depending on the stimulating growth factor.This cell fate is regulated by differences in the ERK (MAPK) activation profile, which shows a transient response upon stimulation with EGF, while the response is sustained in case of NGF. This behavior was explained by different effective network topologies. It is widely believed that this sustained response requires a bistable system.ResultsIn this study we present a sampling-based Bayesian model analysis on a dataset, in which PC-12 cells have been stimulated with different growth factors. This is combined with novel analysis methods to investigate the role of feedback interconnections to shape ERK response. Results strongly suggest that, besides bistability, an additional effect called quasi-bistability can contribute to explain the observed responses of the system to different stimuli. Quasi-bistability is the ability of a monostable system to maintain two distinct states over a long time period upon a transient signal, which is also related to positive feedback, but cannot be detected by standard steady state analysis methods.ConclusionsAlthough applied on a specific example, our framework is generic enough to be also relevant for other regulatory network modeling studies that comprise positive feedback to explain cellular decision making processes. Overall, this study advices to focus not only on steady states, but also to take transient behavior into account in the analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0392-6) contains supplementary material, which is available to authorized users.
Background:The description of intracellular processes based on chemical reaction kinetics has become a standard approach in the last decades, and parameter estimation poses several challenges. Sensitivity analysis is a powerful tool in model development that can aid model calibration in various ways. Results can for example be used to simplify the model by elimination or fixation of parameters that have a negligible influence on relevant model outputs. However, models are usually subject to rescaling and normalization to reference experiments, which changes the variance of the output. Thus, the results of the sensitivity analysis may change depending on the choice of these rescaling factors and reference experiments. Although it might intuitively be clear, this fact has not been addressed in the literature so far. Methods: In this study we investigate the effect of model rescaling and additional normalization to a reference experiment on the outcome of two different sensitivity analyses. Results are exemplified on a model for the MAPK pathway module in PC-12 cell lines. For this purpose we apply local sensitivity analysis and a global variance-based method based on Sobol sensitivity coefficients, and compare the results for differently scaled and normalized model versions. Results: Results indicate that both sensitivity analyses are invariant under simple rescaling of variables and parameters with constant factors, provided that sensitivity coefficients are normalized and that the parameter space is appropriately chosen for Sobol's method. By contrast, normalization to a reference experiment that also depends on parameters has a large impact on the results of any sensitivity analysis, and in particular complicates the interpretation. Conclusion: This work shows that, in order to perform sensitivity analysis, it is necessary to take into account the dependency on parameters of the reference condition when working with normalized model versions.
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