Browsing by Author "Thomaseth, Caterina"

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Open Access
Sampling-based Bayesian approaches reveal the importance of quasi-bistable behavior in cellular decision processes on the example of the MAPK signaling pathway in PC-12 cell lines
(2017) Jensch, Antje; Thomaseth, Caterina; Radde, Nicole
Positive 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. In 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.
Open Access
A statistical framework to optimize experimental design for inference problems in systems biology based on normalized data
(2022) Thomaseth, Caterina; Radde, Nicole (Prof. Dr. rer. nat.)
Inference problems in Systems Biology are primarily based on the theoretical assumption that a measured dataset comprises noisy realizations following some underlying stochastic distribution, having well-defined statistical properties. This uncertainty in the input quantities propagates through the inference process, influences the uncertainty of the estimated model parameters and subsequently affects the quality and reliability of model predictions. Understanding the mechanisms of noise propagation over an inference problem will therefore be instrumental in designing an optimal and robust experimental protocol to reduce the uncertainty of the estimated quantities of interest. This thesis investigates the underlying mechanisms of noise propagation from measured experimental data to estimated parameters by developing a statistical framework to characterize and analyse non-linear transformations of stochastic distributions. Among such non-linear transformations, data normalization, a required step for some common experimental techniques, requires specific attention, representing an additional modification of noise properties. Mathematically, the normalization step translates into ratios of two distributions. We consider standard assumptions on the distributions associated with biological raw data. In this thesis we explore three specific classes of inference problems relevant for Systems Biology applications. At first we consider the problem of statistical inference of different parametrized error models for normalized data. Subsequently, we investigate the effect of such error models when coupled with different normalization strategies on results of parameter estimation for dynamic models of biochemical reaction networks. We conclude this thesis by analysing the effects of noise propagation on Modular Response Analysis based network reconstruction. From our simulation results, we observe that non-linear noise transformations may lead to very uncertain and/or erroneous inference results. Additionally, based on the quantification of statistical measures for accuracy and precision of the inference results, we derive practical advice for an optimized and robust experimental design in order to reduce the uncertainty of the estimated quantities.