References of "Baumuratova, Tatiana 40020841"
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See detailSwitch of Sensitivity Dynamics Revealed with DyGloSA Toolbox for Dynamical Global Sensitivity Analysis as an Early Warning for System's Critical Transition.
Baumuratova, Tatiana UL; Dobre, Simona; Bastogne, Thierry et al

in PloS one (2013), 8(12), 82973

Systems with bifurcations may experience abrupt irreversible and often unwanted shifts in their performance, called critical transitions. For many systems like climate, economy, ecosystems it is highly ... [more ▼]

Systems with bifurcations may experience abrupt irreversible and often unwanted shifts in their performance, called critical transitions. For many systems like climate, economy, ecosystems it is highly desirable to identify indicators serving as early warnings of such regime shifts. Several statistical measures were recently proposed as early warnings of critical transitions including increased variance, autocorrelation and skewness of experimental or model-generated data. The lack of automatized tool for model-based prediction of critical transitions led to designing DyGloSA - a MATLAB toolbox for dynamical global parameter sensitivity analysis (GPSA) of ordinary differential equations models. We suggest that the switch in dynamics of parameter sensitivities revealed by our toolbox is an early warning that a system is approaching a critical transition. We illustrate the efficiency of our toolbox by analyzing several models with bifurcations and predicting the time periods when systems can still avoid going to a critical transition by manipulating certain parameter values, which is not detectable with the existing SA techniques. DyGloSA is based on the SBToolbox2 and contains functions, which compute dynamically the global sensitivity indices of the system by applying four main GPSA methods: eFAST, Sobol's ANOVA, PRCC and WALS. It includes parallelized versions of the functions enabling significant reduction of the computational time (up to 12 times). DyGloSA is freely available as a set of MATLAB scripts at http://bio.uni.lu/systems_biology/software/dyglosa. It requires installation of MATLAB (versions R2008b or later) and the Systems Biology Toolbox2 available at www.sbtoolbox2.org. DyGloSA can be run on Windows and Linux systems, -32 and -64 bits. [less ▲]

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See detailDesigning logical rules to model the response of biomolecular networks with complex interactions: an application to cancer modeling.
Guziolowski, Carito; Blachon, Sylvain; Baumuratova, Tatiana UL et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2011), 8(5), 1223-34

We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with posttranslational ... [more ▼]

We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with posttranslational interactions, we consider two types of nodes in the network, corresponding to proteins and to RNA. Microarray data provides very lacunar information for such types of networks because protein nodes, although needed in the model, are not observed. Propagation of observations in such networks leads to poor and nonsignificant model predictions, mainly because rules used to propagate information--usually disjunctive constraints--are weak. Here, we propose a new, stronger type of logical constraints that allow us to strengthen the analysis of the relation between microarray and interaction data. We use these rules to identify the nodes which are responsible for a phenotype, in particular for cell cycle progression. As the benchmark, we use an interaction network describing major pathways implied in Ewing's tumor development. The Python library used to obtain our results is publicly available on our supplementary web page. [less ▲]

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See detailLocalizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network.
Baumuratova, Tatiana UL; Surdez, Didier; Delyon, Bernard et al

in BMC Systems Biology (2010), 4

BACKGROUND: A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post ... [more ▼]

BACKGROUND: A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level. RESULTS: We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational. Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic constraint-based analysis mimicking biologist's local reasoning on a large scale. The post-processing relied on differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing's sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts of the network which were incomplete and should be submitted for further investigation. CONCLUSIONS: The proposed approach is effective for the qualitative analysis of cancer networks. It allows the integrative use of experimental data of various types in order to identify the specific information that should be considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be introduced into the analysis to improve the network representation and make it more specific. [less ▲]

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