Mathematical Modelling and Effect Size Analysis in Support of Searching for the Proteomic Signature of Radiotherapy Toxicity Development of new technologies has resulted in the significant expansion of biological research, among which studies in the area of genomics, transcriptomics, proteomics, and metabolomics are the leading ones. In the majority of omics studies, the goal is to identify reliable molecular biomarkers and pathways associated with the examined process. In almost all cases, a list of differentially expressed genes or proteins is constructed, which is not easy to obtain for some experimental designs. In our work, we mainly focus on the experiments with small sample size. The goal was to determine the robust proteomic signature of radiation exposure in the mouse model. Our selection algorithm combines mathematical modelling of signal and its fold change distributions with the comprehensive effect size analysis. Thanks to the datadriven automated thresholding of the protein absolute or relative (fold change) expressions, and Cohens effect size based filters, the obtained proteomic signature demonstrated a higher level of consistency and functional coherency. The additional, intuitively expected, signalling pathways were identified when compared to the standard statistical approach.

A MapReduce tool for indepth analysis of KEGG pathways: identification and visualization of therapeutic target candidates Intracellular biochemical reactions emerge from the interaction among multiple extracellular signaling components. Considering the number, type and connections of the signaling components represents a needed step to characterize, identify and describe potential targets for a clinical purpose. However, it is increasingly documented that the presence of subtypes of signaling proteins, branching and crosstalk may lead to very variable outcomes in the same path, which is not always well defined experimentally. For this reason, we present an improved version of the algorithm based on the MapReduce paradigm to facilitate the discovery of new therapeutic targets. Our algorithm allows you to scan and perform a search in depth of biological pathway in order to analyze less recurrent and therefore nontrivial paths. These routes represent a chain of biochemical interactions among different biological actors that can be represented by quite distant nodes along the pathway. This type of analysis can also be performed manually, but with high execution times due to the large amount of pathways and genes present. Thus, our tool performs exhaustive analysis in an automated way, drastically reducing the time required. Our proposal allows us to discover the genes far from the initial target genes, also showing the number of occurrences of a given path found within the set of biological pathways analyzed during the simulation.