(Tissue) P Systems with Vesicles of Multisets

We consider tissue P systems working on vesicles of multisets with the very simple operations of insertion, deletion, and substitution of single objects. With the whole multiset being enclosed in a vesicle, sending it to a target cell can be indicated in those simple rules working on the multiset. As derivation modes we consider the sequential mode, where exactly one rule is applied in a derivation step, and the set maximal mode, where in each derivation step a non-extendable set of rules is applied. With the set maximal mode, computational completeness can already be obtained with tissue P systems having a tree structure, whereas tissue P systems even with an arbitrary communication structure are not computationally complete when working in the sequential mode. Adding polarizations (-1, 0, 1 are sufficient) allows for obtaining computational completeness even for tissue P systems working in the sequential mode.Comment: In Proceedings AFL 2017, arXiv:1708.0622

Poster - Natural variation in root morphology changes upon salt stress

This is a poster presented at the SEB meeting, Manchester, in 2014. The results shown in here are published in the Plant Physiology paper<div><br></div><div>Julkowska et al., Capturing Arabidopsis root architecture dynamics with ROOT-FIT reveals diversity in responses to salinity</div><div></div><div><div><p><br></p><p>Published September 2014. DOI: https://doi.org/10.1104/pp.114.248963</p></div></div><div></div><div><div></div></div

Additional file 1: of Quantitative proteomics analysis of an ethanol- and a lactate-producing mutant strain of Synechocystis sp. PCC6803

Table S1. Strains used in this study. Table S2. A total of 1039 normalized protein 14N/15N isotopic ratios including their mascot scores from 3 biological replicates quantified in Synechocystis sp. PCC6803 wild-type (WT) strain, ethanol-forming strain (SAA012) and lactic acid-forming strain (SAW041). Table S3. List of primers used in RT-qPCR. Table S4. List of proteins quantified in SAA012 that were signifcantly changed and its p-value from z-test. Table S5. List of proteins quantified in SAW041 that were signifcantly changed and its p-value from z-test. Table S6. Results of KEGG pathway enrichment analyses of the significantly altered proteins quantified in the ethanol-producing strain (a) and the lactic acid-producing strain (b)

Additional file 1 of Analyzing postprandial metabolomics data using multiway models: a simulation study

Additional file 1. A list of parameters that are randomly perturbated to generate different individuals

Identification of Analytical Factors Affecting Complex Proteomics Profiles Acquired in a Factorial Design Study with Analysis of Variance: Simultaneous Component Analysis

Complex shotgun proteomics peptide profiles obtained in quantitative differential protein expression studies, such as in biomarker discovery, may be affected by multiple experimental factors. These preanalytical factors may affect the measured protein abundances which in turn influence the outcome of the associated statistical analysis and validation. It is therefore important to determine which factors influence the abundance of peptides in a complex proteomics experiment and to identify those peptides that are most influenced by these factors. In the current study we analyzed depleted human serum samples to evaluate experimental factors that may influence the resulting peptide profile such as the residence time in the autosampler at 4 °C, stopping or not stopping the trypsin digestion with acid, the type of blood collection tube, different hemolysis levels, differences in clotting times, the number of freeze–thaw cycles, and different trypsin/protein ratios. To this end we used a two-level fractional factorial design of resolution IV (2_IV^7‑3). The design required analysis of 16 samples in which the main effects were not confounded by two-factor interactions. Data preprocessing using the Threshold Avoiding Proteomics Pipeline (Suits, F.; Hoekman, B.; Rosenling, T.; Bischoff, R.; Horvatovich, P. Anal. Chem. 2011, 83, 7786−7794, ref ) produced a data-matrix containing quantitative information on 2 559 peaks. The intensity of the peaks was log-transformed, and peaks having intensities of a low <i>t</i>-test significance (<i>p</i>-value > 0.05) and a low absolute fold ratio (<2) between the two levels of each factor were removed. The remaining peaks were subjected to analysis of variance (ANOVA)-simultaneous component analysis (ASCA). Permutation tests were used to identify which of the preanalytical factors influenced the abundance of the measured peptides most significantly. The most important preanalytical factors affecting peptide intensity were (1) the hemolysis level, (2) stopping trypsin digestion with acid, and (3) the trypsin/protein ratio. This provides guidelines for the experimentalist to keep the ratio of trypsin/protein constant and to control the trypsin reaction by stopping it with acid at an accurately set pH. The hemolysis level cannot be controlled tightly as it depends on the status of a patient’s blood (e.g., red blood cells are more fragile in patients undergoing chemotherapy) and the care with which blood was sampled (e.g., by avoiding shear stress). However, its level can be determined with a simple UV spectrophotometric measurement and samples with extreme levels or the peaks affected by hemolysis can be discarded from further analysis. The loadings of the ASCA model led to peptide peaks that were most affected by a given factor, for example, to hemoglobin-derived peptides in the case of the hemolysis level. Peak intensity differences for these peptides were assessed by means of extracted ion chromatograms confirming the results of the ASCA model