Genome informatics. International Conference on Genome Informatics最新文献

We address an issue of detecting a switching mechanism in gene expression, where two genes are positively correlated for one experimental condition while they are negatively correlated for another. We compare the performance of existing methods for this issue, roughly divided into two types: interaction test (IT) and the difference of correlation coefficients. Interaction test, currently a standard approach for detecting epistasis in genetics, is the log-likelihood ratio test between two logistic regressions with/without an interaction term, resulting in checking the strength of interaction between two genes. On the other hand, two correlation coefficients can be computed for two experimental conditions and the difference of them shows the alteration of expression trends in a more straightforward manner. In our experiments, we tested three different types of correlation coefficients: Pearson, Spearman and a midcorrelation (biweight midcorrelation). The experiment was performed by using ~ 2.3 × 10(9) combinations selected out of the GEO (Gene Expression Omnibus) database. We sorted all combinations according to the p-values of IT or by the absolute values of the difference of correlation coefficients and then visually evaluated the top ranked combinations in terms of the switching mechanism. The result showed that 1) combinations detected by IT included non-switching combinations and 2) Pearson was affected by outliers easily while Spearman and the midcorrelation seemed likely to avoid them.

We present here an efficient but detailed approach to modelling Ca(2+)-induced Ca(2+) release in the diadic cleft of cardiac ventricular myocytes. In this Framework we developed a spatial resolved Ca(2+) release unit (CaRU), consisting of the junctional sarcoplasmic reticulum and the diadic cleft, with a well defined channel placement. By taking advantage of time scale separation, the model could be finally reduced to only one ordinary differential equation for describing Ca(2+) fluxes and diffusion. Additionally the channel gating is described in a stochastic way. The resulting model is able to reproduce experimental findings like the gradedness of SR release, the voltage dependence of ECC gain and typical spark life time. Due to the numerical efficiency of the model, it is suitable to use for whole cell simulations. The approach we want to use extend the developed (CaRU) to such a whole cell model is already outlined in this work.

In healthy individuals, dehydration of the body leads to release of the hormone vasopressin from the pituitary. Via the bloodstream, vasopressin reaches the collecting duct cells in the kidney, where the water channel Aquaporin-2 (AQP2) is expressed. After stimulation of the vasopressin V2 receptor by vasopressin, intracellular AQP2-containing vesicles fuse with the apical plasma membrane of the collecting duct cells. This leads to increased water reabsorption from the pro-urine into the blood and therefore to enhanced retention of water within the body. Using existing biological data we propose a mathematical model of AQP-2 trafficking and regulation in collecting duct cells. Our model includes the vasopressin receptor, adenylate cyclase, protein kinase A, and intracellular as well as membrane located AQP2. To model the chemical reactions we used ordinary differential equations (ODEs) based on mass action kinetics. We employ known protein concentrations and time series data to estimate the kinetic parameters of our model and demonstrate its validity. Through generating, testing and ranking different versions of the model, we show that some model versions can describe the data well as soon as important regulatory parts such as the reduction of the signal by internalization of the vasopressin-receptor or the negative feedback loop representing phosphodiesterase activity are included. We perform time-dependent sensitivity analysis to identify the reactions that have the greatest influence on the cAMP and membrane located AQP2 levels over time. We predict the time courses for membrane located AQP2 at different vasopressin concentrations, compare them with newly generated data and discuss the competencies of the model.

We designed a simple graphical presentation for the results of a transcription factor (TF) pattern matching analysis. The TF analysis algorithm utilized known sequence signature motifs from several databases. The graphical presentation enabled a quick overview of potential TF binding sites, their frequency and spacing on both DNA strands and thus straight forward identification of promising candidates for further experimental investigations. The developed tool was applied on in total four Saccharomyces cerevisiae gene promoter regions. The selected differentially expressed genes belong to functionally different families and encode duplicate functions, TRK1 and TRK2 as ion transporters and BMH1 and BMH2 as multiple regulators. Output evaluation revealed a number of TFs with promising differences in the promoter regions of each gene pair. Experimental investigations were performed by using corresponding TF yeast mutants for either phenotypic analysis of ion transport mediated growth or expression analysis of BMH1,2 genes. Upon phenotypic testing one TF mutant exhibited severely impaired growth under non-permissive conditions. This TF, Mot3p was identified as of most abundant potential binding sites and distinctive patterns among the TRK promoter regions.