Biomedical subjects

Maik Kschischo

Publications and source records attributed to Maik Kschischo.

4 recordsLinked to original sources

PubMed2006

Ring test assessment of the mKir2.1 growth based assay in Saccharomyces cerevisiae using parametric models and model-free fits.

Inward rectifying K+ (Kir) channels are a subfamily of the potassium channel superfamily. They mediate potassium influx into the cells, a process responding to the polarization state, a variety of intracellular messengers and specific auxiliary proteins, thereby they are involved in important physiological processes such as the pacemaker activity in the heart, insulin release, and potassium uptake in glial cells. The Saccharomyces cerevisiae mKir2.1 in vitro assay was subjected to a ring test assessment. Compound-associated mKir2.1 modulating effects were detected by growth determination of functionally complemented S. cerevisiae cells in a 96-well format within 15 h. Dose-response diagrams and EC50 value calculations were determined by parametric model and model-free fits using cubic spline interpolation. These characteristics were evaluated by statistical methods to determine reproducibility among working groups. Nonparametric bootstrap simulations of the variability of the data revealed that EC50 values of the mKir2.1 indicator strain were well-matched (81-92 microM), enabling unambiguous quantitative statements about inhibitory effects and no significant influence of the different laboratory conditions. Limitations of the assay include compounds/samples that are either insoluble under the conditions of the test or strongly cytotoxic to yeast. Thus, the described test is a sensitive and reliable tool that can be used in different laboratories and is applicable in drug discovery and development as simple and reliable prescreening procedure.

Biological Assay↗

PubMed2005

Toward an accurate statistics of gapped alignments.

Sequence alignment has been an invaluable tool for finding homologous sequences. The significance of the homology found is often quantified statistically by p-values. Theory for computing p-values exists for gapless alignments [Karlin, S., Altschul, S.F., 1990. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc. Natl. Acad. Sci. USA 87, 2264-2268; Karlin, S., Dembo A., 1992. Limit distributions of maximal segmental score among Markov-dependent partial sums. Adv. Appl. Probab. 24, 13-140], but a full generalization to alignments with gaps is not yet complete. We present a unified statistical analysis of two common sequence comparison algorithms: maximum-score (Smith-Waterman) alignments and their generalized probabilistic counterparts, including maximum-likelihood alignments and hidden Markov models. The most important statistical characteristic of these algorithms is the distribution function of the maximum score S(max), resp. the maximum free energy F(max), for mutually uncorrelated random sequences. This distribution is known empirically to be of the Gumbel form with an exponential tail P(S(max)>x) approximately exp(-lambdax) for maximum-score alignment and P(F(max)>x) approximately exp(-lambdax) for some classes of probabilistic alignment. We derive an exact expression for lambda for particular probabilistic alignments. This result is then used to obtain accurate lambda values for generic probabilistic and maximum-score alignments. Although the result demonstrated uses a simple match-mismatch scoring system, it is expected to be a good starting point for more general scoring functions.

Algorithms↗

PubMed2005

Automatic scoring and quality assessment using accuracy bounds for FP-TDI SNP genotyping data.

BACKGROUND: Human diversity, namely single nucleotide polymorphisms (SNPs), is becoming a focus of biomedical research. Despite the binary nature of SNP determination, the majority of genotyping assay data need a critical evaluation for genotype calling. We applied statistical models to improve the automated analysis of 2-dimensional SNP data. METHODS: We derived several quantities in the framework of Gaussian mixture models that provide figures of merit to objectively measure the data quality. The accuracy of individual observations is scored as the probability of belonging to a certain genotype cluster, while the assay quality is measured by the overlap between the genotype clusters. RESULTS: The approach was extensively tested with a dataset of 438 nonredundant SNP assays comprising >150,000 datapoints. The performance of our automatic scoring method was compared with manual assignments. The agreement for the overall assay quality is remarkably good, and individual observations were scored differently by man and machine in 2.6% of cases, when applying stringent probability threshold values. CONCLUSION: Our definition of bounds for the accuracy for complete assays in terms of misclassification probabilities goes beyond other proposed analysis methods. We expect the scoring method to minimise human intervention and provide a more objective error estimate in genotype calling.

Algorithms↗

PubMed2004

Target specificity analysis of the Abl kinase using peptide microarray data.

Protein kinases play an important role in cellular signalling. The reliable prediction of their substrates is of high importance for the deciphering of signalling pathways. A recently developed peptide microarray technology for the charcterisation of protein kinases delivers data on the individual phosphorylation status of each single member of a large peptide library. This data can be used to approximate the substrate specificity of the investigated kinase. We present an approach to process the collected information using a combination of a weight matrix approach and a nearest neighbor approach. Experiments with the protein-tyrosine kinase Abl are conducted to validate the results. Randomly selected peptides (1433) are used to estimate the substrate preferences of the kinase. The obtained prediction results are compared with standard methods. The new approach is tested further on bona fide Abl phosphorylation sites.

Binding Sites↗