Q13593750
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Time-lapse microscopic movies are being increasingly utilized for understanding the derivation of cell states and predicting cell future. Often, fluorescence and other types of labeling are not available or desirable, and cell state-definitions based on observable structures must be used. We present the methodology for cell behavior recognition and prediction based on the short term cell recurrent behavior analysis. This approach has theoretical justification in non-linear dynamics theory. The methodology is based on the general stochastic systems theory which allows us to define the cell states, trajectory and the system itself. We introduce the usage of a novel image content descriptor based on information contribution (gain) by each image point for the cell state characterization as the first step. The linkage between the method and the general system theory is presented as a general frame for cell behavior interpretation. We also discuss extended cell description, system theory and methodology for future development. This methodology may be used for many practical purposes, ranging from advanced, medically relevant, precise cell culture diagnostics to very utilitarian cell recognition in a noisy or uneven image background. In addition, the results are theoretically justified.
- MeSH
- buňky MeSH
- mikroskopie metody MeSH
- stochastické procesy MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Expertomica Cells is a program for the creation and analysis of pedigree plots from time-lapse micrographs of cell monolayers. It enables recording the basic events in a cell cycle, cell neighbourhoods and spatial migration. The output is both numeric and graphical. The software helps to lower main hurdles in the manual analysis of cell monolayer development to practical limits; it reduces the operator processing time of typical experiment containing 5000 consecutive images from the usual 3 months to 3-10 h. Availability and Implementation: Freely available on the web at http://www.expertomicacells.tk or http://www.expertomicacells.wu.cz. The source code is implemented in JAVA 6 and supported by Linux, Mac and MS Windows. SUPPLEMENTARY INFORMATION: Supplementary data available at Bioinformatics online.
Mass spectrometers are sophisticated, fine instruments which are essential in a variety applications. However, the data they produce are usually interpreted in a rather primitive way, without considering the accuracy of this data and the potential errors in identifying peaks. Our new approach corrects this situation by dividing the LC-MS output into three components: (i) signature of the analyte, (ii) random noise and (iii) systemic noise. The systemic noise is related to the instrument and to the particular experiment; its characteristics change in time and depend on the analyzed substance. Working with these components allows us to quantify the probability of peak errors and, at the same time, to retrieve some peaks which get lost in the noise when using the existing methods. Our software tool, Expertomica metabolite profiling, automatically evaluates the given instrument, detects compounds and calculates the probability of individual peaks. It does not need any artificial user-defined parameters or thresholds. AVAILABILITY: MATLAB scripts with a simple graphical user interface are free to download from http://sourceforge.net/projects/expertomica-eda/. The software reads data exported by most Thermo and Agilent spectrometers, and it can also read the more general JCAMP-DX ASCII format. Other formats will be supported on request, assuming that the user can provide representative data samples.
The specificity of the proteinase of myeloblastosis-associated virus (MAV) was studied with (a) 21 substrate-based inhibitors, (b) 9 inhibitors with pseudopalindrome sequences, (c) 8 chimeric inhibitors, and (d) 3 compounds designed as human immunodeficiency virus 1 (HIV-1) proteinase inhibitors. The central inhibitory unit (transition state or cleaved bond analog) and the role of the inhibitor side chains from P4 to P4' were investigated. MAV proteinase prefers an aromatic side chain in P1 and a small aliphatic nonpolar chain in P2 and P2'. Residues in P5 and P4 positions are outside of the short catalytic cleft of the enzyme, but still influence binding considerably. The data obtained provide evidence that the MAV proteinase has generally lower specificity and poorer binding than the HIV proteinase.
- MeSH
- aspartátové endopeptidasy * antagonisté a inhibitory MeSH
- HIV-1 enzymologie MeSH
- HIV-proteasa metabolismus MeSH
- inhibitory proteas farmakologie chemická syntéza MeSH
- kinetika MeSH
- molekulární sekvence - údaje MeSH
- oligopeptidy farmakologie chemická syntéza MeSH
- sekvence aminokyselin MeSH
- substrátová specifita MeSH
- virus ptačí myeloblastózy * enzymologie MeSH
- vztahy mezi strukturou a aktivitou MeSH
- Publikační typ
- srovnávací studie MeSH
