Series of Concentration-Induced Phase Transitions in Cholesterol/Phosphatidylcholine Mixtures

In lipid membranes, temperature-induced transition from gel-to-fluid phase increases the lateral diffusion of the lipid molecules by three orders of magnitude. In cell membranes, a similar phase change may trigger the communication between the membrane components. Here concentration-induced phase transition properties of our recently developed statistical mechanical model of cholesterol/phospholipid mixtures are investigated. A slight (<1%) decrease in the model parameter values, controlling the lateral interaction energies, reveals the existence of a series of first- or second-order phase transitions. By weakening the lateral interactions first, the proportion of the ordered (i.e., superlattice) phase (Areg) is slightly and continuously decreasing at every cholesterol mole fraction. Then sudden decreases in Areg appear at the 0.18–0.26 range of cholesterol mole fractions. We point out that the sudden changes in Areg represent first- or second-order concentration-induced phase transitions from fluid to superlattice and from superlattice to fluid phase. Sudden changes like these were detected in our previous experiments at 0.2, 0.222, and 0.25 sterol mole fractions in ergosterol/DMPC mixtures. By further decreasing the lateral interactions, the fluid phase will dominate throughout the 0.18–0.26 interval, whereas outside this interval sudden increases in Areg may appear. Lipid composition-induced phase transitions as specified here should have far more important biological implications than temperature- or pressure-induced phase transitions. This is the case because temperature and pressure in cell membranes are largely invariant under physiological conditions

Characterization of the Lateral Distribution of Fluorescent Lipid in Binary-Constituent Lipid Monolayers by Principal Component Analysis

Lipid lateral organization in binary-constituent monolayers consisting of fluorescent and nonfluorescent lipids has been investigated by acquiring multiple emission spectra during measurement of each force-area isotherm. The emission spectra reflect BODIPY-labeled lipid surface concentration and lateral mixing with different nonfluorescent lipid species. Using principal component analysis (PCA) each spectrum could be approximated as the linear combination of only two principal vectors. One point on a plane could be associated with each spectrum, where the coordinates of the point are the coefficients of the linear combination. Points belonging to the same lipid constituents and experimental conditions form a curve on the plane, where each point belongs to a different mole fraction. The location and shape of the curve reflects the lateral organization of the fluorescent lipid mixed with a specific nonfluorescent lipid. The method provides massive data compression that preserves and emphasizes key information pertaining to lipid distribution in different lipid monolayer phases. Collectively, the capacity of PCA for handling large spectral data sets, the nanoscale resolution afforded by the fluorescence signal, and the inherent versatility of monolayers for characterization of lipid lateral interactions enable significantly enhanced resolution of lipid lateral organizational changes induced by different lipid compositions

Misty Mountain clustering: application to fast unsupervised flow cytometry gating

<p>Abstract</p> <p>Background</p> <p>There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 10⁶points that are often generated by high throughput experiments.</p> <p>Results</p> <p>To circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 10⁶data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.</p> <p>Conclusions</p> <p>Misty Mountain is fast, unbiased for cluster shape, identifies stable clusters and is robust to noise. It provides a useful, general solution for multidimensional clustering problems. We demonstrate its suitability for automated gating of flow cytometry data.</p

Self-regulating genes. Exact steady state solution by using Poisson representation

Systems biology studies the structure and behavior of complex gene regulatory networks. One of its aims is to develop a quantitative understanding of the modular components that constitute such networks. The self-regulating gene is a type of auto regulatory genetic modules which appears in over 40% of known transcription factors in E. coli. In this work, using the technique of Poisson Representation, we are able to provide exact steady state solutions for this feedback model. By using the methods of synthetic biology (P.E.M. Purnick and Weiss, R., Nature Reviews, Molecular Cell Biology, 2009, 10: 410-422) one can build the system itself from modules like this.Comment: 10 pages, 2 figures, 1 table, 1 supplemental material (9 pages); additional reference to the work of Grima et a