Luo Qingming

Biological networks to the analysis of microarray data

Abstract Microarray technology, which permits rapid and large-scale screening for patterns of gene expressions, usually generates a large amount of data. How to mine the biological meanings under these data is one of the main challenges in bioinformatics. Compared to the pure mathematical techniques, those methods incorporated with some prior biological knowledge generally bring better interpretations. Recently, a new analysis, in which the knowledge of biological networks such as metabolic network and protein interaction network is introduced, is widely applied to microarray data analysis. The microarray data analysis based on biological networks contains two main research aspects: identification of active components in biological networks and assessment of gene sets significance. In this paper, we briefly review the progress of these two categories of analyses, especially some representative methods. * Supported by the National Program on Key Basic Research Projects (No. 2004CB518606), the Fundamental R...

A quantitative theory model of a photobleaching mechanism

A photobleaching model: D-P (dye-photon interaction) and D-O (Dye-oxygen oxidative reaction) photobleaching theory, is proposed. The quantitative power dependences of photobleaching rates with both one- and two-photon excitations (1?PE and TPE) are obtained. This photobleaching model can be used to elucidate our and other experimental results commendably. Experimental studies of the photobleaching rates for rhodamine B with TPE under unsaturation conditions reveals that the power dependences of photobleaching rates increase with the increasing dye concentration, and that the photobleaching rate of a single molecule increases in the second power of the excitation intensity, which is different from the high-order (>3) nonlinear dependence of ensemble molecules.

FluxExplorer： A general platform for modeling and analyses of metabolic networks based on stoichiometry

Stoichiometry-based analyses of metabolic networks have aroused significant interest of systems biology researchers in recent years. It is necessary to develop a more convenient modeling platform on which users can reconstruct their network models using completely graphical operations, and explore them with powerful analyzing modules to get a better understanding of the properties of metabolic systems. Herein, an in silico platform, FluxExplorer, for metabolic modeling and analyses based on stoichiometry has been developed as a publicly available tool for systems biology research. This platform integrates various analytic approaches, including flux balance analysis, minimization of metabolic adjustment, extreme pathways analysis, shadow prices analysis, and singular value decomposition, providing a thorough characterization of the metabolic system. Using a graphic modeling process, metabolic networks can be reconstructed and modified intuitively and conveniently. The inconsistencies of a model with respect to the FBA principles can be proved automatically. In addition, this platform supports systems biology markup language (SBML). FluxExplorer has been applied to rebuild a metabolic network in mammalian mitochondria, producing meaningful results. Generally, it is a powerful and very convenient tool for metabolic network modeling and analysis.

Parallel Visualization of Visible Chinese Human with Extremely Large Datasets

Single sectional anatomy image of Visible Chinese Human datasets achieved recently in First Military Medical University is almost 127 MByte. The total datasets are estimated to be 1.1 TByte if 48 bits color image is stored. Personal computer and graph workstation with multiple CPUs are also impossible to process extremely large datasets. Therefore the challenge is to visualize the extremely large datasets efficiently. Visible Chinese Human datasets are so massive in size they require the use of parallel computing resources for effective visualization. At present a parallel visualization program has been developed based on parallelism visualization toolkit (pVTK) on high performance cluster to solve the problem. The visualization results and performance demonstrate that the parallel program we developed provides a promising solution of handling extremely large datasets

Brainsmatics—bridging the brain science and brain-inspired artificial intelligence

We propose a new approach of brain-spatial information science, abbreviated to brainsmatics, which refers to the integrated, systematic approach of tracing, measuring, analyzing, managing and displaying cross-level brain spatial data with multi-scale resolution. We discussed its research contents, technological systems and key scientific problems, analyzed its discipline orientation, and forecasted the applications. Taking the micro-optical sectioning tomography (MOST) serial techniques as the core, we have developed a multidisciplinary complete technical system of visible brain-wide network (VBN), which makes brainsmatics more mature. Based on big data of three-dimensional fine structural and functional imaging of neuron types, neural circuits and networks, vascular network et al, with definite temporal-spatial resolution and specific spatial locations, brainsmatics makes it possible to better decipher the brain function and disease and promote the brain-inspired artificial intelligence by extracting cross-level and multi-scale temporal-spatial characteristics of brain connectivity.