Current Issue : April - June Volume : 2013 Issue Number : 2 Articles : 6 Articles
Gene alterations are a major component of the landscape of tumor genomes. To assess the significance of these alterations\r\nin the development of prostate cancer, it is necessary to identify these alterations and analyze them from systems biology\r\nperspective. Here, we present a new method (EigFusion) for predicting outlier genes with potential gene rearrangement. EigFusion\r\ndemonstrated excellent performance in identifying outlier genes with potential rearrangement by testing it to synthetic and real\r\ndata to evaluate performance. EigFusion was able to identify previously unrecognized genes such as FABP5 and KCNH8 and\r\nconfirmed their association with primary and metastatic prostate samples while confirmed the metastatic specificity for other\r\ngenes such as PAH, TOP2A, and SPINK1. We performed protein network based approaches to analyze the network context of\r\npotential rearranged genes. Functional gene rearrangement Modules are constructed by integrating functional protein networks.\r\nRearranged genes showed to be highly connected to well-known altered genes in cancer such as AR, RB1, MYC, and BRCA1.\r\nFinally, using clinical outcome data of prostate cancer patients, potential rearranged genes demonstrated significant association\r\nwith prostate cancer specific death....
Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that\r\neach function can only depend on a few nodes. Biologically motivated constraints further show that functions\r\nfound in Boolean regulatory networks belong to certain classes of functions, for example, the unate functions. It\r\nturns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem\r\nof detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks\r\nwith unbalanced functions and functions of an average sensitivity less than 23\r\nk, where k is the number of\r\ncontrolling variables for a function. Further, we consider the class of 1-low networks which include unate networks,\r\nlinear threshold networks, and networks with nested canalyzing functions. We show that the application of spectral\r\nlearning algorithms leads to both better time and sample complexity for the detection of controlling nodes\r\ncompared with algorithms based on exhaustive search. For a particular algorithm, we state analytical upper bounds\r\non the number of samples needed to find the controlling nodes of the Boolean functions. Further, improved\r\nalgorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied....
Treatment of bipolar disorder with lithium therapy during pregnancy is a medical challenge. Bipolar disorder is more prevalent in\r\nwomen and its onset is often concurrent with peak reproductive age. Treatment typically involves administration of the element\r\nlithium, which has been classified as a class D drug (legal to use during pregnancy, but may cause birth defects) and is one of only\r\nthirty known teratogenic drugs. There is no clear recommendation in the literature on the maximum acceptable dosage regimen for\r\npregnant, bipolar women.We recommend a maximum dosage regimen based on a physiologically based pharmacokinetic (PBPK)\r\nmodel. The model simulates the concentration of lithium in the organs and tissues of a pregnant woman and her fetus. First, we\r\nmodeled time-dependent lithium concentration profiles resulting from lithium therapy known to have caused birth defects. Next,\r\nwe identified maximum and average fetal lithium concentrations during treatment. Then, we developed a lithium therapy regimen\r\nto maximize the concentration of lithium in the mother�s brain, while maintaining the fetal concentration low enough to reduce\r\nthe risk of birth defects. This maximum dosage regimen suggested by the model was 400 mg lithium three times per day....
Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue\r\noccurrence is important for understanding biological processes. Therefore, we analyzed microarray and highthroughput\r\nRNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data\r\nwere used to investigate the presence of proteins, protein interactions and protein complexes in different tissues.\r\nOur comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied\r\nmeasurement technique. We found that microarrays are less sensitive for low expressed genes than highthroughput\r\nsequencing. Functional analyses based on microarray data are thus biased towards high expressed\r\ngenes. This also means that previous biological findings based on microarrays might have to be re-examined using\r\nhigh-throughput sequencing results...
Lattice models are a common abstraction used in the study of protein structure, folding, and refinement. They are advantageous\r\nbecause the discretisation of space can make extensive protein evaluations computationally feasible. Various approaches to\r\nthe protein chain lattice fitting problem have been suggested but only a single backbone-only tool is available currently. We\r\nintroduce LatFit, a new tool to produce high-accuracy lattice protein models. It generates both backbone-only and backboneside-\r\nchain models in any user defined lattice. LatFit implements a new distance RMSD-optimisation fitting procedure in\r\naddition to the known coordinate RMSD method. We tested LatFitâ��s accuracy and speed using a large nonredundant set of\r\nhigh resolution proteins (SCOP database) on three commonly used lattices: 3D cubic, face-centred cubic, and knightâ��s walk.\r\nFitting speed compared favourably to other methods and both backbone-only and backbone-side-chain models show low\r\ndeviation from the original data (~1.5 �°A RMSD in the FCC lattice). To our knowledge this represents the first comprehensive\r\nstudy of lattice quality for on-lattice protein models including side chains while LatFit is the only available tool for such\r\nmodels....
The weighted stochastic simulation algorithm (wSSA) recently developed by Kuwahara and Mura and the refined\r\nwSSA proposed by Gillespie et al. based on the importance sampling technique open the door for efficient\r\nestimation of the probability of rare events in biochemical reaction systems. In this paper, we first apply the\r\nimportance sampling technique to the next reaction method (NRM) of the stochastic simulation algorithm and\r\ndevelop a weighted NRM (wNRM). We then develop a systematic method for selecting the values of importance\r\nsampling parameters, which can be applied to both the wSSA and the wNRM. Numerical results demonstrate that\r\nour parameter selection method can substantially improve the performance of the wSSA and the wNRM in terms\r\nof simulation efficiency and accuracy....
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