Network-based functional enrichment

<p>Abstract</p> <p>Background</p> <p>Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account.</p> <p>Results</p> <p>Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i) determine which functions are enriched in a given network, ii) given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii) given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms.</p> <p>Conclusions</p> <p>We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are implemented in C++ and are freely available under the GNU General Public License at our supplementary website. Additionally, all our input data and results are available at <url>http://bioinformatics.cs.vt.edu/~murali/supplements/2011-incob-nbe/</url>.</p

Evaluation of flight efficiency for Stockholm Arlanda Airport arrivals

Analysis of punctuality of airport arrivals, as well as identification of causes of the delays within transition airspace, is an important step in evaluating performance of the Terminal Maneuvering Area (TMA) Air Navigation Services: without knowing the current performance levels, it is difficult to identify which areas could be improved. Deviations from the flight plans is one of the major reasons for arrival delays. In this work, we quantified the impact of the deviations from the flight plans on the fuel burn. One of the main reasons of fuel waste is non- optimal vertical profiles during the descent phase. We calculated how much extra fuel is wasted due to vertical flight inefficiency within Stockholm TMA.Peer ReviewedPostprint (published version

A Comparison of Mathematical Models for Polarization of Single Eukaryotic Cells in Response to Guided Cues

Polarization, a primary step in the response of an individual eukaryotic cell to a spatial stimulus, has attracted numerous theoretical treatments complementing experimental studies in a variety of cell types. While the phenomenon itself is universal, details differ across cell types, and across classes of models that have been proposed. Most models address how symmetry breaking leads to polarization, some in abstract settings, others based on specific biochemistry. Here, we compare polarization in response to a stimulus (e.g., a chemoattractant) in cells typically used in experiments (yeast, amoebae, leukocytes, keratocytes, fibroblasts, and neurons), and, in parallel, responses of several prototypical models to typical stimulation protocols. We find that the diversity of cell behaviors is reflected by a diversity of models, and that some, but not all models, can account for amplification of stimulus, maintenance of polarity, adaptation, sensitivity to new signals, and robustness

Establishment of a 3D In Vitro Model to Accelerate the Development of Human Therapies against Corneal Diabetes

The authors thank Dr. John M Asara, Min Yuan, and Susanne Breitkopf for their technical help with metabolomics experiments, Dr. Ben Fowler for his technical help with TEM experiments and also Tina B McKay for many thoughtful discussions and scientific insights during the study.Purpose To establish an in vitro model that would mirror the in vivo corneal stromal environment in diabetes (DM) patients. Methods Human corneal fibroblasts from Healthy (HCFs), Type 1DM (T1DM) and Type 2DM (T2DM) donors were isolated and cultured for 4 weeks with Vitamin C stimulation in order to allow for extracellular matrix (ECM) secretion and assembly. Results Our data indicated altered cellular morphology, increased cellular migration, increased ECM assembly, and severe mitochondrial damage in both T1DM and T2DMs when compared to HCFs. Furthermore, we found significant downregulation of Collagen I and Collagen V expression in both T1DM and T2DMs. Furthermore, a significant up regulation of fibrotic markers was seen, including α-smooth muscle actin in T2DM and Collagen III in both T1DM and T2DMs. Metabolic analysis suggested impaired Glycolysis and Tricarboxylic acid cycle (TCA) pathway. Conclusion DM has significant effects on physiological and clinical aspects of the human cornea. The benefits in developing and fully characterizing our 3D in vitro model are enormous and might provide clues for the development of novel therapeutics.Yeshttp://www.plosone.org/static/editorial#pee