Multi-scale genetic network inference based on time series gene expression profiles

This work integrates multi-scale clustering and short-time correlation to estimate genetic networks with different time resolutions and detail levels. Gene expression data are noisy and large scale. Clustering is widely used to group genes with similar pattern. The cluster centers can be used to infer the genetic networks among these clusters. This work introduces the Multi-scale Fuzzy K-means clustering algorithm to uncover groups of coregulated genes and capture the networks in different levels of detail.;Time series expression profiles provide dynamic information for inferring gene regulatory relationships. Large scale network inference, identifying the transient interactions and feedback loops as well as differentiating direct and indirect interactions are among the major challenges of genetic network inference. Time correlation can estimate the time delay and edge direction. Partial correlation and directed-separation theory help differentiate direct and indirect interactions and identify feedback loops. This work introduces the constraint-based time-correlation (CBTC) network inference algorithm that combines these methods with time correlation estimation to more fully characterize genetic networks. Gene expression regulation can happen in specific time periods and conditions instead of across the whole expression profile. Short-time correlation can capture transient interactions.;The network discovery algorithm was mainly validated using yeast cell cycle data. The algorithm successfully identified the yeast cell cycle development stages, cell cycle and negative feedback loops, and indicated how the networks dynamically changes over time. The inferred networks reflect most interactions previously identified by genome-wide location analysis and match the extant literature. At detailed network level, the inferred networks provide more detailed information about genes (or clusters) and the interactions among them. Interesting genes, clusters and interactions were identified, which match the literature and the gene ontology information and provide hypotheses for further studies

Numerical Drag Prediction of NASA Common Research Models Using Different Turbulence Models

The goal of this research is to perform 3D turbulence flow simulations to predict the drag of Wing-body-tail (WBT) and Wing-body-nacelle-Pylon (WBNP) aircraft configurations from NASA Common Research Models. These configurations are also part of the 4th and 6th AIAA Drag Prediction Workshops in which CFD modelers have participated worldwide. The computations are performed using CFD solver ANSYS FLUENT. The compressible Reynolds-Averaged Navier-Stokes (RANS) equations are solved using two turbulence models – the Spalart-Allmaras (SA) and SST k-ω. Drag polar and drag rise curves are obtained by performing computations at different angles of attack at a constant Mach number. Pressure distributions and flow separation analysis are presented at different angles of attack. Comparison of computational results for WBT and WBNP models is made with the experimental data using the two turbulence models; good agreements is obtained. For WBNP, an aero-elastically deformed model of the wing is also considered at an angle of attack of 2.75°; the computations again are in reasonable agreement with the experiment. The computed WBNP results are compared with WB results for the drag increment study

Robust H∞ feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise

The official published version can found at the link below.Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal rate disturbance. Time delays are usually inevitable due to different biochemical reactions in such GRNs. In this paper, a delayed stochastic model with additive and multiplicative noises is utilized to describe stochastic GRNs. A feedback gene controller design scheme is proposed to guarantee that the GRN is mean-square asymptotically stable with noise attenuation, where the structure of the controllers can be specified according to engineering requirements. By applying control theory and mathematical tools, the analytical solution to the control design problem is given, which helps to provide some insight into synthetic biology and systems biology. The control scheme is employed in a three-gene network to illustrate the applicability and usefulness of the design.This work was funded by Royal Society of the U.K.; Foundation for the Author of National Excellent Doctoral Dissertation of China. Grant Number: 2007E4; Heilongjiang Outstanding Youth Science Fund of China. Grant Number: JC200809; Fok Ying Tung Education Foundation. Grant Number: 111064; International Science and Technology Cooperation Project of China. Grant Number: 2009DFA32050; University of Science and Technology of China Graduate Innovative Foundation