A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data

Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach

Model-based clustering with data correction for removing artifacts in gene expression data

The NIH Library of Integrated Network-based Cellular Signatures (LINCS) contains gene expression data from over a million experiments, using Luminex Bead technology. Only 500 colors are used to measure the expression levels of the 1,000 landmark genes measured, and the data for the resulting pairs of genes are deconvolved. The raw data are sometimes inadequate for reliable deconvolution leading to artifacts in the final processed data. These include the expression levels of paired genes being flipped or given the same value, and clusters of values that are not at the true expression level. We propose a new method called model-based clustering with data correction (MCDC) that is able to identify and correct these three kinds of artifacts simultaneously. We show that MCDC improves the resulting gene expression data in terms of agreement with external baselines, as well as improving results from subsequent analysis.Comment: 28 page

Autonomous frequency domain identification: Theory and experiment

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability

Extreme enrichment in atmospheric 15N15N.

Molecular nitrogen (N2) comprises three-quarters of Earth's atmosphere and significant portions of other planetary atmospheres. We report a 19 per mil (‰) excess of 15N15N in air relative to a random distribution of nitrogen isotopes, an enrichment that is 10 times larger than what isotopic equilibration in the atmosphere allows. Biological experiments show that the main sources and sinks of N2 yield much smaller proportions of 15N15N in N2. Electrical discharge experiments, however, establish 15N15N excesses of up to +23‰. We argue that 15N15N accumulates in the atmosphere because of gas-phase chemistry in the thermosphere (>100 km altitude) on time scales comparable to those of biological cycling. The atmospheric 15N15N excess therefore reflects a planetary-scale balance of biogeochemical and atmospheric nitrogen chemistry, one that may also exist on other planets

Lattice Long Short-Term Memory for Human Action Recognition

Human actions captured in video sequences are three-dimensional signals characterizing visual appearance and motion dynamics. To learn action patterns, existing methods adopt Convolutional and/or Recurrent Neural Networks (CNNs and RNNs). CNN based methods are effective in learning spatial appearances, but are limited in modeling long-term motion dynamics. RNNs, especially Long Short-Term Memory (LSTM), are able to learn temporal motion dynamics. However, naively applying RNNs to video sequences in a convolutional manner implicitly assumes that motions in videos are stationary across different spatial locations. This assumption is valid for short-term motions but invalid when the duration of the motion is long. In this work, we propose Lattice-LSTM (L2STM), which extends LSTM by learning independent hidden state transitions of memory cells for individual spatial locations. This method effectively enhances the ability to model dynamics across time and addresses the non-stationary issue of long-term motion dynamics without significantly increasing the model complexity. Additionally, we introduce a novel multi-modal training procedure for training our network. Unlike traditional two-stream architectures which use RGB and optical flow information as input, our two-stream model leverages both modalities to jointly train both input gates and both forget gates in the network rather than treating the two streams as separate entities with no information about the other. We apply this end-to-end system to benchmark datasets (UCF-101 and HMDB-51) of human action recognition. Experiments show that on both datasets, our proposed method outperforms all existing ones that are based on LSTM and/or CNNs of similar model complexities.Comment: ICCV201

Experimental Line Parameters of the b^(1)Σ^(+)_g ← X^(3)Σ^(-)_g Band of Oxygen Isotopologues at 760 nm Using Frequency-Stabilized Cavity Ring-Down Spectroscopy

Positions, intensities, self-broadened widths, and collisional narrowing coefficients of the oxygen isotopologues ^(16)O^(18)O, ^(16)O^(17)O, ^(17)O^(18)O, and ^(18)O^(18)O have been measured for the b^(1)Σg + ← X^(3)Σg − (0,0) band using frequency-stabilized cavity ring-down spectroscopy. Line positions of 156 P-branch transitions were referenced against the hyperfine components of the ^(39)K D_1 (4s ^(2)S_(1/2) → 4p ^(2)P_(1/2)) and D_2 (4s ^(2)S_(1/2) → 4p ^(2)P_(3/2)) transitions, yielding precisions of ~0.00005 cm^(−1) and absolute accuracies of 0.00030 cm^(−1) or better. New excited b^(1)Σg + state molecular constants are reported for all four isotopologues. The measured line intensities of the ^(16)O^(18)O isotopologue are within 2% of the values currently assumed in molecular databases. However, the line intensities of the ^(16)O^(17)O isotopologue show a systematic, J-dependent offset between our results and the databases. Self-broadening half-widths for the various isotopologues are internally consistent to within 2%. This is the first comprehensive study of the line intensities and shapes for the ^(17)O^(18)O or ^(18)O_2 isotopologues of the b^(1)Σg + ← X^(3)Σg − (0,0) band of O_2. The ^(16)O_2, ^(16)O^(18)O, and ^(16)O^(17)O line parameters for the oxygen A-band have been extensively revised in the HITRAN 2008 database using results from the present study

Computational models for inferring biochemical networks

Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.The Romanian National Authority for Scientific Research, CNDI–UEFISCDI, Project No. PN-II-PT-PCCA-2011-3.2-0917