639 research outputs found
Predicting Cell Cycle Regulated Genes by Causal Interactions
The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first
Integrative Network Biology: Graph Prototyping for Co-Expression Cancer Networks
Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure
Hierarchical coordination of periodic genes in the cell cycle of Saccharomyces cerevisiae
<p>Abstract</p> <p>Background</p> <p>Gene networks are a representation of molecular interactions among genes or products thereof and, hence, are forming causal networks. Despite intense studies during the last years most investigations focus so far on inferential methods to reconstruct gene networks from experimental data or on their structural properties, e.g., degree distributions. Their structural analysis to gain functional insights into organizational principles of, e.g., pathways remains so far under appreciated.</p> <p>Results</p> <p>In the present paper we analyze cell cycle regulated genes in <it>S. cerevisiae</it>. Our analysis is based on the transcriptional regulatory network, representing causal interactions and not just associations or correlations between genes, and a list of known periodic genes. No further data are used. Partitioning the transcriptional regulatory network according to a graph theoretical property leads to a hierarchy in the network and, hence, in the information flow allowing to identify two groups of periodic genes. This reveals a novel conceptual interpretation of the working mechanism of the cell cycle and the genes regulated by this pathway.</p> <p>Conclusion</p> <p>Aside from the obtained results for the cell cycle of yeast our approach could be exemplary for the analysis of general pathways by exploiting the rich causal structure of inferred and/or curated gene networks including protein or signaling networks.</p
Photodetection of propagating quantum microwaves in circuit QED
We develop the theory of a metamaterial composed of an array of discrete
quantum absorbers inside a one-dimensional waveguide that implements a
high-efficiency microwave photon detector. A basic design consists of a few
metastable superconducting nanocircuits spread inside and coupled to a
one-dimensional waveguide in a circuit QED setup. The arrival of a {\it
propagating} quantum microwave field induces an irreversible change in the
population of the internal levels of the absorbers, due to a selective
absorption of photon excitations. This design is studied using a formal but
simple quantum field theory, which allows us to evaluate the single-photon
absorption efficiency for one and many absorber setups. As an example, we
consider a particular design that combines a coplanar coaxial waveguide with
superconducting phase qubits, a natural but not exclusive playground for
experimental implementations. This work and a possible experimental realization
may stimulate the possible arrival of "all-optical" quantum information
processing with propagating quantum microwaves, where a microwave photodetector
could play a key role.Comment: 27 pages, submitted to Physica Scripta for Nobel Symposium on "Qubits
for Quantum Information", 200
Influence of the Time Scale on the Construction of Financial Networks
BACKGROUND: In this paper we investigate the definition and formation of financial networks. Specifically, we study the influence of the time scale on their construction. METHODOLOGY/PRINCIPAL FINDINGS: For our analysis we use correlation-based networks obtained from the daily closing prices of stock market data. More precisely, we use the stocks that currently comprise the Dow Jones Industrial Average (DJIA) and estimate financial networks where nodes correspond to stocks and edges correspond to none vanishing correlation coefficients. That means only if a correlation coefficient is statistically significant different from zero, we include an edge in the network. This construction procedure results in unweighted, undirected networks. By separating the time series of stock prices in non-overlapping intervals, we obtain one network per interval. The length of these intervals corresponds to the time scale of the data, whose influence on the construction of the networks will be studied in this paper. CONCLUSIONS/SIGNIFICANCE: Numerical analysis of four different measures in dependence on the time scale for the construction of networks allows us to gain insights about the intrinsic time scale of the stock market with respect to a meaningful graph-theoretical analysis
Joint Elastic Side-Scattering Lidar and Raman Lidar Measurements of Aerosol Optical Properties in South East Colorado
We describe an experiment, located in south-east Colorado, USA, that measured
aerosol optical depth profiles using two Lidar techniques. Two independent
detectors measured scattered light from a vertical UV laser beam. One detector,
located at the laser site, measured light via the inelastic Raman
backscattering process. This is a common method used in atmospheric science for
measuring aerosol optical depth profiles. The other detector, located
approximately 40km distant, viewed the laser beam from the side. This detector
featured a 3.5m2 mirror and measured elastically scattered light in a bistatic
Lidar configuration following the method used at the Pierre Auger cosmic ray
observatory. The goal of this experiment was to assess and improve methods to
measure atmospheric clarity, specifically aerosol optical depth profiles, for
cosmic ray UV fluorescence detectors that use the atmosphere as a giant
calorimeter. The experiment collected data from September 2010 to July 2011
under varying conditions of aerosol loading. We describe the instruments and
techniques and compare the aerosol optical depth profiles measured by the Raman
and bistatic Lidar detectors.Comment: 34 pages, 16 figure
Superradiance and Phase Multistability in Circuit Quantum Electrodynamics
By modeling the coupling of multiple superconducting qubits to a single
cavity in the circuit-quantum electrodynamics (QED) framework we find that it
should be possible to observe superradiance and phase multistability using
currently available technology. Due to the exceptionally large couplings
present in circuit-QED we predict that superradiant microwave pulses should be
observable with only a very small number of qubits (just three or four), in the
presence of energy relaxation and non-uniform qubit-field coupling strengths.
This paves the way for circuit-QED implementations of superradiant state
readout and decoherence free subspace state encoding in subradiant states. The
system considered here also exhibits phase multistability when driven with
large field amplitudes, and this effect may have applications for collective
qubit readout and for quantum feedback protocols.Comment: Published Versio
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