CSO validator: improving manual curation workflow for biological pathways

Summary: Manual curation and validation of large-scale biological pathways are required to obtain high-quality pathway databases. In a typical curation process, model validation and model update based on appropriate feedback are repeated and requires considerable cooperation of scientists. We have developed a CSO (Cell System Ontology) validator to reduce the repetition and time during the curation process. This tool assists in quickly obtaining agreement among curators and domain experts and in providing a consistent and accurate pathway database

Discriminating dynamical from additive noise in the Van der Pol oscillator

We address the distinction between dynamical and additive noise in time series analysis by making a joint evaluation of both the statistical continuity of the series and the statistical differentiability of the reconstructed measure. Low levels of the latter and high levels of the former indicate the presence of dynamical noise only, while low values of the two are observed as soon as additive noise contaminates the signal. The method is presented through the example of the Van der Pol oscillator, but is expected to be of general validity for continuous-time systems.Comment: 12 pages (Elsevier LaTeX class), 4 EPS figures, submitted to Physica D (4 july 2001

Field-induced metal-insulator transition and switching phenomenon in correlated insulators

We study the nonequilibrium switching phenomenon associated with the metal-insulator transition under electric field E in correlated insulator by a gauge-covariant Keldysh formalism. Due to the feedback effect of the resistive current I, this occurs as a first-order transition with a hysteresis of I-V characteristics having a lower threshold electric field (\sim 10^4 Vcm^{-1}) much weaker than that for the Zener breakdown. It is also found that the localized mid-gap states introduced by impurities and defects act as hot spots across which the resonant tunneling occurs selectively, which leads to the conductive filamentary paths and reduces the energy cost of the switching function.Comment: 5 pages, 3 figures. A study on the metal-insulator transition in correlated insulators was adde

Sensitivity Analysis of Intracellular Signaling Pathway Kinetics Predicts Targets for Stem Cell Fate Control

Directing stem cell fate requires knowledge of how signaling networks integrate temporally and spatially segregated stimuli. We developed and validated a computational model of signal transducer and activator of transcription-3 (Stat3) pathway kinetics, a signaling network involved in embryonic stem cell (ESC) self-renewal. Our analysis identified novel pathway responses; for example, overexpression of the receptor glycoprotein-130 results in reduced pathway activation and increased ESC differentiation. We used a systematic in silico screen to identify novel targets and protein interactions involved in Stat3 activation. Our analysis demonstrates that signaling activation and desensitization (the inability to respond to ligand restimulation) is regulated by balancing the activation state of a distributed set of parameters including nuclear export of Stat3, nuclear phosphatase activity, inhibition by suppressor of cytokine signaling, and receptor trafficking. This knowledge was used to devise a temporally modulated ligand delivery strategy that maximizes signaling activation and leads to enhanced ESC self-renewal

Elucidating the Altered Transcriptional Programs in Breast Cancer using Independent Component Analysis

The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes. Independent Component Analysis (ICA) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/inhibition patterns of biological pathways. While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis (PCA), a validation using explicit pathway and regulatory element information has not yet been performed. We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway-knowledge and regulatory-element databases for validation. We show that ICA outperforms PCA and clustering-based methods in that ICA components map closer to known cancer-related pathways, regulatory modules, and cancer phenotypes. Furthermore, we identify cancer signalling and oncogenic pathways and regulatory modules that play a prominent role in breast cancer and relate the differential activation patterns of these to breast cancer phenotypes. Importantly, we find novel associations linking immune response and epithelial–mesenchymal transition pathways with estrogen receptor status and histological grade, respectively. In addition, we find associations linking the activity levels of biological pathways and transcription factors (NF1 and NFAT) with clinical outcome in breast cancer. ICA provides a framework for a more biologically relevant interpretation of genomewide transcriptomic data. Adopting ICA as the analysis tool of choice will help understand the phenotype–pathway relationship and thus help elucidate the molecular taxonomy of heterogeneous cancers and of other complex genetic diseases

Discounting in LTL

In recent years, there is growing need and interest in formalizing and reasoning about the quality of software and hardware systems. As opposed to traditional verification, where one handles the question of whether a system satisfies, or not, a given specification, reasoning about quality addresses the question of \emph{how well} the system satisfies the specification. One direction in this effort is to refine the "eventually" operators of temporal logic to {\em discounting operators}: the satisfaction value of a specification is a value in $[0,1]$, where the longer it takes to fulfill eventuality requirements, the smaller the satisfaction value is. In this paper we introduce an augmentation by discounting of Linear Temporal Logic (LTL), and study it, as well as its combination with propositional quality operators. We show that one can augment LTL with an arbitrary set of discounting functions, while preserving the decidability of the model-checking problem. Further augmenting the logic with unary propositional quality operators preserves decidability, whereas adding an average-operator makes some problems undecidable. We also discuss the complexity of the problem, as well as various extensions

An X-Ray Induced Structural Transition in La_0.875Sr_0.125MnO_3

We report a synchrotron x-ray scattering study of the magnetoresistive manganite La_0.875Sr_0.125MnO_3. At low temperatures, this material undergoes an x-ray induced structural transition at which charge ordering of Mn^3+ and Mn^4+ ions characteristic to the low-temperature state of this compound is destroyed. The transition is persistent but the charge-ordered state can be restored by heating above the charge-ordering transition temperature and subsequently cooling. The charge-ordering diffraction peaks, which are broadened at all temperatures, broaden more upon x-ray irradiation, indicating the finite correlation length of the charge-ordered state. Together with the recent reports on x-ray induced transitions in Pr_(1-x)Ca_xMnO_3, our results demonstrate that the photoinduced structural change is a common property of the charge-ordered perovskite manganites.Comment: 5 pages, 4 embedded EPS figures; significant changes in the data analysis mad

Ultrafast Photoinduced Formation of Metallic State in a Perovskite-type Manganite with Short Range Charge and Orbital Order

Femtosecond reflection spectroscopy was performed on a perovskite-type manganite, Gd0.55Sr0.45MnO3, with the short-range charge and orbital order (CO/OO). Immediately after the photoirradiation, a large increase of the reflectivity was detected in the mid-infrared region. The optical conductivity spectrum under photoirradiation obtained from the Kramers-Kronig analyses of the reflectivity changes demonstrates a formation of a metallic state. This suggests that ferromagnetic spin arrangements occur within the time resolution (ca. 200 fs) through the double exchange interaction, resulting in an ultrafast CO/OO to FM switching.Comment: 4 figure

Modeling gene expression regulatory networks with the sparse vector autoregressive model

<p>Abstract</p> <p>Background</p> <p>To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems.</p> <p>Results</p> <p>We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets.</p> <p>Conclusion</p> <p>The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any <it>a priori </it>information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.</p

Model Checking Branching Properties on Petri Nets with Transits (Full Version)

To model check concurrent systems, it is convenient to distinguish between the data flow and the control. Correctness is specified on the level of data flow whereas the system is configured on the level of control. Petri nets with transits and Flow-LTL are a corresponding formalism. In Flow-LTL, both the correctness of the data flow and assumptions on fairness and maximality for the control are expressed in linear time. So far, branching behavior cannot be specified for Petri nets with transits. In this paper, we introduce Flow-CTL* to express the intended branching behavior of the data flow while maintaining LTL for fairness and maximality assumptions on the control. We encode physical access control with policy updates as Petri nets with transits and give standard requirements in Flow-CTL*. For model checking, we reduce the model checking problem of Petri nets with transits against Flow-CTL* via automata constructions to the model checking problem of Petri nets against LTL. Thereby, physical access control with policy updates under fairness assumptions for an unbounded number of people can be verified.Comment: 23 pages, 5 figure