PromoterPlot: a graphical display of promoter similarities by pattern recognition

PromoterPlot (http://promoterplot.fmi.ch) is a web-based tool for simplifying the display and processing of transcription factor searches using either the commercial or free TransFac distributions. The input sequence is a TransFac search (public version) or FASTA/Affymetrix IDs (local install). It uses an intuitive pattern recognition algorithm for finding similarities between groups of promoters by dividing transcription factor predictions into conserved triplet models. To minimize the number of false-positive models, it can optionally exclude factors that are known to be unexpressed or inactive in the cells being studied based on microarray or proteomic expression data. The program will also estimate the likelihood of finding a pattern by chance based on the frequency observed in a control set of mammalian promoters we obtained from Genomatix. The results are stored as an interactive SVG web page on our serve

PromoterPlot: a graphical display of promoter similarities by pattern recognition

PromoterPlot () is a web-based tool for simplifying the display and processing of transcription factor searches using either the commercial or free TransFac distributions. The input sequence is a TransFac search (public version) or FASTA/Affymetrix IDs (local install). It uses an intuitive pattern recognition algorithm for finding similarities between groups of promoters by dividing transcription factor predictions into conserved triplet models. To minimize the number of false-positive models, it can optionally exclude factors that are known to be unexpressed or inactive in the cells being studied based on microarray or proteomic expression data. The program will also estimate the likelihood of finding a pattern by chance based on the frequency observed in a control set of mammalian promoters we obtained from Genomatix. The results are stored as an interactive SVG web page on our server

Synchronous versus asynchronous modeling of gene regulatory networks

Motivation: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html. Contact: [email protected]

Dynamic simulation of regulatory networks using SQUAD

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available

Modeling stochasticity and robustness in gene regulatory networks

Motivation: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. Results: In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. Availability: Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/∼garg/genysis.html. Contact: [email protected]

Site effects estimation and their effects on strong ground motion at Amatrice village (Central Italy)

We present a summary of seismological and geophysical investigations at Amatrice (Central Italy), a village seated on an alluvial terrace and severely stroke by the Mw 6.0 event of August 24th 2016. The high vulnerability alone could not explain the heavy damage (X-XI MCS), whereas the vicinity of the seismic source and the peculiar site effects should be claimed to understand the ground motion variability. After the first mainshock, we investigated the Amatrice terrace for microzonation purposes together with several Italian institutions (Priolo et al., Bull. Earthquake Eng. 2019). In particular: (i) we installed 7 seismic stations as a part of the 3A network (DOI: 10.13127/SD/ku7Xm12Yy9; Cara et al., Sci. Data 2019); we performed (ii) an extensive campaign of 60 single-station ambient noise measurements (downtown stations recorded also few earthquakes), and (iii) several 2D passive seismic arrays aimed at obtaining Vs profiles down to a depth of few tens of meters (Milana et al., Bull. Earthquake Eng. 2019). Earthquake recordings were used to empirically evaluate ground motion amplification effects through spectral ratio approaches, and noise data were collected for defining the spatial distribution of the resonance frequencies. Data analysis reveals a diffuse amplification effect that reaches its maximum values in downtown area with a resonant frequency (f0) of about 2 Hz. Seismic amplification is also characterized by spatial variation and directional amplification, mainly in downtown to the west side of the alluvial terrace, and related to both stratigraphic and topographic effects. This effect tends to decrease and almost vanishes in the central part of the terrace, and it increases again moving towards its eastern edge with a clear shift of f0 towards higher frequencies. Empirical transfer functions were then used to recover the ground motion that could have hit the historical center of Amatrice during the August 24th mainshock, through the convolution with the only record in the vicinity (IT.AMT station experienced a PGA of 0.87 g). The reconstructed peak values are much greater than expected from ground motion models, showing that detailed studies on local site response can largely modify the seismic hazard assessment.PublishedSan Francisco, California (USA)4T. Sismicità dell'Italia5T. Sismologia, geofisica e geologia per l'ingegneria sismic

Protein quantitative trait locus study in obesity during weight-loss identifies a leptin regulator

Although many genetic variants are known for obesity, their function remains largely unknown. Here, in a weight-loss intervention cohort, the authors identify protein quantitative trait loci associated with BMI at baseline and after weight loss and find FAM46A to be a regulator of leptin in adipocytes

Psychopathological Impact in Patients with History of Rheumatic Fever with or without Sydenham's Chorea: A Multicenter Prospective Study

Sydenham's chorea (SC) is a post-streptococcal autoimmune disorder of the central nervous system, and it is a major criterium for the diagnosis of acute rheumatic fever (ARF). SC typically improves in 12-15 weeks, but patients can be affected for years by persistence and recurrencies of both neurological and neuropsychiatric symptoms. We enrolled 48 patients with a previous diagnosis of ARF, with or without SC, in a national multicenter prospective study, to evaluate the presence of neuropsychiatric symptoms several years after SC's onset. Our population was divided in a SC group (n = 21), consisting of patients who had SC, and a nSC group (n = 27), consisting of patients who had ARF without SC. Both groups were evaluated by the administration of 8 different neuropsychiatric tests. The Work and Social Adjustment Scale (WSAS) showed significantly (p = 0.021) higher alterations in the SC group than in the nSC group. Furthermore, 60.4% (n = 29) of the overall population experienced neuropsychiatric symptoms other than choreic movements at diagnosis and this finding was significantly more common (p = 0.00) in SC patients (95.2%) than in nSC patients (33.3%). The other neuropsychiatric tests also produced significant results, indicating that SC can exert a strong psychopathological impact on patients even years after its onset

Temporary dense seismic network during the 2016 Central Italy seismic emergency for microzonation studies

In August 2016, a magnitude 6.0 earthquake struck Central Italy, starting a devastating seismic sequence, aggravated by other two events of magnitude 5.9 and 6.5, respectively. After the first mainshock, four Italian institutions installed a dense temporary network of 50 seismic stations in an area of 260 km2. The network was registered in the International Federation of Digital Seismograph Networks with the code 3A and quoted with a Digital Object Identifier ( https://doi.org/10.13127/SD/ku7Xm12Yy9 ). Raw data were converted into the standard binary miniSEED format, and organized in a structured archive. Then, data quality and completeness were checked, and all the relevant information was used for creating the metadata volumes. Finally, the 99 Gb of continuous seismic data and metadata were uploaded into the INGV node of the European Integrated Data Archive repository. Their use was regulated by a Memorandum of Understanding between the institutions. After an embargo period, the data are now available for many different seismological studies.Publishedid 1825T. Sismologia, geofisica e geologia per l'ingegneria sismicaJCR Journa