This work presents a direct procedure to apply Padé method to find approximate solutions for nonlinear differential equations. Moreover, we present some cases study showing the strength of the method to generate highly accurate rational approximate solutions compared to other semi-analytical methods. The type of tested nonlinear equations are: a highly nonlinear boundary value problem, a differential-algebraic oscillator problem, and an asymptotic problem. The high accurate handy approximations obtained by the direct application of Padé method shows the high potential if the proposed scheme to approximate a wide variety of problems. What is more, the direct application of the Padé approximant aids to avoid the previous application of an approximative method like Taylor series method, homotopy perturbation method, Adomian Decomposition method, homotopy analysis method, variational iteration method, among others, as tools to obtain a power series solutions to post-treat with the Padé approximant.
AMS Subject Classification
Padé transform; Nonlinear differential equations
Public repositories for proteomics data have accelerated proteomics research by enabling more efficient cross-analyses of datasets, supporting the creation of protein and peptide compendia of experimental results, supporting the development and testing of new software tools, and facilitating the manuscript review process. The repositories available to date have been designed to accommodate either shotgun experiments or generic proteomic data files. Here, we describe a new kind of proteomic data repository for the collection and representation of data from selected reaction monitoring (SRM) measurements. The PeptideAtlas SRM Experiment Library (PASSEL) allows researchers to easily submit proteomic data sets generated by SRM. The raw data are automatically processed in a uniform manner and the results are stored in a database, where they may be downloaded or browsed via a web interface that includes a chromatogram viewer. PASSEL enables cross-analysis of SRM data, supports optimization of SRM data collection, and facilitates the review process of SRM data. Further, PASSEL will help in the assessment of proteotypic peptide performance in a wide array of samples containing the same peptide, as well as across multiple experimental protocols.
data repository; MRM; software; SRM; targeted proteomics
HIV-1 is taken up by immature monocyte derived dendritic cells (iMDDCs) into tetraspanin rich caves from which the virus can either be transferred to T lymphocytes or enter into endosomes resulting in degradation. HIV-1 binding and fusion with the DC membrane results in low level de novo infection that can also be transferred to T lymphocytes at a later stage. We have previously reported that HIV-1 can induce partial maturation of iMDDCs at both stages of trafficking. Here we show that CD45+ microvesicles (MV) which contaminate purified HIV-1 inocula due to similar size and density, affect DC maturation, de novo HIV-1 infection and transfer to T lymphocytes. Comparing iMDDCs infected with CD45-depleted HIV-1BaL or matched non-depleted preparations, the presence of CD45+ MVs was shown to enhance DC maturation and ICAM-1 (CD54) expression, which is involved in DC∶T lymphocyte interactions, while restricting HIV-1 infection of MDDCs. Furthermore, in the DC culture HIV-1 infected (p24+) MDDCs were more mature than bystander cells. Depletion of MVs from the HIV-1 inoculum markedly inhibited DC∶T lymphocyte clustering and the induction of alloproliferation as well as limiting HIV-1 transfer from DCs to T lymphocytes. The effects of MV depletion on these functions were reversed by the re-addition of purified MVs from activated but not non-activated SUPT1.CCR5-CL.30 or primary T cells. Analysis of the protein complement of these MVs and of these HIV-1 inocula before and after MV depletion showed that Heat Shock Proteins (HSPs) and nef were the likely DC maturation candidates. Recombinant HSP90α and β and nef all induced DC maturation and ICAM-1 expression, greater when combined. These results suggest that MVs contaminating HIV-1 released from infected T lymphocytes may be biologically important, especially in enhancing T cell activation, during uptake by DCs in vitro and in vivo, particularly as MVs have been detected in the circulation of HIV-1 infected subjects.
Dendritic cells (DCs) are vital for immune recognition of pathogens as they capture, internalise, degrade and present foreign peptides to T lymphocytes. It is thought that HIV-1 hijacks the DCs functions, such as migration and maturation, to increase contact with the major target cell CD4+ T lymphocytes leading to dissemination throughout the body. Currently there is still some controversy over the ability of HIV-1 to infect and mature DCs, which may be due to differences in the inoculum used. Here we examined the effect of contaminating microvesicles (MVs) identified in HIV-1 preparations on HIV-1 modulation of DC function. We show that when MVs are present with HIV-1, the inoculum induces greater DC maturation and adhesion probably via cellular HSP90α and β and viral nef within the MVs. The functional consequences are reduced de novo replication of HIV-1 but increased clustering with T lymphocytes, resulting in increased T lymphocyte alloproliferation and HIV-1 transfer. As MVs are produced in HIV-1 susceptible cells and would be present in vivo due to HIV-1 induced cell death and hence are physiologically relevant, these results also indicate that MVs present in HIV-1 inocula should be considered when assessing HIV∶DC interactions.
The vulva of Caenorhabditis elegans has been long used as an experimental model of cell differentiation and organogenesis. While it is known that the signaling cascades of Wnt, Ras/MAPK, and NOTCH interact to form a molecular network, there is no consensus regarding its precise topology and dynamical properties. We inferred the molecular network, and developed a multivalued synchronous discrete dynamic model to study its behavior. The model reproduces the patterns of activation reported for the following types of cell: vulval precursor, first fate, second fate, second fate with reversed polarity, third fate, and fusion fate. We simulated the fusion of cells, the determination of the first, second, and third fates, as well as the transition from the second to the first fate. We also used the model to simulate all possible single loss- and gain-of-function mutants, as well as some relevant double and triple mutants. Importantly, we associated most of these simulated mutants to multivulva, vulvaless, egg-laying defective, or defective polarity phenotypes. The model shows that it is necessary for RAL-1 to activate NOTCH signaling, since the repression of LIN-45 by RAL-1 would not suffice for a proper second fate determination in an environment lacking DSL ligands. We also found that the model requires the complex formed by LAG-1, LIN-12, and SEL-8 to inhibit the transcription of eff-1 in second fate cells. Our model is the largest reconstruction to date of the molecular network controlling the specification of vulval precursor cells and cell fusion control in C. elegans. According to our model, the process of fate determination in the vulval precursor cells is reversible, at least until either the cells fuse with the ventral hypoderm or divide, and therefore the cell fates must be maintained by the presence of extracellular signals.
Caenorhabditis VPCs; vulval precursor cells; regulatory networks; discrete state network model; Caenorhabditis model
Targeted proteomics via selected reaction monitoring is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via selected reaction monitoring.
The objective of the current study was to formulate ketorolac tromethamine-loaded elastic liposomes and evaluate their in vitro drug release and their ex vivo and in vivo transdermal delivery. Ketorolac tromethamine (KT), which is a potent analgesic, was formulated in elastic liposomes using Tween 80 as an edge activator. The elastic vesicles were prepared by film hydration after optimizing the sonication time and number of extrusions. The vesicles exhibited an entrapment efficiency of 73 ± 11%, vesicle size of 127.8 ± 3.4 nm and a zeta potential of −12 mV. In vitro drug release was analyzed from liposomes and an aqueous solution, using Franz diffusion cells and a cellophane dialysis membrane with molecular weight cut-off of 8000 Da. Ex vivo permeation of KT across pig ear skin was studied using a Franz diffusion cell, with phosphate buffer (pH 7.4) at 32 °C as receptor solution. An in vivo drug permeation study was conducted on healthy human volunteers using a tape-stripping technique. The in vitro results showed (i) a delayed release when KT was included in elastic liposomes, compared to an aqueous solution of the drug; (ii) a flux of 0.278 μg/cm2h and a lag time of about 10 h for ex vivo permeation studies, which may indicate that KT remains in the skin (with the possibility of exerting a local effect) before reaching the receptor medium; (iii) a good correlation between the total amount permeated, the penetration distance (both determined by tape stripping) and transepidermal water loss (TEWL) measured during the in vivo permeation studies. Elastic liposomes have the potential to transport the drug through the skin, keep their size and drug charge, and release the drug into deep skin layers. Therefore, elastic liposomes hold promise for the effective topical delivery of KT.
elastic liposomes; ketorolac tromethamine; skin permeation; tape stripping; TEWL
Neurocognitive functioning may be compromised in children with type 1 diabetes mellitus (T1DM). The factor most consistently implicated in the long-term neurocognitive functioning of children with T1DM is age of onset. The pediatric literature suggests that glycemic extremes may have an effect on the neurocognitive functioning of children, but findings are mixed. The purpose of this study was to compare the neurocognitive functioning of young children with T1DM diagnosed before six years of age and healthy children (i.e., without chronic illness). Additionally, in the children with T1DM, we examined the relationship between their neurocognitive functioning and glycemic control. Sixty eight (36 with T1DM and 32 without chronic illness) preschool-age children (M age = 4.4yrs) were recruited and administered a battery of instruments to measure cognitive, language, and fine motor skills. Children with T1DM performed similarly to the healthy controls and both groups' skills fell in the average range. Among children with diabetes, poor glycemic control (higher HbA1c) was related to lower general cognitive abilities (r = -.44, p < .04), slower fine motor speed (r = -.64, p < .02), and lower receptive language scores (r = -.39, p < .04). Such findings indicate that young children with T1DM already demonstrate some negative neurocognitive effects in association with chronic hyperglycemia.
children; type 1 diabetes; neurocognitive
Electron transfer dissociation (ETD) is an alternative fragmentation technique to collision induced dissociation (CID) that has recently become commercially available. ETD has several advantages over CID. It is less prone to fragmenting amino acid side chains, especially those that are modified, thus yielding fragment ion spectra with more uniform peak intensities. Further, precursor ions of longer peptides and higher charge states can be fragmented and identified. However, analysis of ETD spectra has a few important differences that require the optimization of the software packages used for the analysis of CID data, or the development of specialized tools. We have adapted the Trans-Proteomic Pipeline (TPP) to process ETD data. Specifically, we have added support for fragment ion spectra from high charge precursors, compatibility with charge-state estimation algorithms, provisions for the use of the Lys-C protease, capabilities for ETD spectrum library building, and updates to the data formats to differentiate CID and ETD spectra. We show the results of processing datasets from several different types of ETD instruments and demonstrate that application of the ETD-enhanced TPP can increase the number of spectrum identifications at a fixed false discovery rate by as much as 100% over native output from a single sequence search engine.
shotgun proteomics; electron-transfer dissociation; bioinformatics
The Trans-Proteomic Pipeline (TPP) is a suite of software tools for the analysis of tandem mass spectrometry datasets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein-level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample dataset, demonstrating that the set up and use of the tools is straightforward and well supported and does not require specialized informatics resources or knowledge.
Multiple reaction monitoring mass spectrometry (MRM-MS) is a targeted analysis method that has been increasingly viewed as an avenue to explore proteomes with unprecedented sensitivity and throughput. We have developed a software tool, called MaRiMba, to automate the creation of explicitly defined MRM transition lists required to program triple quadrupole mass spectrometers in such analyses. MaRiMba creates MRM transition lists from downloaded or custom-built spectral libraries, restricts output to specified proteins or peptides, and filters based on precursor peptide and product ion properties. MaRiMba can also create MRM lists containing corresponding transitions for isotopically heavy peptides, for which the precursor and product ions are adjusted according to user specifications. This open-source application is operated through a graphical user interface incorporated into the Trans-Proteomic Pipeline, and it outputs the final MRM list to a text file for upload to MS instruments. To illustrate the use of MaRiMba, we used the tool to design and execute an MRM-MS experiment in which we targeted the proteins of a well-defined and previously published standard mixture.
multiple reaction monitoring (MRM); selective reaction monitoring (SRM); MRM transition; transition list; spectral library; mass spectrometry; targeted proteomics
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.
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.
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.
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 of molecular networks is necessary to understand their dynamical properties. While a wealth of information on molecular connectivity is available, there are still relatively few data regarding the precise stoichiometry and kinetics of the biochemical reactions underlying most molecular networks. This imbalance has limited the development of dynamical models of biological networks to a small number of well-characterized systems. To overcome this problem, we wanted to develop a methodology that would systematically create dynamical models of regulatory networks where the flow of information is known but the biochemical reactions are not. There are already diverse methodologies for modeling regulatory networks, but we aimed to create a method that could be completely standardized, i.e. independent of the network under study, so as to use it systematically.
We developed a set of equations that can be used to translate the graph of any regulatory network into a continuous dynamical system. Furthermore, it is also possible to locate its stable steady states. The method is based on the construction of two dynamical systems for a given network, one discrete and one continuous. The stable steady states of the discrete system can be found analytically, so they are used to locate the stable steady states of the continuous system numerically. To provide an example of the applicability of the method, we used it to model the regulatory network controlling T helper cell differentiation.
The proposed equations have a form that permit any regulatory network to be translated into a continuous dynamical system, and also find its steady stable states. We showed that by applying the method to the T helper regulatory network it is possible to find its known states of activation, which correspond the molecular profiles observed in the precursor and effector cell types.
For genes that have been successfully delineated within the human genome sequence, most regulatory sequences remain to be elucidated. The annotation and interpretation process requires additional data resources and significant improvements in computational methods for the detection of regulatory regions. One approach of growing popularity is based on the preferential conservation of functional sequences over the course of evolution by selective pressure, termed 'phylogenetic footprinting'. Mutations are more likely to be disruptive if they appear in functional sites, resulting in a measurable difference in evolution rates between functional and non-functional genomic segments.
We have devised a flexible suite of methods for the identification and visualization of conserved transcription-factor-binding sites. The system reports those putative transcription-factor-binding sites that are both situated in conserved regions and located as pairs of sites in equivalent positions in alignments between two orthologous sequences. An underlying collection of metazoan transcription-factor-binding profiles was assembled to facilitate the study. This approach results in a significant improvement in the detection of transcription-factor-binding sites because of an increased signal-to-noise ratio, as demonstrated with two sets of promoter sequences. The method is implemented as a graphical web application, ConSite, which is at the disposal of the scientific community at .
Phylogenetic footprinting dramatically improves the predictive selectivity of bioinformatic approaches to the analysis of promoter sequences. ConSite delivers unparalleled performance using a novel database of high-quality binding models for metazoan transcription factors. With a dynamic interface, this bioinformatics tool provides broad access to promoter analysis with phylogenetic footprinting.
We report the isolation in the United States of Vibrio fluvialis from the stools of a patient who had severe watery diarrhea without fever and who subsequently died. V. fluvialis, a known enteric pathogen in other parts of the world, should be suspected in patients with watery diarrhea, especially in coastal areas.