Página 1 dos resultados de 1784 itens digitais encontrados em 0.026 segundos

Prediction of the human oral bioavailability by using in vitro and in silico drug related parameters in a physiologically based absorption model

Paixão, P; Gouveia, LF; Morais, JA
Fonte: International journal of pharmaceutics Publicador: International journal of pharmaceutics
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
682.52414%
Estimates of the human oral absolute bioavailability were made by using a physiological-based pharmacokinetic model of absorption and the drug solubility at the gastrointestinal pH range 1.5-7.5, the apparent permeability (P(app)) in Caco-2 cells and the intrinsic clearance (Cl(int)) in human hepatocytes suspensions as major drug related parameters. The predictive ability of this approach was tested in 164 drugs divided in four levels of input data: (i) in vitro data for both P(app) and Cl(int); (ii) in vitro data for Cl(int) only; (iii) in vitro data for P(app) only and (iv) in silico data for both P(app) and Cl(int). In all scenarios, solubility was estimated in silico. Excellent predictive abilities were observed when in vitro data for both P(app) and Cl(int) were used, with 84% of drugs with oral bioavailability predictions within a±20% interval of the correct value. This predictive ability is reduced with the introduction of the in silico estimated parameters, particularly when Cl(int) is used. Performance of the model using only in silico data provided 53% of drugs with bioavailability predictions within a±20% acceptance interval. However, 74% of drugs in the same scenario resulted in bioavailability predictions within a±35% interval...

Correlação in vitro - in vivo de comprimidos matriciais de furosemida complexada à hidroxipropil-β-ciclodextrina: métodos in vitro, in vivo e in silico; In vitro - in vivo correlation of matrix tablets of furosemide complexed with hidroxypropyl-β-cyclodextrin: in vitro, in vivo and in silico methods

Silva, Marina de Freitas
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 14/02/2014 PT
Relevância na Pesquisa
581.1588%
A correlação in vitro - in vivo (CIVIV) refere-se ao estabelecimento de uma relação racional entre uma propriedade in vitro de uma forma farmacêutica (FF) e uma característica biológica, ou parâmetros derivados destas, produzidas a partir da absorção do fármaco, liberado por uma FF. Para o desenvolvimento de uma CIVIV, são necessárias três ou mais formulações, as quais são avaliadas em relação ao comportamento de dissolução e à biodisponibilidade (BD), e por meio do cálculo de deconvolução, estimam-se as frações absorvidas. A furosemida, fármaco modelo, é um diurético usado no tratamento de hipertensão. Este fármaco é classificado como classe IV do sistema de classificação biofarmacêutico (SCB) (Amidon et al., 1995). O objetivo do presente trabalho foi estabelecer uma CIVIV para formas farmacêuticas (FFs) de liberação modificada contendo complexo de furosemida e hidroxipropil-β-ciclodextrina (HP-β-CD), a partir de ensaios de dissolução e estudos de BD. O complexo de furosemida e HP-β-CD foi obtido por liofilização e caracterizado por análise térmica, solubilidade e permeabilidade. A partir do complexo, foram produzidas cinco formulações de comprimidos de liberação modificada...

The Development of a Universal In Silico Predictor of Protein-Protein Interactions

Valente, Guilherme T.; Acencio, Marcio L.; Martins, Cesar; Lemke, Ney
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
671.5863%
Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al.

Description and Interpretation of Adaptive Evolution of Escherichia coli K-12 MG1655 by Using a Genome-Scale In Silico Metabolic Model

Fong, Stephen S.; Marciniak, Jennifer Y.; Palsson, Bernhard Ø.
Fonte: American Society for Microbiology Publicador: American Society for Microbiology
Tipo: Artigo de Revista Científica
Publicado em /11/2003 EN
Relevância na Pesquisa
578.6289%
Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of α-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37°C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third...

In Silico and in Vitro Modeling of Hepatocyte Drug Transport Processes: Importance of ABCC2 Expression Levels in the Disposition of CarboxydichloroflurosceinS⃞

Howe, Katharine; Gibson, G. Gordon; Coleman, Tanya; Plant, Nick
Fonte: American Society for Pharmacology and Experimental Therapeutics Publicador: American Society for Pharmacology and Experimental Therapeutics
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
578.9102%
The impact of transport proteins in the disposition of chemicals is becoming increasingly evident. Alteration in disposition can cause altered pharmacokinetic and pharmacodynamic parameters, potentially leading to reduced efficacy or overt toxicity. We have developed a quantitative in silico model, based upon literature and experimentally derived data, to model the disposition of carboxydichlorofluroscein (CDF), a substrate for the SLCO1A/B and ABCC subfamilies of transporters. Kinetic parameters generated by the in silico model closely match both literature and experimentally derived kinetic values, allowing this model to be used for the examination of transporter action in primary rat hepatocytes. In particular, we show that the in silico model is suited to the rapid, accurate determination of Ki values, using 3-[[3-[2-(7-chloroquinolin-2-yl)vinyl]phenyl]-(2-dimethylcarbamoylethylsulfanyl)methylsulfanyl] propionic acid (MK571) as a prototypical pan-ABCC inhibitor. In vitro-derived data are often used to predict in vivo response, and we have examined how differences in protein expression levels between these systems may affect chemical disposition. We show that ABCC2 and ABCC3 are overexpressed in sandwich culture hepatocytes by 3.5- and 2.3-fold...

Development, Evaluation, and Application of an In Silico Model for Antimalarial Drug Treatment and Failure▿†

Winter, Katherine; Hastings, Ian M.
Fonte: American Society for Microbiology Publicador: American Society for Microbiology
Tipo: Artigo de Revista Científica
Publicado em /07/2011 EN
Relevância na Pesquisa
663.76664%
Pharmacological mechanism-based modeling was refined and used to develop an in silico model of antimalarial drug treatment validated against clinical and field data. We used this approach to investigate key features of antimalarial drug action and effectiveness, with emphasis on the current generation of artemisinin combination therapies. We made the following conclusions. (i) The development of artemisinin tolerance and resistance will, unless checked, have an immediate, large impact on the protection afforded to its partner drug and on the likely clinical efficacy of artemisinin combination therapies. (ii) Long follow-up periods are required in clinical trials to detect all drug failures; the follow-up periods of 28 days recommended by the World Health Organization are likely to miss at least 50% of drug failures, and we confirmed recent suggestions that 63 days would be a more appropriate follow-up period. (iii) Day 7 serum drug concentrations are a significant risk factor of failure, although, paradoxically, receiver operating characteristic curve analysis revealed that their predictive power is relatively poor. (iv) The pharmacokinetic properties of the partner drugs in artemisinin-containing combination therapies are the most important determinants of treatment outcome...

An in silico model for identification of small RNAs in whole bacterial genomes: characterization of antisense RNAs in pathogenic Escherichia coli and Streptococcus agalactiae strains

Pichon, Christophe; du Merle, Laurence; Caliot, Marie Elise; Trieu-Cuot, Patrick; Le Bouguénec, Chantal
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
572.55375%
Characterization of small non-coding ribonucleic acids (sRNA) among the large volume of data generated by high-throughput RNA-seq or tiling microarray analyses remains a challenge. Thus, there is still a need for accurate in silico prediction methods to identify sRNAs within a given bacterial species. After years of effort, dedicated software were developed based on comparative genomic analyses or mathematical/statistical models. Although these genomic analyses enabled sRNAs in intergenic regions to be efficiently identified, they all failed to predict antisense sRNA genes (asRNA), i.e. RNA genes located on the DNA strand complementary to that which encodes the protein. The statistical models enabled any genomic region to be analyzed theorically but not efficiently. We present a new model for in silico identification of sRNA and asRNA candidates within an entire bacterial genome. This model was successfully used to analyze the Gram-negative Escherichia coli and Gram-positive Streptococcus agalactiae. In both bacteria, numerous asRNAs are transcribed from the complementary strand of genes located in pathogenicity islands, strongly suggesting that these asRNAs are regulators of the virulence expression. In particular, we characterized an asRNA that acted as an enhancer-like regulator of the type 1 fimbriae production involved in the virulence of extra-intestinal pathogenic E. coli.

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Fang, Yilin; Wilkins, Michael J.; Yabusaki, Steven B.; Lipton, Mary S.; Long, Philip E.
Fonte: American Society for Microbiology Publicador: American Society for Microbiology
Tipo: Artigo de Revista Científica
Publicado em /12/2012 EN
Relevância na Pesquisa
585.92582%
Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data...

White Matter Damage Impairs Adaptive Recovery More than Cortical Damage in an in silico Model of Activity-Dependent Plasticity

Follett, Pamela L.; Roth, Cassandra; Follett, David; Dammann, Olaf
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /09/2009 EN
Relevância na Pesquisa
666.0336%
Little is understood of how damaged white matter interacts with developmental plasticity. We propose that computational neuroscience methods are underutilized in this problem. In this paper we present a non-deterministic, in silico model of activity-dependent plasticity. Using this model we compared the impact of neuronal cell loss or axonal dysfunction on the ability of the system to generate, maintain, and recover synapses. The results suggest the axonal dysfunction seen in white matter injury is a greater burden to adaptive plasticity and recovery than is the neuronal loss of cortical injury. Better understanding of the interaction between features of preterm brain injury and developmental plasticity is an essential component for improving recovery.

An In-Silico Model of Lipoprotein Metabolism and Kinetics for the Evaluation of Targets and Biomarkers in the Reverse Cholesterol Transport Pathway

Lu, James; Hübner, Katrin; Nanjee, M. Nazeem; Brinton, Eliot A.; Mazer, Norman A.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 13/03/2014 EN
Relevância na Pesquisa
670.3864%
High-density lipoprotein (HDL) is believed to play an important role in lowering cardiovascular disease (CVD) risk by mediating the process of reverse cholesterol transport (RCT). Via RCT, excess cholesterol from peripheral tissues is carried back to the liver and hence should lead to the reduction of atherosclerotic plaques. The recent failures of HDL-cholesterol (HDL-C) raising therapies have initiated a re-examination of the link between CVD risk and the rate of RCT, and have brought into question whether all target modulations that raise HDL-C would be atheroprotective. To help address these issues, a novel in-silico model has been built to incorporate modern concepts of HDL biology, including: the geometric structure of HDL linking the core radius with the number of ApoA-I molecules on it, and the regeneration of lipid-poor ApoA-I from spherical HDL due to remodeling processes. The ODE model has been calibrated using data from the literature and validated by simulating additional experiments not used in the calibration. Using a virtual population, we show that the model provides possible explanations for a number of well-known relationships in cholesterol metabolism, including the epidemiological relationship between HDL-C and CVD risk and the correlations between some HDL-related lipoprotein markers. In particular...

A Case Study of In Silico Modelling of Ciprofloxacin Hydrochloride/Metallic Compound Interactions

Stojkovic, Aleksandra; Parojcic, Jelena; Djuric, Zorica; Corrigan, Owen I.
Fonte: Springer US Publicador: Springer US
Tipo: Artigo de Revista Científica
Publicado em 05/12/2013 EN
Relevância na Pesquisa
577.94188%
With the development of physiologically based absorption models, there is an increased scientific and regulatory interest in in silico modelling and simulation of drug–drug and drug–food interactions. Clinically significant interactions between ciprofloxacin and metallic compounds are widely documented. In the current study, a previously developed ciprofloxacin-specific in silico absorption model was employed in order to simulate ciprofloxacin/metallic compound interaction observed in vivo. Commercially available software GastroPlus™ (Simulations Plus Inc., USA) based on the ACAT model was used for gastrointestinal (GI) simulations. The required input parameters, relating to ciprofloxacin hydrochloride physicochemical and pharmacokinetic characteristics, were experimentally determined, taken from the literature or estimated by GastroPlus™. Parameter sensitivity analysis (PSA) was used to assess the importance of selected input parameters (solubility, permeability, stomach and small intestine transit time) in predicting percent drug absorbed. PSA identified solubility and permeability as critical parameters affecting the rate and extent of ciprofloxacin absorption. Using the selected input parameters, it was possible to generate a ciprofloxacin absorption model...

A Multiscale Agent-Based in silico Model of Liver Fibrosis Progression

Dutta-Moscato, Joyeeta; Solovyev, Alexey; Mi, Qi; Nishikawa, Taichiro; Soto-Gutierrez, Alejandro; Fox, Ira J.; Vodovotz, Yoram
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 30/05/2014 EN
Relevância na Pesquisa
567.79863%
Chronic hepatic inflammation involves a complex interplay of inflammatory and mechanical influences, ultimately manifesting in a characteristic histopathology of liver fibrosis. We created an agent-based model (ABM) of liver tissue in order to computationally examine the consequence of liver inflammation. Our liver fibrosis ABM (LFABM) is comprised of literature-derived rules describing molecular and histopathological aspects of inflammation and fibrosis in a section of chemically injured liver. Hepatocytes are modeled as agents within hexagonal lobules. Injury triggers an inflammatory reaction, which leads to activation of local Kupffer cells and recruitment of monocytes from circulation. Portal fibroblasts and hepatic stellate cells are activated locally by the products of inflammation. The various agents in the simulation are regulated by above-threshold concentrations of pro- and anti-inflammatory cytokines and damage-associated molecular pattern molecules. The simulation progresses from chronic inflammation to collagen deposition, exhibiting periportal fibrosis followed by bridging fibrosis, and culminating in disruption of the regular lobular structure. The ABM exhibited key histopathological features observed in liver sections from rats treated with carbon tetrachloride (CCl4). An in silico “tension test” for the hepatic lobules predicted an overall increase in tissue stiffness...

Structural Model of the hUbA1-UbcH10 Quaternary Complex: In Silico and Experimental Analysis of the Protein-Protein Interactions between E1, E2 and Ubiquitin

Correale, Stefania; de Paola, Ivan; Morgillo, Carmine Marco; Federico, Antonella; Zaccaro, Laura; Pallante, Pierlorenzo; Galeone, Aldo; Fusco, Alfredo; Pedone, Emilia; Luque, F. Javier; Catalanotti, Bruno
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 06/11/2014 EN
Relevância na Pesquisa
568.48367%
UbcH10 is a component of the Ubiquitin Conjugation Enzymes (Ubc; E2) involved in the ubiquitination cascade controlling the cell cycle progression, whereby ubiquitin, activated by E1, is transferred through E2 to the target protein with the involvement of E3 enzymes. In this work we propose the first three dimensional model of the tetrameric complex formed by the human UbA1 (E1), two ubiquitin molecules and UbcH10 (E2), leading to the transthiolation reaction. The 3D model was built up by using an experimentally guided incremental docking strategy that combined homology modeling, protein-protein docking and refinement by means of molecular dynamics simulations. The structural features of the in silico model allowed us to identify the regions that mediate the recognition between the interacting proteins, revealing the active role of the ubiquitin crosslinked to E1 in the complex formation. Finally, the role of these regions involved in the E1–E2 binding was validated by designing short peptides that specifically interfere with the binding of UbcH10, thus supporting the reliability of the proposed model and representing valuable scaffolds for the design of peptidomimetic compounds that can bind selectively to Ubcs and inhibit the ubiquitylation process in pathological disorders.

In Silico Models for Dynamic Connected Cell Cultures Mimicking Hepatocyte-Endothelial Cell-Adipocyte Interaction Circle

Andreoni, Chiara; Orsi, Gianni; De Maria, Carmelo; Montemurro, Francesca; Vozzi, Giovanni
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 15/12/2014 EN
Relevância na Pesquisa
575.45008%
The biochemistry of a system made up of three kinds of cell is virtually impossible to work out without the use of in silico models. Here, we deal with homeostatic balance phenomena from a metabolic point of view and we present a new computational model merging three single-cell models, already available from our research group: the first model reproduced the metabolic behaviour of a hepatocyte, the second one represented an endothelial cell, and the third one described an adipocyte. Multiple interconnections were created among these three models in order to mimic the main physiological interactions that are known for the examined cell phenotypes. The ultimate aim was to recreate the accomplishment of the homeostatic balance as it was observed for an in vitro connected three-culture system concerning glucose and lipid metabolism in the presence of the medium flow. The whole model was based on a modular approach and on a set of nonlinear differential equations implemented in Simulink, applying Michaelis-Menten kinetic laws and some energy balance considerations to the studied metabolic pathways. Our in silico model was then validated against experimental datasets coming from literature about the cited in vitro model. The agreement between simulated and experimental results was good and the behaviour of the connected culture system was reproduced through an adequate parameter evaluation. The developed model may help other researchers to investigate further about integrated metabolism and the regulation mechanisms underlying the physiological homeostasis.

Semantically Linking In Silico Cancer Models

Johnson, David; Connor, Anthony J; McKeever, Steve; Wang, Zhihui; Deisboeck, Thomas S; Quaiser, Tom; Shochat, Eliezer
Fonte: Libertas Academica Publicador: Libertas Academica
Tipo: Artigo de Revista Científica
EN_US
Relevância na Pesquisa
569.0561%
Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability...

An??lise in silico e de express??o da fam??lia g??nica ETHYLENE RESPONSE FACTORS (ERF) no g??nero Malus.; In silico and expression analysis of the ETHYLENE RESPONSE FACTORS (ERF) gene family in the genus Malus.

CERO, Joceani Dal
Fonte: Universidade Federal de Pelotas; Agronomia; Programa de P??s-Gradua????o em Ci??ncia e Tecnologia Agroindustrial; UFPel; BR Publicador: Universidade Federal de Pelotas; Agronomia; Programa de P??s-Gradua????o em Ci??ncia e Tecnologia Agroindustrial; UFPel; BR
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
570.0317%
Regulatory molecules, such as transcription factors, have been thoroughly investigated, especially in hormone-mediated responses that involve gene expression modulation. Frequently, the main determinant of gene expression is its transcriptional rate. Thus, molecular mechanisms underlying transcription regulation have become an important topic in genetic studies of ethylene signaling. The present work aimed to investigate the ERF (Ethylene Response Factor) family employing bioinformatic tools, integrating publicly available datasets from the model species Arabidopsis thaliana and phylogenetic analyses to help elucidating the biological roles of the family in apple. The preliminary survey of the ERF sequences in Malus has provided basic information to be incorporated in further studies of the functional role of ERFs in this perennial species. Expression analyses of MdERF1 and MdERF in apple fruits suggest that other factors, besides ethylene, are involved in their transcriptional regulation in Malus. The second chapter reports the investigation of the transcriptional profiling of those ERF genes in response to pathogen attack, using a biological assay of in vitro propagated plants inoculated with the fungus Venturia inaequalis (apple scab disease). The study has provided evidences of the involvement of MdERF1 in eliciting the plant response; whereas...

Comparison of H. pylori in silico metabolic model predictions with experimental data

Correia, Daniela M.; Cunha, M. L. R.; Azevedo, N. F.; Vieira, Maria João; Rocha, I.
Fonte: Universidade do Minho Publicador: Universidade do Minho
Tipo: Conferência ou Objeto de Conferência
Publicado em /03/2012 ENG
Relevância na Pesquisa
576.1399%
The Systems Biology approach has been replacing the reductionist view that dominated biology research in the last decades. Present biochemical knowledge and genomic databases allowed the development of metabolic models for several organisms, which, however, are still incomplete. The availability of the genome sequence of H. pylori has allowed the construction of a genome-scale metabolic model for this organism. The purposes of this work were to study the growth of H. pylori in a chemically defined medium, to compare the experimental data obtained with the simulated data supplied by the model and analyse the composition of the in silico media used. Cultures were grown at 37ºC under microaerophilic conditions in Ham´s F-12 medium supplemented with fetal bovine serum. Optical density and the counting of CFU/mL were performed for assessing the growth. OptFlux, a software platform for metabolic engineering, which includes several tools such as flux balance analysis (FBA) was employed for simulate the behavior of wild type H. pylori under the conditions used in vivo. The simultaneous use of both approaches allows to correct the in silico model, and on the other hand, to rationally adjust the medium components present in F-12. For instance pimelate...

Análise in silico de regiões promotoras de genes de Xylella fastidiosa; In silico analysis on promoter sequences of protein-coding genes from Xylella fastidiosa

Tria, Fernando Domingues Kümmel
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 24/06/2013 PT
Relevância na Pesquisa
674.4019%
Xylella fastidiosa é uma bactéria gram-negativa, não flagelada, agente causal de doenças de importância econômica como a doença de Pierce nas videiras e a clorose variegada dos citros (CVC) nas laranjeiras. O objetivo do presente trabalho foi realizar análises in silico das sequências promotoras dos genes deste fitopatógeno em uma tentativa de arrecadar novas evidências para o melhor entendimento da dinâmica de regulação transcricional de seus genes, incluindo aqueles envolvidos em mecanismos de patogenicidade e virulência. Para tanto, duas estratégias foram utilizadas para predição de elementos cis-regulatórios em regiões promotoras do genoma da cepa referência 9a5c, comprovadamente associada à CVC. A primeira, conhecida como phylogenetic footprinting, foi empregada para identificação de elementos regulatórios conservados em promotores de unidades transcricionais ortólogas, levando em consideração o conjunto de genes de X. fastidiosa e 7 espécies comparativas. O critério para identificação de unidades transcricionais ortólogas, isto é, unidades trancricionais oriundas de espécies distintas e cujos promotores compartilham elementos cis-regulatórios, foi paralelamente estudado utilizando-se informações regulatórias das bactérias modelos: Pseudomonas aeruginosa...

A Method for Accurate in silico modeling of Ultrasound Transducer Arrays

Guenther, Drake A.; Walker, William F.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
578.55047%
This paper presents a new approach to improve the in silico modeling of ultrasound transducer arrays. While current simulation tools accurately predict the theoretical element spatio-temporal pressure response, transducers do not always behave as theorized. In practice, using the probe's physical dimensions and published specifications in silico, often results in unsatisfactory agreement between simulation and experiment. We describe a general optimization procedure used to maximize the correlation between the observed and simulated spatio-temporal response of a pulsed single element in a commercial ultrasound probe. A linear systems approach is employed to model element angular sensitivity, lens effects, and diffraction phenomena. A numerical deconvolution method is described to characterize the intrinsic electro-mechanical impulse response of the element. Once the response of the element and optimal element characteristics are known, prediction of the pressure response for arbitrary apertures and excitation signals is performed through direct convolution using available tools. We achieve a correlation of 0.846 between the experimental emitted waveform and simulated waveform when using the probe's physical specifications in silico. A far superior correlation of 0.988 is achieved when using the optimized in silico model. Electronic noise appears to be the main effect preventing the realization of higher correlation coefficients. More accurate in silico modeling will improve the evaluation and design of ultrasound transducers as well as aid in the development of sophisticated beamforming strategies.

In Silico Model-Driven Assessment of the Effects of Single Nucleotide Polymorphisms (SNPs) on Human Red Blood Cell Metabolism

Jamshidi, Neema; Wiback, Sharon J.; Palsson, Bernhard Ø.
Fonte: Cold Spring Harbor Laboratory Press Publicador: Cold Spring Harbor Laboratory Press
Tipo: Artigo de Revista Científica
Publicado em /11/2002 EN
Relevância na Pesquisa
676.732%
The completion of the human genome project and the construction of single nucleotide polymorphism (SNP) maps have lead to significant efforts to find SNPs that can be linked to pathophysiology. In silico models of complete biochemical reaction networks relate a cell's individual reactions to the function of the entire network. Sequence variations can in turn be related to kinetic properties of individual enzymes, thus allowing an in silico model-driven assessment of the effects of defined SNPs on overall cellular functions. This process is applied to defined SNPs in two key enzymes of human red blood cell metabolism: glucose-6-phosphate dehydrogenase and pyruvate kinase. The results demonstrate the utility of in silico models in providing insight into differences between red cell function in patients with chronic and nonchronic anemia. In silico models of complex cellular processes are thus likely to aid in defining and understanding key SNPs in human pathophysiology.