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Results for “In silico modeling”

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At least 271 records · Page 15Linked to original sources

PubMed2006

[Microarray as a standard laboratory technique and as an unprecedented tool for understanding systems biology].

It is now possible to examine all the expressed genes present in a cell (transcriptome) simultaneously using microarray. Thus, microarrays have attracted tremendous interest among biologists. We have applied microarray technology to various studies regarding allergic diseases. Although we and others have discovered various novel molecules crucially involved in the pathogenesis of the disease, transcriptome assay is now expected as a tool for understanding overall molecule balancing, i.e., systems biology. Here we introduce examples of our trials to understand the functional roles of a certain cell type related to allergy as has been published in the web database. In the near future, we will be able to construct human inflammatory cell models in silico (in a computer) by analyzing integrative information regarding the genome, transcriptome and proteome of mast cells and can test our hypothesis without performing in vitro testing.

Animals↗

PubMed2004

Agent-based computational modeling of wounded epithelial cell monolayers.

Computational modeling of biological systems, or in silico biology, is an emerging tool for understanding structure and order in biological tissues. Computational models of the behavior of epithelial cells in monolayer cell culture have been developed and used to predict the healing characteristics of scratch wounds made to urothelial cell cultures maintained in low- and physiological [Ca2+] environments. Both computational models and in vitro experiments demonstrated that in low exogenous [Ca2+], the closure of 500-microm scratch wounds was achieved primarily by cell migration into the denuded area. The wound healing rate in low (0.09 mM) [Ca2+] was approximately twice as rapid as in physiological (2 mM) [Ca2+]. Computational modeling predicted that in cell cultures that are actively proliferating, no increase in the fraction of cells in the S-phase would be expected, and this conclusion was supported experimentally in vitro by bromodeoxyuridine incorporation assay. We have demonstrated that a simple rule-based model of cell behavior, incorporating rules relating to contact inhibition of proliferation and migration, is sufficient to qualitatively predict the calcium-dependent pattern of wound closure observed in vitro. Differences between the in vitro and in silico models suggest a role for wound-induced signaling events in urothelial cell cultures.

Algorithms↗

PubMed2003

A homology model of penicillin acylase from Alcaligenes faecalis and in silico evaluation of its selectivity.

A three-dimensional model of the relatively unknown penicillin acylase from Alcaligenes faecalis (PA-AF) was built up by means of homology modeling based on three different crystal structures of penicillin acylase from various sources. An in silico selectivity study was performed to compare this homology model to the structure of the Escherichia coli enzyme (PA-EC) in order to find any selectivity differences between the two enzymes. The program GRID was applied in combination with the principal component analysis technique to identify the regions of the active sites where the PAs potentially engage different interactions with ligands. These differences were further analyzed and confirmed by molecular docking simulations. The PA-AF homology model provided the structural basis for the explanation of the different enantioselectivities of the enzymes previously demonstrated experimentally and reported in the literature. Different substrate selectivities were also predicted for PA-AF compared to PA-EC. Since no crystallographic data are available for PA-AF to date, the three-dimensional homology model represents a useful and efficient tool for fully exploiting this attractive and efficient biocatalyst, particularly in enantioselective acylations of amines.

Alcaligenes faecalis↗

PubMed2006

C3 photosynthesis in silico.

A computer model comprising light reactions, electron-proton transport, enzymatic reactions, and regulatory functions of C3 photosynthesis has been developed as a system of differential budget equations for intermediate compounds. The emphasis is on electron transport through PSII and PSI and on the modeling of Chl fluorescence and 810 nm absorptance signals. Non-photochemical quenching of PSII excitation is controlled by lumenal pH. Alternative electron transport is modeled as the Mehler type O2 reduction plus the malate-oxaloacetate shuttle based on the chloroplast malate dehydrogenase. Carbon reduction enzymes are redox-controlled by the ferredoxin-thioredoxin system, sucrose synthesis is controlled by the fructose 2,6-bisphosphate inhibition of cytosolic FBPase, and starch synthesis is controlled by ADP-glucose pyrophosphorylase. Photorespiratory glycolate pathway is included in an integrated way, sufficient to reproduce steady-state rates of photorespiration. Rate-equations are designed on principles of multisubstrate-multiproduct enzyme kinetics. The parameters of the model were adopted from literature or were estimated from fitting the photosynthetic rate and pool sizes to experimental data. The model provided good simulations for steady-state photosynthesis, Chl fluorescence, and 810 nm transmittance signals under varying light, CO2 and O2 concentrations, as well as for the transients of post-illumination CO2 uptake, Chl fluorescence induction and the 810 nm signal. The modeling shows that the present understanding of photosynthesis incorporated in the model is basically correct, but still insufficient to reproduce the dark-light induction of photosynthesis, the time kinetics of non-photochemical quenching, 'photosynthetic control' of plastoquinone oxidation, cyclic electron flow around PSI, oscillations in photosynthesis. The model may find application for predicting the results of gene transformations, the analysis of kinetic experimental data, the training of students.

Carbon↗

PubMed2002

Modeling a hox gene network in silico using a stochastic simulation algorithm.

The amount of molecular information that has been gathered about Hox cis-regulatory mechanisms allows us to take the next important step: integrating the results and constructing a higher-level model for the interaction and regulation of the Hox genes. Here, we present the results of our investigation into a cis-regulatory network for the early Hox genes. Instead of using conventional differential equation approaches for analyzing the system, we have adopted the use of a stochastic simulation algorithm (SSA) to model the network. The model allows us to track in detail the behavior of each component of a biochemical pathway and to produce computerized movies of the time evolution of the system that is a result of the dynamic interplay of these various components. The simulation is able to reproduce key features of the wild-type pattern of gene expression, and in silico experiments yield results similar to their corresponding in vivo experiments. This analysis shows the utility of using stochastic methods to model biochemical networks. In addition, the model has suggested several intriguing new results that are currently being investigated in vivo.

Algorithms↗

PubMed2006

Development and validation of an in silico P450 profiler based on pharmacophore models.

In today's drug discovery process, the very early consideration of ADME properties is aimed at a reduction of drug candidate drop out rate in later development stages. Apart from in vitro testing, in silico methods are evaluated as complementary screening tools for compounds with unfavorable ADME attributes. Especially members of the cytochrome P450 (P450) enzyme superfamily-- e.g. P450 1A2, P450 2C9, P450 2C19, P450 2D6, and P450 3A4-- contribute to xenobiotic metabolism, and compound interaction with one of these enzymes is therefore critically evaluated. Pharmacophore models are widely used to identify common features amongst ligands for any target. In this study, both structure-based and ligand-based models for prominent drug-metabolizing members of the P450 family were generated employing the software packages LigandScout and Catalyst. Essential chemical ligand features for substrate and inhibitor activity for all five P450 enzymes investigated were determined and analyzed. Finally, a collection of 11 pharmacophores for substrates and inhibitors was evaluated as an in silico P450 profiling tool that could be used for early ADME estimation of new chemical entities.

Cytochrome P-450 Enzyme Inhibitors↗

PubMed2004

Validation of model of cytochrome P450 2D6: an in silico tool for predicting metabolism and inhibition.

There has been much interest in the development of a predictive model of cytochrome P450 2D6 particularly because this enzyme is involved in the oxidation of at least 50 drugs. Previously we have described the combined use of homology modeling and molecular docking to correctly position a range of substrates in the CYP2D6 active site with the known sites of metabolism above the heme. Here, our approach identifies correctly the site of metabolism of the atypical (no basic nitrogen) cytochrome P450 2D6 substrate, spirosulfonamide. The same method is used to screen a small compound database for cytochrome P450 2D6 inhibition. A database containing 33 compounds from the National Cancer Institute database was docked into our cytochrome P450 2D6 homology model using the program GOLDv2.0. Experimental IC50 values for the 33 compounds were determined; comparison with the corresponding docked scores revealed a correlation with a regression coefficient of r2 = 0.61 (q2 = 0.59). The method was able to discriminate between tight and weak binding compounds and correctly identified several novel inhibitors. The results therefore suggest that our approach, which combines homology modeling with molecular docking, has produced a useful predictive in silico tool for cytochrome P450 2D6 inhibition, which is best used as one filter in a multifilter database screen.

Binding Sites↗

PubMed2005

Combining in silico tools and NMR data to validate protein-ligand structural models: application to matrix metalloproteinases.

A combination of in silico tools and experimental NMR data is proposed for relatively fast determination of protein-ligand structural models and demonstrated from known inhibitors of matrix metalloproteinases (MMP). The 15N 1H heteronuclear single quantum coherence (HSQC) spectral assignment and the 3D structure, either X-ray or NMR, are needed. In this method, the HSQC spectrum with or without the ligand is used to determine the interaction region of the ligand. Docking calculations are then performed to obtain a set of structural models. From the latter, the nuclear Overhauser effects (NOEs) between the ligand and the protein can be predicted. Guided by these predictions, a number of NOEs can be detected and assigned through a HSQC NOESY experiment. These data are used as structural restraints to reject/refine the initial structural models through further in silico work. For a test protein (MMP-12, human macrophage metalloelastase), a final structure of a protein-ligand adduct was obtained that matches well with the full structural determination. A number of structural predictions were then made for adducts of a similar protein (MMP-1, human fibroblast collagenase) with the same and different ligands. The quality of the final results depended on the type and number of experimental NOEs, but in all cases, a well-defined ligand conformation in the protein binding site was obtained. This protocol is proposed as a viable alternative to the many approaches described in the literature.

Alanine↗

PubMed2007

Plasma protein binding affinity and its relationship to molecular structure: an in-silico analysis.

In-silico plasma protein binding (PPB) models have been generated on human and rat in-house datasets, and on a human dataset sourced from the literature. From the results reported herein, it is apparent that models built on datasets relevant to the chemotypes under investigation in lead optimization programs will perform considerably better in this role than those generated on diverse compounds sourced from the literature. The in-house human and rat partial least-squares regression (PLS) models have cross-validated q2 values of 0.53 and 0.42 on the training sets, respectively. On the independent test and validation sets, they display similar predictive ability, with logK prediction errors of approximately 0.5 log units. This compares to approximately 0.25 log units variability expected for experiment. Given the considerable interspecies PPB differences, the prediction of PPB in one species using measurements in the other is no better than a prediction from an in-silico model generated on that species.

Analysis of Variance↗

PubMed2005

Enhanced phenotyping of complex traits with a circadian clock model.

Models of biological systems are increasingly used to generate and test predictions in silico. This article explores the basic workings of a multifeedback network model of a circadian clock. In a series of in silico experiments, we investigated the influence of the number of feedbacks by adding and removing one or more. We further explore the possibilities of testing in silico models in classic "circadian" protocols. In addition, we performed an in silico mutagenesis screen (by altering parameters throughout the network), creating a library of mutants (based on "phenotype," not "genotype"), and subjected them to a variety of straightforward "circadian" protocols. The results of this mutant "taxonomy" are surprising. While most mutants can be identified (separated) using a limited set of experimental protocols, some resist such a separation, even when "mutations" are at vastly different locations within the complex model. Furthermore, some protocols distinguish similar alleles of the same component, which would be counterproductive. The described taxonomy invites experimental verification, in vivo, and may ultimately streamline genotyping of complex traits, which may have been based previously on imprecise phenotypes.

Circadian Rhythm↗

PubMed2006

WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks.

SUMMARY: WebCell is a web-based environment for managing quantitative and qualitative information on cellular networks and for interactively exploring their steady-state and dynamic behaviors in response to systemic perturbations. It is designed as a user-friendly web interface, allowing users to efficiently construct, visualize, analyze and store reaction network models, thereby facilitating kinetic modeling and in silico simulation of biological systems of interest. A collected model library is also available to provide comprehensive implications for cellular dynamics of the published models.

Cell Physiological Phenomena↗

PubMed2005

Homology-modeled ligand-binding domains of zebrafish estrogen receptors alpha, beta1, and beta2: from in silico to in vivo studies of estrogen interactions in Danio rerio as a model system.

Homology models were constructed for the ligand-binding domains of zebrafish estrogen receptors (zfERs) alpha, beta(1), and beta(2). Estradiol-binding sites are nearly identical in zfERs and their human homologs, suggesting that zebrafish will serve as a good model system for studying human ER-binding drugs. Conversely, studies of endocrine disruptor effects on zebrafish will benefit from the wealth of data available on xenoestrogen interactions with human ERs. Compounds flagged by the Interagency Coordinating Committee on the Validation of Alternative Methods for endocrine disruptor screening were docked into our zfER homology models. Ideally, these in silico docking studies would be complemented with in vivo binding studies. To this end, fluorescently tagged estradiol was docked into zfERalpha and found to bind in the same manner as in human ERalpha, with fluorescein preferentially occupying a region between helices 11 and 12. Fluorescently tagged estradiol was synthesized and was found to localize along the path of primordial germ cell migration in the developing zebrafish embryo 3 d after fertilization, consistent with previous reports of 1) a role for estradiol in sex determination, and 2) the first appearance of ERs 2 d after fertilization. These data provide a foundation for future in silico and in vivo binding studies of estrogen agonists and antagonists with zebrafish ERs.

Amino Acid Sequence↗

PubMed2005

In silico predictions of drug solubility and permeability: two rate-limiting barriers to oral drug absorption.

Aqueous drug solubility and intestinal drug permeability are two of the most important factors influencing drug absorption. If the developability of a drug is to be included in the lead optimization, new experimental and computational models of solubility and permeability are needed. These models must have the capacity to handle a large amount of data. Nowadays, epithelial cell culture models such as Caco-2 are routinely used to assess intestinal drug permeability and transport in drug discovery settings. The permeability values obtained from the Caco-2 cell monolayers have been traditionally used to devise in silico models for the prediction of drug absorption. In this paper, the use of molecular surface areas as descriptors of permeability and solubility will be reviewed. Moreover, a virtual filter for the prediction of oral drug developability based on the successful combination of in vitro and in silico models of drug permeability and aqueous drug solubility will be discussed.

Administration, Oral↗

PubMed2001

Evolution in silico and in vitro: the RNA model.

Theoretical concepts and experiments dealing with the evolution of molecules in vitro reached a state that allows for direct applications to the design of biomolecules with predefined properties. RNA evolution in vitro represents a basis for the development of a new and comprehensive model of evolution, focusing on the phenotype and its fitness relevant properties. Relations between genotypes and phenotypes are described by mappings from genotype space onto a space of phenotypes, which are many-to-one and thus give ample room for neutrality as expressed by the existence of extended neutral networks in genotype space. The RNA model reduces genotype-phenotype relations to mappings from sequences into secondary structures of minimal free energies and allows for derivation of otherwise inaccessible quantitative results. Continuity and discontinuity in evolution are defined through a new notion of accessibility in phenotype space that provides a basis for straight forward interpretation of computer simulations on RNA optimization; furthermore, it reveals the constructive role of random genomic drift in the search for phenotypes of higher fitness. The effects of population size on the course of evolutionary optimization can be predicted quantitatively by means of a simple stochastic model based on a birth-anddeath process with immigration.

Base Sequence↗

PubMed2025

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we have integrated Carafe into the widely used Skyline tool.

Journal Article↗

PubMed2006

In silico human and rat Vss quantitative structure-activity relationship models.

We present herein a QSAR tool enabling an entirely in silico prediction of human and rat steady-state volume of distribution (Vss), to be made prior to chemical synthesis, preceding detailed allometric or mechanistic assessment of Vss. Three different statistical methodologies, Bayesian neural networks (BNN), classification and regression trees (CART), and partial least squares (PLS) were employed to model human (N=199) and rat (N=2086) data sets. The results in prediction of an independent test set show the human model has an r2 of 0.60 and an rms error in prediction of 0.48. The corresponding rat model has an r2 of 0.53 and an rms error in prediction of 0.37, indicating both models could be very useful in the early stages of the drug discovery process. This is the first reported entirely in silico approach to the prediction of rat and human steady-state volume of distribution.

Animals↗

PubMed2006

Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae.

We describe the use of model-driven analysis of multiple data types relevant to transcriptional regulation of metabolism to discover novel regulatory mechanisms in Saccharomyces cerevisiae. We have reconstructed the nutrient-controlled transcriptional regulatory network controlling metabolism in S. cerevisiae consisting of 55 transcription factors regulating 750 metabolic genes, based on information in the primary literature. This reconstructed regulatory network coupled with an existing genome-scale metabolic network model allows in silico prediction of growth phenotypes of regulatory gene deletions as well as gene expression profiles. We compared model predictions of gene expression changes in response to genetic and environmental perturbations to experimental data to identify potential novel targets for transcription factors. We then identified regulatory cascades connecting transcription factors to the potential targets through a systematic model expansion strategy using published genome-wide chromatin immunoprecipitation and binding-site-motif data sets. Finally, we show the ability of an integrated metabolic and regulatory network model to predict growth phenotypes of transcription factor knockout strains. These studies illustrate the potential of model-driven data integration to systematically discover novel components and interactions in regulatory and metabolic networks in eukaryotic cells.

Chromatin Immunoprecipitation↗

PubMed2025

Kinome analysis of Madurella mycetomatis identified kinases in the cell wall integrity pathway as novel potential therapeutic drug targets in eumycetoma caused by Madurella mycetomatis.

Eumycetoma is a neglected tropical subcutaneous disease most commonly caused by the fungus Madurella mycetomatis. Currently, eumycetoma is treated by a combination of antifungal therapy and surgery, with limited success rates. To identify novel drug targets we used an in silico approach to determine the kinases present in M. mycetomatis genome and rank them as potential drug targets. In total 132 predicted kinases were identified in M. mycetomatis, of which 21 were predicted to be essential for fungal viability and 4 of these had no human orthologues. Two were linked to the Cell Wall Integrity (CWI) signalling pathway and were expressed in a Galleria mellonella infection model. Several kinase inhibitors were identified after in silico modelling, however only 8 were able to inhibit growth. Five had predicted binding affinity with components of the CWI. Altogether, the CWI shows potential as a drug target for further evaluation.

Madurella↗