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Results for “In silico modeling”

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At least 307 records · Page 17Linked to original sources

PubMed2003

DNA binding specificity of the replication initiator protein, DnaA from Helicobacter pylori.

The key protein in the initiation of Helicobacter pylori chromosome replication, DnaA, has been characterized. The amount of the DnaA protein was estimated to be approximately 3000 molecules per single cell; a large part of the protein was found in the inner membrane. The H.pylori DnaA protein has been analysed using in vitro (gel retardation assay and surface plasmon resonance (SPR)) as well as in silico (comparative computer modeling) studies. DnaA binds a single DnaA box as a monomer, while binding to the fragment containing several DnaA box motifs, the oriC region, leads to the formation of high molecular mass nucleoprotein complexes. In comparison with the Escherichia coli DnaA, the H.pylori DnaA protein exhibits lower DNA-binding specificity; however, it prefers oriC over non-box DNA fragments. As determined by gel retardation techniques, the H.pylori DnaA binds with a moderate level of affinity to its origin of replication (4nM). Comparative computer modelling showed that there are nine residues within the binding domain which are possible determinants of the reduced H.pylori DnaA specificity. Of these, the most interesting is probably the triad PTL; all three residues show significant divergence from the consensus, and Thr398 is the most divergent residue of all.

Bacterial Proteins↗

PubMed2005

Complementary identification of multiple flux distributions and multiple metabolic pathways.

Cell robustness and complexity have been recognized as unique features of biological systems. Such robustness and complexity of metabolic-reaction systems can be explored by discovering, or identifying, the multiple flux distributions (MFD) and redundant pathways that lead to a given external state; however, this is exceedingly cumbersome to accomplish. It is, therefore, highly desirable to establish an effective computational method for their identification, which, in turn, gives rise to a novel insight into the cellular function. An effective approach is proposed for complementarily identifying MFD in metabolic flux analysis and multiple metabolic pathways (MMP) in structural pathway analysis. This approach judiciously integrates flux balance analysis (FBA) based on linear programming and the graph-theoretic method for determining reaction pathways. A single metabolic pathway, with the concomitant flux distribution and the overall reaction manifesting itself as the desired phenotype under some environmental conditions, is determined by FBA from the initial candidate sequence of metabolic reactions. Subsequently, the graph-theoretic method recovers all feasible MMP and the corresponding MFD. The approach's efficacy is demonstrated by applying it to the in silico Escherichia coli model under various culture conditions. The resultant MMP and MFD attaining a unique external state reveal the surprising adaptability and robustness of the intricate cellular network as a key to cell survival against environmental or genetic changes. These results indicate that the proposed approach would be useful in facilitating drug discovery.

Algorithms↗

PubMed2006

The role of electrostatic interactions in the antitumor activity of dimeric RNases.

The cytotoxic action of some ribonucleases homologous to bovine pancreatic RNase A, the superfamily prototype, has interested and intrigued investigators. Their ribonucleolytic activity is essential for their cytotoxic action, and their target RNA is in the cytosol. It has been proposed that the cytosolic RNase inhibitor (cRI) plays a major role in determining the ability of an RNase to be cytotoxic. However, to interact with cRI RNases must reach the cytosol, and cross intracellular membranes. To investigate the interactions of cytotoxic RNases with membranes, cytotoxic dimeric RNases resistant, or considered to be resistant to cRI, were assayed for their effects on negatively charged membranes. Furthermore, we analyzed the electrostatic interaction energy of the RNases complexed in silico with a model membrane. The results of this study suggest that close correlations can be recognized between the cytotoxic action of a dimeric RNase and its ability to complex and destabilize negatively charged membranes.

Animals↗

PubMed2006

Functional evolution of the pregnane X receptor.

The pregnane X receptor (PXR; NR1I2) is a nuclear hormone receptor (NR) that transcriptionally regulates genes encoding transporters and drug-metabolising enzymes in the liver and intestine. PXR activation leads to enhanced metabolism and elimination of xenobiotics and endogenous compounds such as hormones and bile salts. Relative to other vertebrate NRs, PXR has the broadest specificity for ligand activators by virtue of a large, flexible ligand-binding cavity. In addition, PXR has the most extensive sequence diversity across vertebrate species in the ligand-binding domain of any NR, with significant pharmacological differences between human and rodent PXRs, and especially marked divergence between mammalian and nonmammalian PXRs. The unusual properties of PXR complicate the use of in silico and animal models to predict in vivo human PXR pharmacology. Research into the evolutionary history of the PXR gene has also provided insight into the function of PXR in humans and other animals.

Amino Acid Sequence↗

PubMed2005

Using ontologies in PROTEUS for modeling proteomics data mining applications.

Bioinformatics applications are often characterized by a combination of (pre) processing of raw data representing biological elements, (e.g. sequence alignment, structure prediction), and an high level data mining analysis. Developing such applications needs knowledge of both data mining and bioinformatics domains, that can be effectively achieved by combining ontology about the application domain and ontology about the approaches and processes to solve the given problem. In this paper we talk about using ontologies to model proteomics in silico experiments. In particular data mining of mass spectrometry proteomics data is considered.

Computational Biology↗

PubMedDate not supplied

Global model for optimizing crossflow microfiltration and ultrafiltration processes: a new predictive and design tool.

A global model and algorithm that predicts the performance of crossflow MF and UF process individually or in combination in the laminar flow regime is presented and successfully tested. The model accounts for solute polydispersity, ionic environment, electrostatics, membrane properties and operating conditions. Computer programs were written in Fortran 77 for different versions of the model algorithm that can optimize MF/UF processes rapidly in terms of yield, purity, selectivity, or processing time. The model is validated successfully with three test cases: separation of bovine serum albumin (BSA) from hemoglobin (Hb), capture of immunoglobulin (IgG) from transgenic goat milk by MF, and separation of BSA from IgG by UF. These comparisons demonstrate the capability of the global model to conduct realistic in silico simulations of MF and UF processes. This model and algorithm should prove to be an invaluable technique to rapidly design new or optimize existing MF and UF processes separately or in combination in both pressure-dependent and pressure-independent regimes.

Algorithms↗

PubMed2004

A discrete approach for a model of temporal learning by the cerebellum: in silico classical conditioning of the eyeblink reflex.

Cerebellar cortex is known to be involved in acquisition and expression of eyeblink conditioned reflex. These phenomena imply temporal intervals of learning. Several cellular and network mechanisms have been proposed to produce the eyeblink. In this paper we briefly review the main theories concerning temporal coding, and we propose an alternative way of producing and storing delays and signal sequences after supervised learning. A network of Leaky Integrate-and-Fire (LIF) neurons is built, taking into account several cerebellar features. This network is then trained to produce simple or multiple eyeblink delays using (i) the classical conditioning paradigm and (ii) known data on cerebellar spike timing dependent plasticity (STDP). The resulting model behaves like an adaptive temporal filter. It improves cell subpopulations effects according to their mean firing rate. This rate based selection allows robust supervised learning of temporal events (i.e., delayed signals) and gives the network ability to react with anticipation on the arousal of a noxious event.

Action Potentials↗

PubMed2006

Assessment of QT liabilities in drug development.

Since the publication, in 1997, of the CPMP (Committee for Proprietary Medicinal Products) Points to Consider document on "The assessment of potential for QT prolongation by non-cardiovascular medicinal products," both regulatory bodies and the pharmaceutical industry have paid increasing attention to the conduct of careful preclinical studies on the subject. Regulatory attention has focused on the drafting of Safety Pharmacology guidelines through the ICH (International Conference on Harmonization) process, which resulted in approval by the ICH and acceptance by the three main regions (USA, Europe, and Japan) of the ICH S7A guideline. The guideline does not deal only with cardiovascular studies and does not provide guidance on QT investigations. This part has been deferred to a second guideline (ICH S7B). Nevertheless, pharmaceutical companies have implemented screening strategies aimed at selecting compounds that do not present QT liabilities. These strategies can differ according to the pharmaceutical class, while experimental models differ according to the stage of development of the compound. Several in vitro models are employed in discovery (radioligand binding, high-throughput patch clamp, efflux, and fluorescence assays). These models, coupled with in silico methods, allow companies to screen a high number of compounds. Other in vitro models, applied later in the R&D process (action potential duration, APD, in Purkinje fibers or papillary muscle and the isolated heart) are useful in better describing the activity of compounds on cardiac ion channels. The most robust and accepted in vivo test is represented by telemetry studies in conscious non-rodents.

Animals↗

PubMed2002

In silico prediction of optimal in vivo delivery properties using convolution-based model and clinical trial simulation.

PURPOSE: To develop a new strategy for the in silico evaluation of the optimal in vivo delivery properties of a drug, minimizing a cost function defined by the brain receptor occupancy obtained in positron-emission tomography experiments. METHODS: A convolution-based model was formulated to link in vivo delivery rate to plasma concentrations whereas a second-stage model was used to link plasma concentrations to the pharmacodynamic effect. A feedback control approach was applied to identify the optimal in vivo delivery rate given an appropriate optimality criterion. Finally, clinical trial simulation was used as a supportive tool for decision-making by evaluating different scenarios accounting for pharmacokinetic/pharmacodynamic parameter uncertainty, inter-subject variability. and drug potency. RESULTS: The results revealed that the mean in vivo delivery time significantly affects brain receptor occupancy whereas the fraction of the dose available for the systemic circulation shows the highest influence on brain receptor occupancy for a given in vivo delivery rate. Finally, variability on receptor occupancy seems to be more affected by the inter-individual variability on the disposition PK parameters. CONCLUSION: The integration of convolution-based model. feedback control approach, and clinical trial simulation offers a unique tool for in ilico improvement of the drug development process by identifying critical issues on drug properties, optimal in vivo delivery rate, and potential problems related to the inter-individual variability.

Clinical Trials as Topic↗

PubMed2004

Discrete event modeling of CD4+ memory T cell generation.

Studies of memory T cell differentiation are hampered by a lack of quantitative models to test hypotheses in silico before in vivo experimentation. We created a stochastic computer model of CD4+ memory T cell generation that can simulate and track 10(1)-10(8) individual lymphocytes over time. Parameters for the model were derived from experimental data using naive human CD4+ T cells stimulated in vitro. Using discrete event computer simulation, we identified two key variables that heavily influence effector burst size and the persistent memory pool size: the cell cycle dependent probability of apoptosis, and the postactivation mitosis at which memory T cells emerge. Multiple simulations were performed and varying critical parameters permitted estimates of how sensitive the model was to changes in all of the model parameters. We then compared two hypotheses of CD4+ memory T cell generation: maturation from activated naive to effector to memory cells (model I) vs direct progression from activated naive to memory cells (model II). We find that direct progression of naive to memory T cells does not explain published measurements of the memory cell mass unless postactivation expansion of the memory cell cohort occurs. We conclude that current models suggesting direct progression of activated naive cells to the persistent memory phenotype (model II) do not account for the experimentally measured size of the postactivation CD4+, Ag-specific, memory T cell cohort.

Apoptosis↗

PubMed2002

Structural model of nicotinic acetylcholine receptor isotypes bound to acetylcholine and nicotine.

BACKGROUND: Nicotine is a psychoactive drug presenting a diverse array of biological activities, some positive, such as enhancement of cognitive performances, others negative, such as addiction liability. Ligands that discriminate between the different isotypes of nicotinic acetylcholine receptors (nAChRs) could present improved pharmacology and toxicity profile. RESULTS: Based on the recent crystal structure of a soluble acetylcholine binding protein from snails, we have built atomic models of acetylcholine and nicotine bound to the pocket of four different human nAChR subtypes. The structures of the docked ligands correlate with available biochemical data, and reveal that the determinants for isotype selectivity are relying essentially on four residues, providing diversity of the ligand binding pocket both in terms of Van der Waals boundary, and electrostatic potential. We used our models to screen in silico a large compound database and identify a new ligand candidate that could display subtype selectivity. CONCLUSION: The nAChR-agonist models should be useful for the design of nAChR agonists with diverse specificity profiles.

Acetylcholine↗

PubMed2004

Atomic model of the Thermus thermophilus 70S ribosome developed in silico.

The ribosome is a large molecular complex that consists of at least three ribonucleic acid molecules and a large number of proteins. It translates genetic information from messenger ribonucleic acid and makes protein accordingly. To better understand ribosomal function and provide information for designing biochemical experiments require knowledge of the complete structure of the ribosome. For expanding the structural information of the ribosome, we took on the challenge of developing a detailed Thermus thermophilus ribosomal structure computationally. By combining information derived from the low-resolution x-ray structure of the 70S ribosome (providing the overall fold), high-resolution structures of the ribosomal subunits (providing the local structure), sequences, and secondary structures, we have developed an atomic model of the T. thermophilus ribosome using a homology modeling approach. Our model is stereochemically sound with a consistent single-species sequence. The overall folds of the three ribosomal ribonucleic acids in our model are consistent with those in the low-resolution crystal structure (root mean-square differences are all <1.9 angstroms). The large overall interface area (approximately 2500 angstroms2) of intersubunit bridges B2a, B3, and B5, and the inherent flexibility in regions connecting the contact residues are consistent with these bridges serving as anchoring patches for the ratcheting and rolling motions between the two subunits during translocation.

Computer Simulation↗

PubMedDate not supplied

Progress in predicting human ADME parameters in silico.

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.

Catalysis↗

PubMed2005

Cell culture-based models for intestinal permeability: a critique.

The model systems that are currently used to determine the intestinal permeability characteristics of discovery compounds often represent a combination of high-throughout, but less predictive, in silico and in vitro models and low-throughput, but more predictive, in vivo models. Cell-based permeability models have been integrated into the discovery paradigm for some time and represent the "method of choice" across the industry. Here, in addition to an objective analysis of the utility of cell culture models for permeability screening, anticipated future trends in the field of cell culture models are discussed.

Animals↗

PubMed2005

Rational design of complementary peptides to the betaAmyloid 29-42 fusion peptide: an application of PepDesign.

Peptides in solution currently exist under several conformations; an equilibrium which varies with solvent polarity. Despite or because of this structure versatility, peptides can be selective biological tools: they can adapt to a target, vary conformation with solvents and so on. These capacities are crucial for cargo carriers. One promising way of using peptides in biotechnologies is to decipher their medium-sequence-structure-function relationships and one approach is molecular modelling. Only few "in silico" methods of peptide design are described in the literature. Most are used in support of experimental screening of peptide libraries. However, the way they are made does not teach us much for future researches. In this paper, we describe an "in silico" method (PepDesign) which starts by analysing the native interaction of a peptide with a target molecule in order to define which points are important. From there, a modelling protocol for the design of 'better' peptides is set. The PepDesign procedure calculates new peptides fulfilling the hypothesis, tests the conformational space of these peptides in interaction with the target by angular dynamics and goes up to the selection of the best peptide based on the analysis of complex structure properties. Experimental biological assays are finally used to test the selected peptides, hence to validate the approach. Applications of PepDesign are wide because the procedure will remain similar irrespective of the target which can be a protein, a drug or a nucleic acid. In this paper, we describe the design of peptides which binds to the fusogenic helical form of the C-terminal domain of the Abeta peptide (Abeta29-42).

Amino Acid Sequence↗

PubMed2005

Metabolic homeostasis in the human erythrocyte: in silico analysis.

A detailed computer model of human erythrocyte metabolism was shown to predict three steady states, two stable and one unstable. The most extreme steady state is characterized by almost zero concentrations of all the phosphorylated intermediates. The "normal" steady state is remarkably robust in the face of large changes in the activity of most of the enzymes of glycolysis and the pentose phosphate pathway: this steady state can be viewed as an attractor towards which the system returns following a metabolic perturbation. Focus is given to three responses of the system: (1) the 'energy charge' that pertains to the concentration of ATP relative to all purine nucleotides; (2) redox power expressed as the ratio of reduced-to-total glutathione and (3) the concentration of 2,3-bisphosphoglycerate, that directly affects the oxygen affinity of haemoglobin thus affecting the main physiological function of the cell. The collapse of the normal steady state in what can be viewed topologically as a catastrophe is posited as one key element of erythrocyte senescence and it is particularly important for erythrocyte destruction in patients with an inborn enzyme deficiency.

2,3-Diphosphoglycerate↗

PubMed2005

Power of in silico QTL mapping from phenotypic, pedigree, and marker data in a hybrid breeding program.

Most quantitative trait locus (QTL) mapping studies in plants have used designed mapping populations. As an alternative to traditional QTL mapping, in silico mapping via a mixed-model approach simultaneously exploits phenotypic, genotypic, and pedigree data already available in breeding programs. The statistical power of this in silico mapping method, however, remains unknown. Our objective was to evaluate the power of in silico mapping via a mixed-model approach in hybrid crops. We used maize (Zea mays L.) as a model species to study, by computer simulation, the influence of number of QTLs (20 or 80), heritability (0.40 or 0.70), number of markers (200 or 400), and sample size (600 or 2,400 hybrids). We found that the average power to detect QTLs ranged from 0.11 to 0.59 for a significance level of alpha=0.01, and from 0.01 to 0.47 for alpha=0.0001. The false discovery rate ranged from 0.22 to 0.74 for alpha=0.01, and from 0.05 to 0.46 for alpha=0.0001. As with designed mapping experiments, a large sample size, high marker density, high heritability, and small number of QTLs led to the highest power for in silico mapping via a mixed-model approach. The power to detect QTLs with large effects was greater than the power to detect QTL with small effects. We conclude that gene discovery in hybrid crops can be initiated by in silico mapping. Finding an acceptable compromise, however, between the power to detect QTL and the proportion of false QTL would be necessary.

Breeding↗

PubMed2003

ADME-Tox in drug discovery: integration of experimental and computational technologies.

Over the past ten years, in vitro experimental tools to characterize ADME-Tox profiles of compounds have been applied in early stages of the drug discovery process to increase the success rate of discovery programmes and to progress better candidates into drug development. Application of in silico ADME-Tox models has further enhanced discovery support, enabling virtual screening of compounds and thus, application of ADME-Tox at every stage of the discovery process. Ultimately, effective and efficient ADME-Tox support of discovery will depend on a complementary and synergistic use of experimental and in silico ADME-Tox.

Animals↗