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[Study of the permeability of bilayer lipid membranes by the Monte-Carlo method].

Results are presented of computer modelling of small molecules transfer through the double lipid layer of the membrane. The problem is considered on the basis of "kink" theory. Dependence of the permeability coefficient P on temperature T and area A per one lipid chain on the interface surface was studied. It has been shown that with T and (or) A decrease the value P decreases. The P values calculated, as well as probabilities of "kinks" and gauche-isomers for the chains in liquid--crystal bilayer well agree with the experimental data. It has been shown that the admixture molecules introduced between the bilayer lipid chains decrease its permeability.

Lipid Bilayers↗
PubMed2000

Energy- and intensity-modulated electron beams for radiotherapy.

This work investigates the feasibility of optimizing energy- and intensity-modulated electron beams for radiation therapy. A multileaf collimator (MLC) specially designed for modulated electron radiotherapy (MERT) was investigated both experimentally and by Monte Carlo simulations. An inverse-planning system based on Monte Carlo dose calculations was developed to optimize electron beam energy and intensity to achieve dose conformity for target volumes near the surface. The results showed that an MLC with 5 mm leaf widths could produce complex field shapes for MERT. Electron intra- and inter-leaf leakage had negligible effects on the dose distributions delivered with the MLC, even at shallow depths. Focused leaf ends reduced the electron scattering contributions to the dose compared with straight leaf ends. As anticipated, moving the MLC position toward the patient surface reduced the penumbra significantly. There were significant differences in the beamlet distributions calculated by an analytic 3-D pencil beam algorithm and the Monte Carlo method. The Monte Carlo calculated beamlet distributions were essential to the accuracy of the MERT dose distribution in cases involving large air gaps, oblique incidence and heterogeneous treatment targets (at the tissue-bone and bone-lung interfaces). To demonstrate the potential of MERT for target dose coverage and normal tissue sparing for treatment of superficial targets, treatment plans for a hypothetical treatment were compared using photon beams and MERT.

Algorithms↗
PubMed1998

Photon buildup in orthovoltage X-ray beams.

Orthovoltage x-ray beams exhibit the characteristic of depth dose buildup which is not well described in the literature. The principal reason for this phenomenon is the increase in dose deposited due to electrons set in motion by secondary (Compton) scattered photons within the phantom, as depth is increased until longitudinal equilibrium is reached. This happens within a few millimetres of the surface and has been demonstrated both experimentally and by Monte Carlo methods. The Monte Carlo technique also enabled description of a second order primary dose buildup effect (due to longitudinal electronic disequilibrium) that would be impossible to detect with conventional detectors due to the short range of the electrons. The magnitude of buildup was observed to alter with various combinations of beam parameters. Variations will also occur with detectors used to measure buildup. It is recommended that radiation oncology departments assess this effect in the context of their clinical data in current use to ensure that there are not doses higher than prescribed being applied a few millimetres below the skin surface, especially if data was collected with a thin windowed, parallel plate ionisation chamber and/or that coarse steps for depth dose data collection were used along the beam central axis.

Biophysical Phenomena↗
PubMed1996

A generalization of Hewitt's test for seasonality.

BACKGROUND: Hewitt's statistic for seasonality in monthly data is the maximal rank sum among all possible rank sums derived using consecutive 6-month periods. In this paper, Hewitt's test is extended to include those instances where 3, 4 or 5-month pulses or periods of raised incidence are hypothesized. METHODS: Monte Carlo methods are used to drive the approximate distribution of the test statistic under the null hypothesis, when the length of the hypothesized period is k = 3, 4, or 5. A combinatorial method is used to derive exact levels for the test statistic. The test is applied to monthly data on adolescent suicide. Finally, the power of the test is compared with the chi2 statistic using Monte Carlo simulation. RESULTS: The distribution of the test statistic was found and used to test the null hypothesis of no seasonal variation in monthly adolescent suicides, using a period of k = 3 months. The null hypothesis was rejected, indicating seasonality in the data. Monte Carlo simulations show the test statistic to be more powerful than the chi2 statistic when sample sizes are small. CONCLUSIONS: This generalization of Hewitt's test should be most useful in those instances where the researcher wishes to carry out a quick and simple test of the null hypothesis of no seasonality against the alternative of a predetermined 3, 4, or 5 month period of raised incidence. When there is no a priori hypothesis about the appropriate length of period.

Epidemiologic Methods↗
PubMed2005

Markov chains of infinite order and asymptotic satisfaction of balance: application to the adaptive integration method.

Adaptive Monte Carlo methods can be viewed as implementations of Markov chains with infinite memory. We derive a general condition for the convergence of a Monte Carlo method whose history dependence is contained within the simulated density distribution. In convergent cases, our result implies that the balance condition need only be satisfied asymptotically. As an example, we show that the adaptive integration method converges.

Algorithms↗
PubMed1998

Prediction of binding affinities for TIBO inhibitors of HIV-1 reverse transcriptase using Monte Carlo simulations in a linear response method.

Monte Carlo (MC) simulations in combination with a linear response approach were used to estimate the free energies of binding for a series of 12 TIBO nonnucleoside inhibitors of HIV-1 reverse transcriptase. Separate correlations were made for the R6 and S6 absolute conformations of the inhibitors, as well as for the analogous N6-monoprotonated species. Models based upon the neutral unbound inhibitors produced overall better fits to experimental values than did those using the protonated unbound inhibitors, with only slight differences between the neutral R6 and S6 cases. The best results were obtained with a three-parameter linear response equation containing van der Waals (alpha), electrostatic (beta), and solvent accessible surface area (SASA, gamma) terms. The averaged (R6 and S6) rms error was approximately 0.88 kcal/mol for the observed range of 4.06 kcal/mol in inhibitor activities. The averaged values of alpha, beta, and gamma were -0.150, 0.114, and 0. 0286, respectively. Omission of the alpha term gave beta 0.152 and gamma 0.022 with a rms of 0.92. The unweighted van der Waals components were found to be highly attractive but failed to correlate well across the series of inhibitors. Contrastingly, while the electrostatic components are all repulsive, they show a direct correlation with inhibitor activity as measured by DeltaGbinding. The role of gamma is primarily to produce an overall negative binding energy, and it can effectively be replaced with a negative constant. During the MC simulations of the unbound solvated inhibitors, the R6 and S6 absolute conformations do not interconvert due to the formation of a favorable hydrogen bond to solvent. In the complex, however, interconversion of these conformations of the inhibitor is observed during the course of the simulations, a phenomenon which is apparently not observed in the crystalline state of the complex. Hydrogen bonding of the inhibitor to the backbone NH of K101 and the lack of such an interaction with the C=O of K101 or with solvent correlate with enhanced activity, as does the ability to assume a number of different orientations of the inhibitor dimethylallyl moiety with respect to residues Y181 and Y188 while retaining contact with W229. Overall, the use of a combination of MC simulation with a linear response method shows promise as a relatively rapid means of estimating inhibitor activities. This approach should be useful in the preliminary evaluation of potential modifications to known inhibitors to enhance activity.

Anti-HIV Agents↗
PubMed2003

Particle aggregation with simultaneous surface growth.

Particle aggregation with simultaneous surface growth was modeled using a dynamic Monte Carlo method. The Monte Carlo algorithm begins in the particle inception zone and constructs aggregates via ensemble-averaged collisions between spheres and deposition of gaseous species on the sphere surfaces. Simulations were conducted using four scenarios. The first, referred to as scenario 0, is used as a benchmark and simulates aggregation in the absence of surface growth. Scenario 1 forces all balls to grow at a uniform rate while scenario 2 only permits them to grow once they have collided and stuck to each other. The last one is a test scenario constructed to confirm conclusions drawn from scenarios 0-2. The transition between the coalescent and the fully developed fractal aggregation regimes is investigated using shape descriptors to quantify particle geometry. They are used to define the transition between the coalescent and fractal growth regimes. The simulations demonstrate that the morphology of aggregating particles is intimately related to both the surface deposition and particle nucleation rates.

Journal Article↗
PubMed1997

Large hierarchical Bayesian analysis of multivariate survival data.

Failure times that are grouped according to shared environments arise commonly in statistical practice. That is, multiple responses may be observed for each of many units. For instance, the units might be patients or centers in a clinical trial setting. Bayesian hierarchical models are appropriate for data analysis in this context. At the first stage of the model, survival times can be modelled via the Cox partial likelihood, using a justification due to Kalbfleisch (1978, Journal of the Royal Statistical Society, Series B 40, 214-221). Thus, questionable parametric assumptions are avoided. Conventional wisdom dictates that it is comparatively safe to make parametric assumptions at subsequent stages. Thus, unit-specific parameters are modelled parametrically. The posterior distribution of parameters given observed data is examined using Markov chain Monte Carlo methods. Specifically, the hybrid Monte Carlo method, as described by Neal (1993a, in Advances in Neural Information Processing 5, 475-482; 1993b, Probabilistic inference using Markov chain Monte Carlo methods), is utilized.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed2006

A Monte Carlo sampling method of amino acid sequences adaptable to given main-chain atoms in the proteins.

We have developed a computational method of protein design to detect amino acid sequences that are adaptable to given main-chain coordinates of a protein. In this method, the selection of amino acid types employs a Metropolis Monte Carlo method with a scoring function in conjunction with the approximation of free energies computed from 3D structures. To compute the scoring function, a side-chain prediction using another Metropolis Monte Carlo method was performed to select structurally suitable side-chain conformations from a side-chain library. In total, two layers of Monte Carlo procedures were performed, first to select amino acid types (1st layer Monte Carlo) and then to predict side-chain conformations (2nd layers Monte Carlo). We applied this method to sequence design for the entire sequence on the SH3 domain, Protein G, and BPTI. The predicted sequences were similar to those of the wild-type proteins. We compared the results of the predictions with and without the 2nd layer Monte Carlo method. The results revealed that the two-layer Monte Carlo method produced better sequence similarity to the wild-type proteins than the one-layer method. Finally, we applied this method to neuraminidase of influenza virus. The results were consistent with the sequences identified from the isolated viruses.

Amino Acid Sequence↗
PubMed2006

Monte Carlo calculations for absolute dosimetry to determine machine outputs for proton therapy fields.

The prescribed dose in radiation therapy has to be converted into machine monitor units for patient treatment. This is done routinely for each spread-out Bragg peak (SOBP) field either by calibration measurements, by using analytical algorithms or by relying on empirical data. At the Northeast Proton Therapy Center, a monitor unit corresponds to a fixed amount of charge collected in a segmented transmission ionization chamber inside the treatment head. The goal of this work was to use a detailed Monte Carlo model of the treatment head to calculate the dose delivered to the patient as a function of ionization chamber reading, i.e. to yield absolute dose in patients in terms of machine monitor units. The results show excellent agreement with measurements. For 50 SOBP fields considered in this study, the mean absolute difference between the experimental and the calculated value is 1.5%, where approximately 50% of the fields agree within 1%. This is within the uncertainties of the data. The Monte Carlo method has advantages over analytical algorithms because it takes into account scattered and secondary radiation, does not rely on empirical parameters, and provides a tool to study the influence of parts of the treatment head on the ionization chamber reading. Compared to experimental methods the Monte Carlo method has the advantage of being able to verify the dose in the patient geometry.

Algorithms↗
PubMed2001

Monte Carlo simulation for MLC-based intensity-modulated radiotherapy.

This article describes photon beam Monte Carlo simulation for multi leaf collimator (MLC)-based intensity-modulated radiotherapy (IMRT). We present the general aspects of the Monte Carlo method for the non-Monte Carloist with an emphasis given to patient-specific radiotherapy application. Patient-specific application of the Monte Carlo method can be used for IMRT dose verification, inverse planning, and forward planning in conventional conformal radiotherapy. Because it is difficult to measure IMRT dose distributions in heterogeneous phantoms that approximate a patient, Monte Carlo methods can be used to verify IMRT dose distributions that are calculated using conventional methods. Furthermore, using Monte Carlo as the dose calculation method for inverse planning results in better-optimized treatment plans. We describe both aspects and present our recent results to illustrate the discussion. Finally, we present current issues related to clinical implementation of Monte Carlo dose calculation. Monte Carlo is the most recent, and most accurate, method of radiotherapy dose calculation. It is currently in the process of being implemented by various treatment planning vendors and will be available for clinical use in the immediate future.

Humans↗