Comparison and assessment of electron cross sections for Monte Carlo track structure codes.

The purpose of this study was to make an intercomparison and assessment of cross sections for electrons in water used in electron track structure codes. This study is intended to shed light on the extent to which the differences between the input data and physical and chemical assumptions influence the outcome in biophysical modeling of radiation effects. Ionization cross sections and spectra of secondary electrons were calculated by various theories. The analyses were carried out for water vapor cross sections, as these are more abundant and readily available. All suitable published experimental total ionization cross sections were fitted by an appropriate function and used for generation of electron tracks. Three sets of compiled data were used for comparison of total excitation cross sections and mean excitation energy. The tracks generated by a Monte Carlo track code, using various combinations of cross sections, were compared in terms of radial distributions of interactions and point kernels. The spectrum of secondary electrons emitted by the ionization process was found to be the factor that has the most influence on these quantities. A different set of cross sections for excitation and elastic scattering did not affect the electron track structure as much as did ionization cross sections. It is concluded that all codes, using different cross sections and in different phase, currently used for biophysical modeling exhibit close similarities for energy deposition in larger size targets while appreciable differences are observed in B-DNA-size targets. We recommend fitted functions to all available suitable experimental data for the total ionization and elastic cross sections. We conclude that most codes produce tracks in reasonable agreement with the macroscopic quantities such as total stopping power and total yield of strand breaks. However, we predict differences in frequencies of clustering in tracks from the different models.     

A Monte Carlo code for positive ion track simulation

An ion interaction model has been described for simulating positive ion tracks in a variety of media with the capability of interfacing with several secondary electron transport codes. Data are presented for single- and double-differential cross-sections, binding energies, probability density distribution for delocalisation parameters for conductors and tissue, branching ratios and ionisation efficiencies for water vapour and liquid water. Received: 20 September 1998 / Accepted in revised form: 15 February 1999     

Radiation shielding parameters of some antioxidants using Monte Carlo method

In this paper, radiation shielding parameters such as mass attenuation coefficients and half value layer (HVL) of some antioxidants are investigated using MCNPX (version 2.4.0). The validation of the generated MCNPX simulation geometry for antioxidant structures is provided by comparing the results with standard WinXcom data for radiation mass attenuation coefficients of antioxidants. Very good agreement between W?NXCOM and MCNPX was obtained. The results from the validated geometry were used to calculate the shielding parameters of different antioxidants. The radiation attenuation properties of each antioxidant were compared with each other. The results showed that, on average, the highest and the lowest radiation mass attenuation coefficients were observed on hesperidin and delphinidin chloride, respectively. It can be concluded that Monte Carlo simulation is a strong tool and an alternate method where experimental investigations are not possible and a standard simulation setup can be used in further studies for different biological structures. It can also be concluded that the obtained results from this study are very useful for radiology and radiotherapy applications where antioxidants are frequently used.     

Metropolis Monte Carlo calculations of DNA structure using internal coordinates and NMR distance restraints: An alternative method for generating a high-resolution solution structure

Summary A new method, a restrained Monte Carlo (rMC) calculation, is demonstrated for generating high-resolution structures of DNA oligonucleotides in solution from interproton distance restraints and bounds derived from complete relaxation matrix analysis of two-dimensional nuclear Overhauser effect (NOE) spectral peak intensities. As in the case of restrained molecular dynamics (rMD) refinement of structures, the experimental distance restraints and bounds are incorporated as a pseudo-energy term (or penalty function) into the mathematical expression for the molecular energy. However, the use of generalized helical parameters, rather than Cartesian coordinates, to define DNA conformation increases efficiency by decreasing by an order of magnitude the number of parameters needed to describe a conformation and by simplifying the potential energy profile. The Metropolis Monte Carlo method is employed to simulate an annealing process. The rMC method was applied to experimental 2D NOE data from the octamer duplex d(GTA-TAATG)·d(CATTATAC). Using starting structures from different locations in conformational space (e.g. A-DNA and B-DNA), the rMC calculations readily converged, with a root-mean-square deviation (RMSD) of <0.3 Å between structures generated using different protocols and starting structures. Theoretical 2D NOE peak intensities were calculated for the rMC-generated structures using the complete relaxation matrix program CORMA, enabling a comparison with experimental intensities via residual indices. Simulation of the vicinal proton coupling constants was carried out for the structures generated, enabling a comparison with the experimental deoxyribose ring coupling constants, which were not utilized in the structure determination in the case of the rMC simulations. Agreement with experimental 2D NOE and scalar coupling data was good in all cases. The rMC structures are quite similar to that refined by a traditional restrained MD approach (RMSD<0.5 Å) despite the different force fields used and despite the fact that MD refinement was conducted with additional restraints imposed on the endocyclic torsion angles of deoxyriboses. The computational time required for the rMC and rMD calculations is about the same. A comparison of structural parameters is made and some limitations of both methods are discussed with regard to the average nature of the experimental restraints used in the refinement.Abbreviations MC Monte Carlo - rMC restrained Monte Carlo - MD molecular dynamics - rMD restrained molecular dynamics - DG distance geometry - EM energy minimization - 2D NOE two-dimensional nuclear Overhauser effect - DQF-COSY double-quantum-filtered correlation spectroscopy - RMSD root-mean-square deviation To whom correspondence should be addressed.     

Modeling of primary photosynthetic processes using the kinetic Monte Carlo method

Processes that occur in the ensemble of photosynthetic electron transport systems have been modeled using a kinetic Monte Carlo method. The size of a simulated ensemble (3–5 million elementary photosynthetic chains) corresponds to the number of photosynthetic reaction centers in a plant cell. The method enables one to modify the structure of a model system according to different concepts of the organization of processes in a photosynthetic membrane. Using this model, the experimental kinetics of the chlorophyll fluorescence induction associated with the Photosystem II and the redox transformations of a photoactive pigment of the Photosystem I have been successfully reproduced. The model was verified by comparing the calculated fluorescence induction curves to experimental curves, obtained in the presence of various photosynthesis inhibitors and under temperature inactivation of the Photosystem II donor side.     

A Monte Carlo method for generating structures of short single-stranded DNA sequences

A Monte Carlo method has been developed for generating the conformations of short single-stranded DNAs from arbitrary starting states. The chain conformers are constructed from energetically favorable arrangements of the constituent mononucleotides. Minimum energy states of individual dinucleotide monophosphate molecules are identified using a torsion angle minimizer. The glycosyl and acyclic backbone torsions of the dimers are allowed to vary, while the sugar rings are held fixed in one of the two preferred puckered forms. A total of 108 conformationally distinct states per dimer are considered in this first stage of minimization. The torsion angles within 5 kcal/mole of the global minimum in the resulting optimized states are then allowed to vary by ±10° in an effort to estimate the breadth of the different local minima. The energies of a total of 2187 (3⁷) angle combinations are examined per local conformational minimum. Finally, the energies of all dinucleotide conformers are scaled so that the populations of differently puckered sugar rings in the theoretical sample match those found in nmr solution studies. This last step is necessitated by limitations in the theoretical methods to predict DNA sugar puckering accurately. The conformer populations of the individual acyclic torsion angles in the composite dimer ensembles are found to be in good agreement with the distributions of backbone conformations deduced from nmr coupling constants and the frequencies of glycosyl conformations in x-ray crystal structures, suggesting that the low energy states are reasonable. The low energy dimer forms (consisting of 150–325 conformational states per dimer step) are next used as variables in a Monte Carlo algorithm, which generates the conformations of single-stranded d(CX_nG) chains, where X = A, T and n = 3, 4, 5. The oligonucleotides are built sequentially from the 5′ end of the chain using random numbers to select the conformations of overlapping dimer units. The simulations are very fast, involving a total of 10⁶ conformations per chain sequence. The potential errors in the buildup procedure are minimized by taking advantage of known rotational interdependences in the sugar–phosphate backbone. The distributions of oligonucleotide conformations are examined in terms of the magnitudes, positions, and orientations of the end-to-end vectors of the chains. The differences in overall flexibility and extension of the oligomers are discussed in terms of the conformations of the constituent dinucleotide steps, while the general methodology is discussed and compared with other nucleic acid model building techniques. © 1993 John Wiley & Sons, Inc.     

Monte Carlo implementation of supercoiled double-stranded DNA

Metropolis Monte Carlo simulation is used to investigate the elasticity of torsionally stressed double-stranded DNA, in which twist and supercoiling are incorporated as a natural result of base-stacking interaction and backbone bending constrained by hydrogen bonds formed between DNA complementary nucleotide bases. Three evident regimes are found in extension versus torsion and force versus extension plots: a low-force regime in which over- and underwound molecules behave similarly under stretching; an intermediate-force regime in which chirality appears for negatively and positively supercoiled DNA and extension of underwound molecule is insensitive to the supercoiling degree of the polymer; and a large-force regime in which plectonemic DNA is fully converted to extended DNA and supercoiled DNA behaves quite like a torsionless molecule. The striking coincidence between theoretic calculations and recent experimental measurement of torsionally stretched DNA (Strick et al., Science. 271:1835, 1996; Biophys. J. 74:2016, 1998) strongly suggests that the interplay between base-stacking interaction and permanent hydrogen-bond constraint takes an important role in understanding the novel properties of elasticity of supercoiled DNA polymer.     

Haplotype association analyses in resources of mixed structure using Monte Carlo testing

Background  

Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions is the logical next step, including regional haplotype studies to identify risk haplotypes upon which the underlying critical variants lie. Pedigrees ascertained for disease can be powerful for genetic analysis due to the cases being enriched for genetic disease. Here we present a Monte Carlo based method to perform haplotype association analysis. Our method, hapMC, allows for the analysis of full-length and sub-haplotypes, including imputation of missing data, in resources of nuclear families, general pedigrees, case-control data or mixtures thereof. Both traditional association statistics and transmission/disequilibrium statistics can be performed. The method includes a phasing algorithm that can be used in large pedigrees and optional use of pseudocontrols.     

Monte Carlo method for bioluminescence tomography

Bioluminescence imaging plays an important role in the areas of cancer biology, cell biology, gene therapy, and so on. The 2D planar bioluminescent imaging has been transformed into a 3D framework by bioluminescence tomography (BLT) that enables bioluminescent source reconstruction in a mouse using a modality fusion approach. To solve this BLT problem, a geometrical model of the mouse is usually built from a CT/micro-CT/micro-MRI scan, which facilitates the assignment of optical parameters to various anatomical regions in the model. This optical model is then used to facilitate BLT. The forward model is based on Monte Carlo simulation to calculate the diffuse light flux on the surface of the mouse. The forward model data are used to define the imaging system and perform the BLT reconstruction. In this paper, we report the reconstruction of sources inside a heterogeneous highly scattering physical phantom to demonstrate the feasibility of this Monte Carlo based BLT method.     

A study of deleterious gene structure in plants using Markov chain Monte Carlo

The characteristics of deleterious genes have been of great interest in both theory and practice in genetics. Because of the complex genetic mechanism of these deleterious genes, most current studies try to estimate the overall magnitude of mortality effects on a population, which is characterized classically by the number of lethal equivalents. This number is a combination of several parameters, each of which has a distinct biological effect on genetic mortality. In conservation and breeding programs, it is important to be able to distinguish among different combinations of these parameters that lead to the same number of lethal equivalents, such as a large number of mildly deleterious genes or a few lethal genes, The ability to distinguish such parameter combinations requires more than one generation of mating. We propose a model for survival data from a two-generation mating experiment on the plant species Brassica rapa, and we enable inference with Markov chain Monte Carlo. This computational strategy is effective because a vast amount of missing genotype information must be accounted for. In addition to the lethal equivalents, the two-generation data provide separate information on the average intensity of mortality and the average number of deleterious genes carried by an individual. In our Markov chain Monte Carlo algorithm, we use a vector proposal distribution to overcome inefficiency of a single-site Gibbs sampler. Information about environmental effects is obtained from an outcrossing experiment conducted in parallel with the two-generation mating experiments.     

Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo Method

An improved Bayesian method is presented for estimating phylogenetic trees using DNA sequence data. The birth-death process with species sampling is used to specify the prior distribution of phylogenies and ancestral speciation times, and the posterior probabilities of phylogenies are used to estimate the maximum posterior probability (MAP) tree. Monte Carlo integration is used to integrate over the ancestral speciation times for particular trees. A Markov Chain Monte Carlo method is used to generate the set of trees with the highest posterior probabilities. Methods are described for an empirical Bayesian analysis, in which estimates of the speciation and extinction rates are used in calculating the posterior probabilities, and a hierarchical Bayesian analysis, in which these parameters are removed from the model by an additional integration. The Markov Chain Monte Carlo method avoids the requirement of our earlier method for calculating MAP trees to sum over all possible topologies (which limited the number of taxa in an analysis to about five). The methods are applied to analyze DNA sequences for nine species of primates, and the MAP tree, which is identical to a maximum-likelihood estimate of topology, has a probability of approximately 95%.      

A multiple-start Monte Carlo docking method.

We present a method to search for possible binding modes of molecular fragments at a specific site of a potential drug target of known structure. Our method is based on a Monte Carlo (MC) algorithm applied to the translational and rotational degrees of freedom of the probe fragment. Starting from a randomly generated initial configuration, favorable binding modes are generated using a two-step process. An MC run is first performed in which the energy in the Metropolis algorithm is substituted by a score function that measures the average distance of the probe to the target surface. This has the effect of making buried probes move toward the target surface and also allows enhanced sampling of deep pockets. In a second MC run, a pairwise atom potential function is used, and the temperature parameter is slowly lowered during the run (Simulated Annealing). We repeat this procedure starting from a large number of different randomly generated initial configurations in order to find all energetically favorable docking modes in a specified region around the target. We test this method using two inhibitor-receptor systems: Streptomyces griseus proteinase B in complex with the third domain of the ovomucoid inhibitor from turkey, and dihydrofolate reductase from E. coli in complex with methotrexate. The method could consistently reproduce the complex found in the crystal structure searching from random initial positions in cubes ranging from 25 A to 50 A about the binding site. In the case of SGPB, we were also successful in docking to the native structure. In addition, we were successful in docking small probes in a search that included the entire protein surface.     

Studies on evolutionary and selective properties of hypercycles using a Monte Carlo method

Summary The most relevant properties of hypercycles were previously studied mainly from a theoretical point of view. We have developed a Monte Carlo method simulating hypercyclic organization to obtain information about the dynamics of this prebiotic organization. Nucleation, growth, and selective properties have been tested and the results obtained are in good agreement with those of the theoretical predictions. The influence of hypercyclic organization of the error threshold has also been studied. As a consequence of the emergence of a hypercycle, the value of this threshold decreases. The amount of this decrease depends on the population size. Moreover, for some interval of quality factor values, either the hypercycle organization or an error catastrophe can be produced, depending on the initial conditions. The influence of these phenomena on both the dynamic behavior and evolutionary advantages of the hypercycle, as well as their decisive roles on genome size, are discussed.Presented at the FEBS Symposium on Genome Organization and Evolution, held in Crete, Greece, September 1–5, 1986     

Time accelerated Monte Carlo simulations of biological networks using the binomial tau-leap method

SUMMARY: Developing a quantitative understanding of intracellular networks requires simulations and computational analyses. However, traditional differential equation modeling tools are often inadequate due to the stochasticity of intracellular reaction networks that can potentially influence the phenotypic characteristics. Unfortunately, stochastic simulations are computationally too intense for most biological systems. Herein, we have utilized the recently developed binomial tau-leap method to carry out stochastic simulations of the epidermal growth factor receptor induced mitogen activated protein kinase cascade. Results indicate that the binomial tau-leap method is computationally 100-1000 times more efficient than the exact stochastic simulation algorithm of Gillespie. Furthermore, the binomial tau-leap method avoids negative populations and accurately captures the species populations along with their fluctuations despite the large difference in their size. AVAILABILITY: http://www.dion.che.udel.edu/multiscale/Introduction.html. Fortran 90 code available for academic use by email. SUPPLEMENTARY INFORMATION: Details about the binomial tau-leap algorithm, software and a manual are available at the above website.     

Monte Carlo simulations and measurement of DNA damage from x-ray-triggered Auger cascades in iododeoxyuridine (IUdR)

We investigated the DNA damage from Auger electrons emitted from incorporated stable iodine (¹²⁷I), following photoelectric absorption of external x-rays. The effectiveness of the Auger electrons in producing DNA double-strand breaks (DSB) was determined theoretically, using Monte Carlo simulations of the radiation physics and chemistry, and was shown to be in reasonable agreement with DNA damage measured using the comet assay. The DSB yields were measured in CHO cells for ⁶⁰Co (as a non-Auger-promoting radiation) and for tungsten-filtered 100 kVp x-rays capable of producing Auger electron emission. The theoretical study showed that on average, 2.5 Auger electrons were emitted for N-shell orbital vacancies and up to 10 Auger electrons were emitted from L₁-shell vacancies. These Auger bursts produced approximately 0.03 DSB per N-shell vacancy and 0.3 DSB per K-shell or L-shell vacancy. The calculated yield of DSB from Auger cascades per unit dose (1 Gy) in water was approximately 1.7 for tungsten-filtered 100 kVp x-rays, assuming 20% IUdR substitution of thymidine. The comet assay yielded an experimental value of 3.6±1.6 per 1 Gy for the same conditions. The Monte Carlo simulations also demonstrated a high complexity of DSB produced by Auger cascades with virtually all DSB from inner shell orbitals (i.e. K, L shells) accompanied by compounded strand breakage and base damage, indicating a difficult lesion to repair. This finding agrees well with comet assay results of DNA repair, where an increase in the DSB yield in IUdR-sensitized cells was shown to persist after a time of 24 h. We conclude that Auger cascades in iodine produce a modest increase in the number of initial strand breaks of the order of 10% but the complex nature of these DSB makes them very difficult to repair or potentially prone to misrepair. The accentuated DNA damage may have major consequences for cell survival and may be exploitable in kilovoltage photon activation therapy (PAT) of tumors sensitized with iodine. Received: 23 October 2000 / Accepted: 26 March 2001     

Monte Carlo simulation of myoglobin primary to helical structure

A Monte Carlo simulation is presented of the formation of the individual helices of myoglobin from their primary to their helical structures. A simplified model in which each amino acid residue is replaced by a single interaction center is used. The small helices formed are in good agreement with experiment, while the larger helices are moderately well reproduced.     

An evolutionary Monte Carlo algorithm for predicting DNA hybridization

Many DNA-based technologies, such as DNA computing, DNA nanoassembly and DNA biochips, rely on DNA hybridization reactions. Previous hybridization models have focused on macroscopic reactions between two DNA strands at the sequence level. Here, we propose a novel population-based Monte Carlo algorithm that simulates a microscopic model of reacting DNA molecules. The algorithm uses two essential thermodynamic quantities of DNA molecules: the binding energy of bound DNA strands and the entropy of unbound strands. Using this evolutionary Monte Carlo method, we obtain a minimum free energy configuration in the equilibrium state. We applied this method to a logical reasoning problem and compared the simulation results with the experimental results of the wet-lab DNA experiments performed subsequently. Our simulation predicted the experimental results quantitatively.