When Can Clades Be Potentially Resolved with Morphology?

Morphology-based phylogenetic analyses are the only option for reconstructing relationships among extinct lineages, but often find support for conflicting hypotheses of relationships. The resulting lack of phylogenetic resolution is generally explained in terms of data quality and methodological issues, such as character selection. A previous suggestion is that sampling ancestral morphotaxa or sampling multiple taxa descended from a long-lived, unchanging lineage can also yield clades which have no opportunity to share synapomorphies. This lack of character information leads to a lack of ‘intrinsic’ resolution, an issue that cannot be solved with additional morphological data. It is unclear how often we should expect clades to be intrinsically resolvable in realistic circumstances, as intrinsic resolution must increase as taxonomic sampling decreases. Using branching simulations, I quantify intrinsic resolution across several models of morphological differentiation and taxonomic sampling. Intrinsically unresolvable clades are found to be relatively frequent in simulations of both extinct and living taxa under realistic sampling scenarios, implying that intrinsic resolution is an issue for morphology-based analyses of phylogeny. Simulations which vary the rates of sampling and differentiation were tested for their agreement to observed distributions of durations from well-sampled fossil records and also having high intrinsic resolution. This combination only occurs in those datasets when differentiation and sampling rates are both unrealistically high relative to branching and extinction rates. Thus, the poor phylogenetic resolution occasionally observed in morphological phylogenetics may result from a lack of intrinsic resolvability within groups.     

Monte Carlo studies of a model for lipid-gramicidin A bilayers.

This paper presents results of Monte Carlo simulations of a full bilayer of 200 lipid chains and one gramicidin A dimer. Simulations are described for systems with lipid chains of 14, 16, and 18 carbons, respectively. Using accepted potential functions to calculate interactions between all non-hydrogen atoms a Monte Carlo configuration sampling is generated from which order parameter profiles are calculated and specific configurations are displayed. Results are compared with experimental data for lipid-gramicidin bilayers.     

跨膜蛋白CD3ε与磷脂酰肌醇二磷酸在脂筏域体系中蛋白质-脂质相互作用的粗粒化模拟研究

细胞膜局部区域可形成富含饱和脂质、胆固醇、鞘脂的脂筏域作为其信号转导调控平台。传统实验手段在研究脂筏及其功能时受到系统复杂度高及区域结构瞬时性强等制约。近年来,分子动力学模拟技术为细胞膜的组织原则提供了重要的理论支撑,从简单的单一组分模型到多组分系统转变,最终形成了越来越多的细胞膜仿真模型。其中,粗粒化模拟由于其简化模型,可大副拓展模拟体系的复杂程度与模拟时间,在细胞膜以及蛋白质-脂质相互作用相关研究中得到了广泛应用。本文采用MARTINI粗粒化力场模拟,构建了一种含有阴离子脂质磷脂酰肌醇二磷酸(phosphatidylinositol diphosphate, PIP2)的混合膜体系。模拟结果表明,该体系在适当温度及饱和度条件下,能自发分层形成脂筏域;膜厚度、膜组分分布、膜组分流动性等多种参数均表明,脂筏结构形成且符合其结构特征;少量PIP2添加不影响分层特性且PIP2对脂筏具有显著亲和性。此外,利用该模型以跨膜信号蛋白CD3ε为例研究了脂筏域体系中蛋白质-脂质相互作用。结果表明,PIP2-CD3ε胞内区相互作用可能是脂筏招募CD3ε的驱动力,且该过程可受钙离子调控。本工作体现了粗粒化模拟在仿真膜相关研究中的巨大优势及良好应用前景。     

Applications of computational fluid dynamics (CFD) in the modelling and design of ventilation systems in the agricultural industry: a review

The application of computational fluid dynamics (CFD) in the agricultural industry is becoming ever more important. Over the years, the versatility, accuracy and user-friendliness offered by CFD has led to its increased take-up by the agricultural engineering community. Now CFD is regularly employed to solve environmental problems of greenhouses and animal production facilities. However, due to a combination of increased computer efficacy and advanced numerical techniques, the realism of these simulations has only been enhanced in recent years. This study provides a state-of-the-art review of CFD, its current applications in the design of ventilation systems for agricultural production systems, and the outstanding challenging issues that confront CFD modellers. The current status of greenhouse CFD modelling was found to be at a higher standard than that of animal housing, owing to the incorporation of user-defined routines that simulate crop biological responses as a function of local environmental conditions. Nevertheless, the most recent animal housing simulations have addressed this issue and in turn have become more physically realistic.     

Hybrid Monte Carlo simulations theory and initial comparison with molecular dynamics

A formal partially dynamical approach to ergodic sampling, hybrid Monte Carlo, has been adapted for the first time from its proven application in quantum chromodynamics to realistic molecular systems. A series of simulations of pancreatic trypsin inhibitor were run using temperature-rescaled molecular dynamics and hybrid Monte Carlo. It was found that simulations run using hybrid Monte Carlo equilibrated an order of magnitude faster than those run using temperature-rescaled molecular dynamics. Certain aspects of improved performance obtained using hybrid Monte Carlo are probably due to the increased efficiency with which this algorithm explores phase space. To discuss this we introduce the notion of “trajectory stiffness”. © 1993 John Wiley & Sons, Inc.     

Multigrid block preconditioning for a coupled system of partial differential equations modeling the electrical activity in the heart

The electrical activity of the heart may be modeled with a system of partial differential equations (PDEs) known as the bidomain model. Computer simulations based on these equations may become a helpful tool to understand the relationship between changes in the electrical field and various heart diseases. Because of the rapid variations in the electrical field, sufficiently accurate simulations require a fine-scale discretization of the equations. For realistic geometries this leads to a large number of grid points and consequently large linear systems to be solved for each time step. In this paper, we present a fully coupled discretization of the bidomain model, leading to a block structured linear system. We take advantage of the block structure to construct an efficient preconditioner for the linear system, by combining multigrid with an operator splitting technique.     

Multigrid Block Preconditioning for a Coupled System of Partial Differential Equations Modeling the Electrical Activity in the Heart

The electrical activity of the heart may be modeled with a system of partial differential equations (PDEs) known as the bidomain model. Computer simulations based on these equations may become a helpful tool to understand the relationship between changes in the electrical field and various heart diseases. Because of the rapid variations in the electrical field, sufficiently accurate simulations require a fine-scale discretization of the equations. For realistic geometries this leads to a large number of grid points and consequently large linear systems to be solved for each time step. In this paper, we present a fully coupled discretization of the bidomain model, leading to a block structured linear system. We take advantage of the block structure to construct an efficient preconditioner for the linear system, by combining multigrid with an operator splitting technique.     

Systolic Loop Methods for Molecular Dynamics Simulation,Generalised for Macromolecules

Abstract

Systolic loop programs have been shown to be very efficient for molecular dynamics simulations of liquid systems on distributed memory parallel computers. The original methods address the case where the number of molecules simulated exceeds the number of processors used. Simulations of large flexible molecules often do not meet this condition, requiring the three- and four-body terms used to model chemical bonds within a molecule to be distributed over several processors. This paper discusses how the systolic loop methods may be generalised to accommodate such systems, and describes the implementation of a computer program for simulation of protein dynamics. Performance figures are given for this program running typical simulations on a Meiko Computing Surface using different number of processors.     

Validation of the 53A6 GROMOS force field

The quality of biomolecular dynamics simulations relies critically on the force field that is used to describe the interactions between particles in the system. Force fields, which are generally parameterized using experimental data on small molecules, can only prove themselves in realistic simulations of relevant biomolecular systems. In this work, we begin the validation of the new 53A6 GROMOS parameter set by examining three test cases. Simulations of the well-studied 129 residue protein hen egg-white lysozyme, of the DNA dodecamer d(CGCGAATTCGCG)₂, and a proteinogenic ³-dodecapeptide were performed and analysed. It was found that the new parameter set performs as well as the previous parameter sets in terms of protein (45A3) and DNA (45A4) stability and that it is better at describing the folding–unfolding balance of the peptide. The latter is a property that is directly associated with the free enthalpy of hydration, to which the 53A6 parameter set was parameterized.     

Highly sampled tetranucleotide and tetraloop motifs enable evaluation of common RNA force fields

Recent modifications and improvements to standard nucleic acid force fields have attempted to fix problems and issues that have been observed as longer timescale simulations have become routine. Although previous work has shown the ability to fold the UUCG stem–loop structure, until now no group has attempted to quantify the performance of current force fields using highly converged structural populations of the tetraloop conformational ensemble. In this study, we report the use of multiple independent sets of multidimensional replica exchange molecular dynamics (M-REMD) simulations with different initial conditions to generate well-converged conformational ensembles for the tetranucleotides r(GACC) and r(CCCC), as well as the larger UUCG tetraloop motif. By generating what is to our knowledge the most complete RNA structure ensembles reported to date for these systems, we remove the coupling between force field errors and errors due to incomplete sampling, providing a comprehensive comparison between current top-performing MD force fields for RNA. Of the RNA force fields tested in this study, none demonstrate the ability to correctly identify the most thermodynamically stable structure for all three systems. We discuss the deficiencies present in each potential function and suggest areas where improvements can be made. The results imply that although “short” (nsec-μsec timescale) simulations may stay close to their respective experimental structures and may well reproduce experimental observables, inevitably the current force fields will populate alternative incorrect structures that are more stable than those observed via experiment.     

A MODEL APPROACH FOR SIZE-SELECTIVE COMPETITION OF MARINE PHYTOPLANKTON FOR FLUCTUATING NITRATE AND AMMONIUM1

Phytoplankton size-selective competition for fluctuating nutrients was studied with the use of a numerical model, which describes nitrate and ammonium uptake, nitrate reduction to ammonium, and growth as a function of cell she under fluctuating nitrogen limitation. The only size-dependent parameter in the model was the cell nutrient quota. Related to this, the cell surface area per biomass was negatively correlated to cell volume, and the vacuole volume per biomass ratio was positively correlated to cell volume. Simulations showed an inverse correlation between the maximum specific growth rate and cell size under steady-state conditions. With nitrate as the limiting nitrogen source, nitrogen quotas were always higher than with ammonium at the same specific growth rate. Net passive transport of ammonium due to unspecific diffusion of ammonia across the plasma membrane decreased the affinity for ammonium, whereas the affinity for nitrate was not influenced. Transient state-specific ammonium uptake was not dependent on cell size. However, small algae always have the highest specific growth rate in ammonium-controlled systems according to our model. Transient state nitrate uptake rate was positively correlated to cell size because larger algae have a higher vacuole volume per biomass, in which nitrate can be stored. Despite their lower maximum growth rate, larger algae became dominant during simulations under fluctuating nitrate supply when amplitude of and the period between nitrate pulses were high enough. Results from model simulations were qualitatively validated by earlier observations that large diatoms become dominant under fluctuating conditions when nitrate is the main nitrogen source.     

Distributed state simulation of endogenous processes in biological wastewater treatment

Distributed state-type simulations (based on modeling of individual bacteria as they move through a reactor system) predicted a greater sensitivity of enhanced biological phosphorus removal (EBPR) performance to endogenous degradation than did conventional, "lumped"-type simulations (based on average biomass compositions). Recent research has indicated that the variable hydraulic residence times experienced by individual microbial storage product accumulating bacteria in systems with completely mixed reactors tend to produce populations with diverse microbial storage product contents (distributed states). Endogenous degradation in EBPR systems is of particular interest because the polyphosphate accumulating organisms (PAOs) responsible for EBPR rely on the accumulation of three different storage products that may be endogenously degraded. Simulations indicated that as endogenous degradation rates of microbial storage products were increased, EBPR performance decreased more rapidly according to the distributed approach than according to the lumped approach. State profile analysis demonstrated that as these rates increased, the population fraction with depleted storage products also increased, and this tended to increase the error in calculated biokinetic rates by the lumped approach. Simulations based on recently reported endogenous rate coefficients also suggested large differences between distributed and lumped predictions of EBPR performance. These results demonstrated that endogenous decay processes may play a more important role in EBPR than predicted by the lumped approach. This suggests a need for further research to determine endogenous process rates, and for incorporation of this information to distributed-type simulators, as this should lead to improved accuracy of EBPR simulations.     

Assessing equilibration and convergence in biomolecular simulations

If molecular dynamics simulations are used to characterize the folding of peptides or proteins, a wide range of conformational states needs to be sampled. This study reports an analysis of peptide simulations to identify the best methods for assessing equilibration and sampling in these systems where there is significant conformational disorder. Four trajectories of a beta peptide in methanol and four trajectories of an alpha peptide in water, each of 5 ns in length, have been studied. Comparisons have also been made with two 50-ns trajectories of the beta peptide in methanol. The convergence rates of quantities that probe both the extent of conformational sampling and the local dynamical properties have been characterized. These include the numbers of hydrogen bonds populated, clusters identified, and main chain torsion angle transitions in the trajectories. The relative equilibrium rates of different quantities are found to vary significantly between the two systems studied reflecting both the differences in peptide primary structure and the different solvents used. A cluster analysis of the simulation trajectories is identified as a very effective method for judging the convergence of the simulations. This is particularly the case if the analysis includes a comparison of multiple trajectories calculated for the same system from different starting structures.     

Multiscale simulations of protein and membrane systems

Classical multiscale simulations are perfectly suited to investigate biological soft matter systems. Owing to the bridging between microscopically realistic and lower-resolution models or the integration of a hierarchy of subsystems, one gets access to biologically relevant system sizes and timescales. In recent years, increasingly complex systems and processes have come into focus such as multidomain proteins, phase separation processes in biopolymer solutions, multicomponent biomembranes, or multiprotein complexes up to entire viruses. The review shows factors that have contributed to this progress — from improved models to machine-learning-based analysis and scale-bridging methods.     

Molecular dynamics simulations of mixed micelles modeling human bile

Extensive molecular dynamics (MD) simulations of binary systems of phospholipids and bile salts, a model for human bile, have been performed. Recent progress in hardware and software development allows simulation of the spontaneous aggregation of the constituents into small mixed micelles, in agreement with experimental observations. The MD simulations reveal the structure of these micelles at atomic detail. The phospholipids are packed radially with their headgroups at the surface and the hydrophobic tails pointing toward the micellar center. The bile salts act as wedges between the phospholipid headgroups, with their hydrophilic sides exposed to the aqueous environment. The structure of the micelles strongly resembles the previously proposed radial shell model. Simulations including small fractions of cholesterol reveal how cholesterol is solubilized inside these mixed micelles without changing their overall structure.     

Protein assembly and crowding simulations

Proteins encounter frequent molecular interactions in biological environments. Computer simulations have become an increasingly important tool in providing mechanistic insights into how such interactions in vivo relate to their biological function. The review here focuses on simulations describing protein assembly and molecular crowding effects as two important aspects that are distinguished mainly by how specific and long-lived protein contacts are. On the topic of crowding, recent simulations have provided new insights into how crowding affects protein folding and stability, modulates enzyme activity, and affects diffusive properties. Recent studies of assembly processes focus on assembly pathways, especially for virus capsids, amyloid aggregation pathways, and the role of multivalent interactions leading to phase separation. Also, discussed are technical challenges in achieving increasingly realistic simulations of interactions in cellular environments.     

Emergence of scale-free leadership structure in social recommender systems

The study of the organization of social networks is important for the understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a "good get richer" mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems.     

Estimates of the mortality and the duration of the trans-Atlantic migration of European eel Anguilla anguilla leptocephali using a particle tracking model

Using Lagrangian simulations, based on circulation models over three different hydroclimatic periods in the last 45 years in the North Atlantic Ocean, the trans-Atlantic migration of the European eel Anguilla anguilla leptocephali was simulated via the passive drift of particles released in the spawning area. Three different behaviours were modelled: drifting at fixed depth, undergoing a vertical migration or choosing the fastest currents. Simulations included mortality hypotheses to estimate a realistic mean migration duration and relative survival of A. anguilla larvae. The mean migration duration was estimated as 21 months and the mortality rate as 3·8 per year, i.e . < 0·2% of A. anguilla larvae may typically survive the trans-Atlantic migration.     

A computer based model for realistic simulations of neural networks

The use of computer simulations as a neurophysiological tool creates new possibilities to understand complex systems and to test whether a given model can explain experimental findings. Simulations, however, require a detailed specification of the model, including the nerve cell action potential and synaptic transmission. We describe a neuron model of intermediate complexity, with a small number of compartments representing the soma and the dendritic tree, and equipped with Na⁺, K⁺, Ca²⁺, and Ca²⁺ dependent K⁺ channels. Conductance changes in the different compartments are used to model conventional excitatory and inhibitory synaptic interactions. Voltage dependent NMDA-receptor channels are also included, and influence both the electrical conductance and the inflow of Ca²⁺ ions. This neuron model has been designed for the analysis of neural networks and specifically for the simulation of the network generating locomotion in a simple vertebrate, the lamprey. By assigning experimentally established properties to the simulated cells and their synapses, it has been possible to verify the sufficiency of these properties to account for a number of experimental findings of the network in operation. The model is, however, sufficiently general to be useful for realistic simulation also of other neural systems.