Acceptable protein and solvent behavior in primary hydration shell simulations of hen lysozyme

The "primary hydration shell" method in molecular dynamics simulations uses a two- to three-layer thick shell of explicitly represented water molecules as the solvent around the protein of interest. We show that despite its simplicity, this computationally cheap model is capable of predicting acceptable water and protein behavior using the CHARMM22/CMAP potential function. For protein dynamics, comparisons are made with Lipari-Szabo order parameters. These have been derived from NMR relaxation parameters for pico-nano second motions of the NH groups in the main-chain and NH(2) groups in Asn/Gln side chains in hen lysozyme. It is also shown that an even simpler, and therefore faster, water-shell model leads to results in similarly good agreement with experiments, and also compared with simulations using a full box of water with periodic boundary conditions or with an implicit solvation model. Thus, the primary hydration shell method should be useful in making larger systems accessible to extensive simulations.     

Flocculation and its effect on the vertical transport of fine-grained sediments

Recent experimental and theoretical work on flocculation and settling speeds of flocs is reviewed. On the basis of this work, an accurate and computationally efficient model of the aggregation and disaggregation of fine-grained sediments is proposed. This model is then used to predict flocculation times and steady-state floc sizes for a wide range of environmental conditions. The predicted flocculation times are smaller, sometimes by as much as two orders of magnitude, than those predicted by mono-disperse theory. The model is also used to show that the disaggregation of flocs due to increased shear near the sediment-water interface may be a possible mechanism for the increased concentrations often observed near this interface.     

ReformAlign: improved multiple sequence alignments using a profile-based meta-alignment approach

Background

Obtaining an accurate sequence alignment is fundamental for consistently analyzing biological data. Although this problem may be efficiently solved when only two sequences are considered, the exact inference of the optimal alignment easily gets computationally intractable for the multiple sequence alignment case. To cope with the high computational expenses, approximate heuristic methods have been proposed that address the problem indirectly by progressively aligning the sequences in pairs according to their relatedness. These methods however are not flexible to change the alignment of an already aligned group of sequences in the view of new data, resulting thus in compromises on the quality of the deriving alignment. In this paper we present ReformAlign, a novel meta-alignment approach that may significantly improve on the quality of the deriving alignments from popular aligners. We call ReformAlign a meta-aligner as it requires an initial alignment, for which a variety of alignment programs can be used. The main idea behind ReformAlign is quite straightforward: at first, an existing alignment is used to construct a standard profile which summarizes the initial alignment and then all sequences are individually re-aligned against the formed profile. From each sequence-profile comparison, the alignment of each sequence against the profile is recorded and the final alignment is indirectly inferred by merging all the individual sub-alignments into a unified set. The employment of ReformAlign may often result in alignments which are significantly more accurate than the starting alignments.

Results

We evaluated the effect of ReformAlign on the generated alignments from ten leading alignment methods using real data of variable size and sequence identity. The experimental results suggest that the proposed meta-aligner approach may often lead to statistically significant more accurate alignments. Furthermore, we show that ReformAlign results in more substantial improvement in cases where the starting alignment is of relatively inferior quality or when the input sequences are harder to align.

Conclusions

The proposed profile-based meta-alignment approach seems to be a promising and computationally efficient method that can be combined with practically all popular alignment methods and may lead to significant improvements in the generated alignments.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-265) contains supplementary material, which is available to authorized users.     

A simple and computationally efficient sampling approach to covariate adjustment for multifactor dimensionality reduction analysis of epistasis

Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR's ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire.     

Network Class Superposition Analyses

Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces'' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., for the yeast cell cycle process [1]), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix , which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for derived from Boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with . We show how to generate Derrida plots based on . We show that -based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on . We motivate all of these results in terms of a popular molecular biology Boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for , for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses.     

Efficient measurement error correction with spatially misaligned data

Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally intensive alternative termed the "parameter bootstrap" that only requires solving one nonlinear optimization problem, and we also compare bootstrap methods to other recently proposed methods. We illustrate our methodology in simulations and with publicly available data from the Environmental Protection Agency.     

The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics

Human crowd motion is mainly driven by self-organized processes based on local interactions among pedestrians. While most studies of crowd behaviour consider only interactions among isolated individuals, it turns out that up to 70% of people in a crowd are actually moving in groups, such as friends, couples, or families walking together. These groups constitute medium-scale aggregated structures and their impact on crowd dynamics is still largely unknown. In this work, we analyze the motion of approximately 1500 pedestrian groups under natural condition, and show that social interactions among group members generate typical group walking patterns that influence crowd dynamics. At low density, group members tend to walk side by side, forming a line perpendicular to the walking direction. As the density increases, however, the linear walking formation is bent forward, turning it into a V-like pattern. These spatial patterns can be well described by a model based on social communication between group members. We show that the V-like walking pattern facilitates social interactions within the group, but reduces the flow because of its “non-aerodynamic” shape. Therefore, when crowd density increases, the group organization results from a trade-off between walking faster and facilitating social exchange. These insights demonstrate that crowd dynamics is not only determined by physical constraints induced by other pedestrians and the environment, but also significantly by communicative, social interactions among individuals.     

Discovering coherent biclusters from gene expression data using zero-suppressed binary decision diagrams

The biclustering method can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse in gene expression measurement. This is because the biclustering approach, in contrast to the conventional clustering techniques, focuses on finding a subset of the genes and a subset of the experimental conditions that together exhibit coherent behavior. However, the biclustering problem is inherently intractable, and it is often computationally costly to find biclusters with high levels of coherence. In this work, we propose a novel biclustering algorithm that exploits the zero-suppressed binary decision diagrams (ZBDDs) data structure to cope with the computational challenges. Our method can find all biclusters that satisfy specific input conditions, and it is scalable to practical gene expression data. We also present experimental results confirming the effectiveness of our approach.     

Using the Pareto principle in genome-wide breeding value estimation

Genome-wide breeding value (GWEBV) estimation methods can be classified based on the prior distribution assumptions of marker effects. Genome-wide BLUP methods assume a normal prior distribution for all markers with a constant variance, and are computationally fast. In Bayesian methods, more flexible prior distributions of SNP effects are applied that allow for very large SNP effects although most are small or even zero, but these prior distributions are often also computationally demanding as they rely on Monte Carlo Markov chain sampling. In this study, we adopted the Pareto principle to weight available marker loci, i.e., we consider that x% of the loci explain (100 - x)% of the total genetic variance. Assuming this principle, it is also possible to define the variances of the prior distribution of the ''big'' and ''small'' SNP. The relatively few large SNP explain a large proportion of the genetic variance and the majority of the SNP show small effects and explain a minor proportion of the genetic variance. We name this method MixP, where the prior distribution is a mixture of two normal distributions, i.e. one with a big variance and one with a small variance. Simulation results, using a real Norwegian Red cattle pedigree, show that MixP is at least as accurate as the other methods in all studied cases. This method also reduces the hyper-parameters of the prior distribution from 2 (proportion and variance of SNP with big effects) to 1 (proportion of SNP with big effects), assuming the overall genetic variance is known. The mixture of normal distribution prior made it possible to solve the equations iteratively, which greatly reduced computation loads by two orders of magnitude. In the era of marker density reaching million(s) and whole-genome sequence data, MixP provides a computationally feasible Bayesian method of analysis.     

Mouse Model of Muscle Crush Injury of the Legs

Because crush injury to skeletal muscle is an important cause of morbidity in natural disaster and battlefield settings, a reproducible and refined animal model of muscle crush injury is needed. Both open and closed small-animal models of skeletal muscle crush injury are available but are limited by their need for surgical isolation of the muscle or by the adverse effect of fibular fracture, respectively. In the current study, we developed and validated a novel, noninvasive mouse model of lower-extremity muscle crush injury. Despite the closed nature of our model, gross evidence of muscle damage was evident in all mice and was verified microscopically through hematoxylin and eosin staining. The injury elicited both neutrophil and macrophage infiltration at 24 and 48 h after injury. The area percentage and mean antigen area of F4/80-positive macrophages were higher at 48 h than at 24 h after injury, and CD68-positive macrophage area percentage and mean antigen area differed significantly between injured and uninjured muscle. In addition, the incidence of fibular fracture was one third lower than that reported for an alternative noninvasive model. In conclusion, our model is a reproducible method for muscle crush injury in the mouse pelvic limb and is a refinement of previous models because of its decreased bone fractures and reduction of animal numbers.Abbreviations: AOI, area of interestSkeletal muscle crush injury is an important cause of morbidity in both civilian and military populations. During earthquakes, tornados, and other natural disasters, collapsed structures result in crush injuries in approximately 40% of victims entrapped in the rubble,¹⁴ and crush injuries sustained during these events primarily affect skeletal muscle tissue.¹³ Crush injuries to skeletal muscle received during military conflict can occur when a limb is compressed for an extended time period, and combat-related crush injuries of the extremities frequently are present in wounded troops who are transported via aeromedical evacuation.²²A muscle-crush injury is induced when pressure is applied to skeletal muscle, interrupting blood flow and damaging the cell membranes of the muscle fibers. Several animal models of skeletal muscle-crush injury are used to study the pathophysiology of acute muscle inflammation and to investigate potential therapies.^{1,2,3,5,8,9,12,17,18,21} The most common model is the application of force to a surgically isolated pelvic limb muscle by using a clamp.¹⁶ Although closed models have been investigated, these studies typically involve dropping weights onto rodents’ pelvic limbs, thereby increasing the adverse event of fractures. Although not often reported in the literature, the incidence of fibular fractures in rats as a result of the dropped weight was 27% in one study.³ An additional drawback to the dropped-weight model is that it simulates a high-force contusion injury and does not provide the ischemic effect of the continuous pressure applied by the open clamp model. The ideal crush-injury model would mimic a force-induced injury, because 40% of survivors trapped in building rubble develop ischemia-induced crush syndrome.¹⁴We chose to investigate a novel model of closed crush injury for several reasons. An animal model of skeletal muscle injury should mimic the human clinical presentation, and a closed model more closely simulates a real-world crush injury. Second, because the incision created in the open model can activate the inflammatory response, a group of sham-operated animals is needed to control for the variable of the incision-induced inflammation. By using a closed model, the contralateral limb can serve as the uninjured control, thereby reducing the number of animals needed to perform the study. Last, the closed model represents a refinement of the crush injury procedure by removing the additional tissue damage and inflammation that result from the incision and tissue dissection of the surgical procedure and by reducing the incidence of fractures.Because this model has not been described in the literature, the objective of the current study was to develop a closed, sustained-force model of lower-extremity crush injury that induces a measurable leukocyte response and minimizes damage to nearby bones. In addition, we used monoclonal antibodies to characterize the leukocyte populations associated with this skeletal muscle crush injury model.     

Detecting conserved interaction patterns in biological networks.

Molecular interaction data plays an important role in understanding biological processes at a modular level by providing a framework for understanding cellular organization, functional hierarchy, and evolutionary conservation. As the quality and quantity of network and interaction data increases rapidly, the problem of effectively analyzing this data becomes significant. Graph theoretic formalisms, commonly used for these analysis tasks, often lead to computationally hard problems due to their relation to subgraph isomorphism. This paper presents an innovative new algorithm, MULE, for detecting frequently occurring patterns and modules in biological networks. Using an innovative graph simplification technique based on ortholog contraction, which is ideally suited to biological networks, our algorithm renders these problems computationally tractable and scalable to large numbers of networks. We show, experimentally, that our algorithm can extract frequently occurring patterns in metabolic pathways and protein interaction networks from the KEGG, DIP, and BIND databases within seconds. When compared to existing approaches, our graph simplification technique can be viewed either as a pruning heuristic, or a closely related, but computationally simpler task. When used as a pruning heuristic, we show that our technique reduces effective graph sizes significantly, accelerating existing techniques by several orders of magnitude! Indeed, for most of the test cases, existing techniques could not even be applied without our pruning step. When used as a stand-alone analysis technique, MULE is shown to convey significant biological insights at near-interactive rates. The software, sample input graphs, and detailed results for comprehensive analysis of nine eukaryotic PPI networks are available at www.cs.purdue.edu/homes/koyuturk/mule.     

A global sampling approach to designing and reengineering RNA secondary structures

The development of algorithms for designing artificial RNA sequences that fold into specific secondary structures has many potential biomedical and synthetic biology applications. To date, this problem remains computationally difficult, and current strategies to address it resort to heuristics and stochastic search techniques. The most popular methods consist of two steps: First a random seed sequence is generated; next, this seed is progressively modified (i.e. mutated) to adopt the desired folding properties. Although computationally inexpensive, this approach raises several questions such as (i) the influence of the seed; and (ii) the efficiency of single-path directed searches that may be affected by energy barriers in the mutational landscape. In this article, we present RNA-ensign, a novel paradigm for RNA design. Instead of taking a progressive adaptive walk driven by local search criteria, we use an efficient global sampling algorithm to examine large regions of the mutational landscape under structural and thermodynamical constraints until a solution is found. When considering the influence of the seeds and the target secondary structures, our results show that, compared to single-path directed searches, our approach is more robust, succeeds more often and generates more thermodynamically stable sequences. An ensemble approach to RNA design is thus well worth pursuing as a complement to existing approaches. RNA-ensign is available at http://csb.cs.mcgill.ca/RNAensign.     

Group differences in captive gorillas’ reaction to large crowds

The impact of visitor crowd size on captive animal behaviour can be difficult to interpret in many institutions due to the lack of variation in crowd size over short periods of time. In attempts to compare greater variation in crowd size, animal behaviour is often compared over conditions that create additional confounds, such as day of week or season. Fluctuations in attendance over the holiday season at Disney's Animal Kingdom^® Theme Park provided an opportunity to examine the impact of variation in crowd size on gorilla behaviour without the confounds found in many other studies. Ten western lowland gorillas (Gorilla g. gorilla) in a bachelor group (n = 4) and a family group (n = 6) were observed over a period of 8 weeks in late 2005. Observations were classified into Large Crowd and Small Crowd days and a repeated-measures ANOVA procedure was conducted to determine the effects of crowd size and social group on gorilla behaviour patterns. Few overall differences were found in behaviour during the Large Crowd and Small Crowd conditions. However, similar to previous research, gorillas were less visible during the Large Crowd condition (F_1,8 = 14.15, P = 0.01). There was also an interaction of crowd size and group (F_1,8 = 7.58, P = 0.01), indicating the bachelor group of gorillas was more aggressive during the Large Crowd condition, whereas the family group showed no increase in aggression with large crowds. These results indicate the importance of providing complex enclosures with visual barriers to allow animals to move away from large crowds if they choose. Future research should focus on the individual characteristics of animals and enclosures that may contribute to behavioural differences in visitor–animal interaction research, as well as the proximate cues associated with behaviour change when exposed to large crowds.     

Brain stimulation competes with ongoing oscillations for control of spike timing in the primate brain

Transcranial alternating current stimulation (tACS) is a popular method for modulating brain activity noninvasively. In particular, tACS is often used as a targeted intervention that enhances a neural oscillation at a specific frequency to affect a particular behavior. However, these interventions often yield highly variable results. Here, we provide a potential explanation for this variability: tACS competes with the brain’s ongoing oscillations. Using neural recordings from alert nonhuman primates, we find that when neural firing is independent of ongoing brain oscillations, tACS readily entrains spiking activity, but when neurons are strongly entrained to ongoing oscillations, tACS often causes a decrease in entrainment instead. Consequently, tACS can yield categorically different results on neural activity, even when the stimulation protocol is fixed. Mathematical analysis suggests that this competition is likely to occur under many experimental conditions. Attempting to impose an external rhythm on the brain may therefore often yield precisely the opposite effect.

Transcranial alternating current stimulation (tACS) is a popular method for modulating brain activity noninvasively; however, tACS can often yield highly variable results. This study shows that when neurons are strongly entrained to ongoing oscillations, tACS often causes a decrease in entrainment instead of the expected enhancement.     

CECT: computationally efficient congestion-avoidance and traffic engineering in software-defined cloud data centers

The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: (1) the elapsing of a predefined time interval, or, (2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3× compared to ECMP while packet loss is decreased up to 2×.     

Grand canonical ensemble Monte Carlo simulation of the dCpG/proflavine crystal hydrate.

The grand canonical ensemble Monte Carlo molecular simulation method is used to investigate hydration patterns in the crystal hydrate structure of the dCpG/proflavine intercalated complex. The objective of this study is to show by example that the recently advocated grand canonical ensemble simulation is a computationally efficient method for determining the positions of the hydrating water molecules in protein and nucleic acid structures. A detailed molecular simulation convergence analysis and an analogous comparison of the theoretical results with experiments clearly show that the grand ensemble simulations can be far more advantageous than the comparable canonical ensemble simulations.     

Visitor effects on the behavior of captive Western lowland gorillas: the importance of individual differences in examining welfare

Given the conflicting and somewhat limited findings available on the effect of zoo visitors on primate behavior, the primary purpose of this study was to provide additional data on gorillas' response to variations in crowd size and to look at what other factors—both intrinsic (e.g. personality, sex, and rearing history) and extrinsic (e.g. group)—might influence such responses. Subjects included four groups (three mixed‐sex and one bachelor) of captive western lowland gorillas living at Zoo Atlanta. Overall activity budgets and rates of social behaviors in high and low crowd conditions were compared. Behaviors suggestive of general welfare did not vary as a function of crowd size alone, although we did observe one significant interaction and several trends. Specifically, two groups spent more time engaged in undesirable behaviors in the high crowd condition, whereas the other two groups showed the opposite response pattern. Additionally, males, but not females, showed a trend toward greater rates of contact aggression in the high crowd condition, although wounding rates did not vary with crowd size. We also found trends towards variation in response to crowd size as a function of individual personality ratings. These results suggest that although the visitor effect may constitute an impediment to optimal animal welfare, this may only apply to some individuals or groups. We emphasize the importance of continuing to explore individual differences and the limitations of a one‐size‐fits‐all approach when describing influences on animal welfare. Zoo Biol 31:586‐599, 2012. © 2011 Wiley Periodicals, Inc.     

Evolution in a changing environment: a case study with great tit fledging mass

Heritable phenotypic traits under significant and consistent directional selection often fail to show the expected evolutionary response. A potential explanation for this contradiction is that because environmental conditions change constantly, environmental change can mask an evolutionary response to selection. We combined an "animal model" analysis with 36 years of data from a long-term study of great tits (Parus major) to explore selection on and evolution of a morphological trait: body mass at fledging. We found significant heritability of this trait, but despite consistent positive directional selection on both the phenotypic and the additive genetic component of body mass, the population mean phenotypic value declined rather than increased over time. However, the mean breeding value for body mass at fledging increased over time, presumably in response to selection. We show that the divergence between the response to selection observed at the levels of genotype and phenotype can be explained by a change in environmental conditions over time, that is, related both to increased spring temperature before breeding and elevated population density. Our results support the suggestion that measuring phenotypes may not always give a reliable impression of evolutionary trajectories and that understanding patterns of phenotypic evolution in nature requires an understanding of how the environment has itself changed.     

Effects of trematode parasitism on the shell morphology of snails from flow and nonflow environments

The primary function of the gastropod shell is protection. However, shells that function well in one environment may be maladaptive in another. Upon infection, the snail shell protects internal parasites and it is to the parasite's advantage to optimize, or not interfere with, shell functionality. However, parasites, particularly trematodes, are often pathogenic and it is not clear if parasitism will induce environment‐dependent or ‐independent changes to gastropod shells. We conducted a field study and a complementary laboratory experiment to examine the effects of trematode parasitism on shell characteristics (shape, size, and crush resistance) of Physa acuta snails in flow and nonflow environments using geometric morphometrics and crush assays. Field results indicate wetland (nonflow) snails had large, crush resistant shells with narrow apertures and tall spires. In contrast, stream (flow) snails had small, weak shells with wide apertures and short spires. Parasitism had no apparent effect on the crush resistance of wetland snails but significantly reduced the crush resistance of stream snails. Parasitism had no significant effect on overall shell shape in stream or wetland snails. Similar to the results of our field study, nonflow tank snails had significantly more crush resistant shells than flow tank snails. Additionally, the shapes of flow and nonflow tank snails significantly differed where nonflow tank snails resembled wetland snails and flow tank snails resembled stream snails. For laboratory snails, parasitism reduced crush resistance regardless of flow/nonflow treatment. Our results demonstrate that habitat and/or flow treatment was the primary factor affecting P. acuta shell morphology and that trematode parasitism played a secondary role. J. Morphol. 277:316–325, 2016. © 2015 Wiley Periodicals, Inc.