How good are comparative models in the understanding of protein dynamics?

The 3D structure of a protein is essential to understand protein dynamics. If experimentally determined structure is unavailable, comparative models could be used to infer dynamics. However, the effectiveness of comparative models, compared to experimental structures, in inferring dynamics is not clear. To address this, we compared dynamics features of ~800 comparative models with their crystal structures using normal mode analysis. Average similarity in magnitude, direction, and correlation of residue motions is >0.8 (where value 1 is identical) indicating that the dynamics of models and crystal structures are highly similar. Accuracy of 3D structure and dynamics is significantly higher for models built on multiple and/or high sequence identity templates (>40%). Three-dimensional (3D) structure and residue fluctuations of models are closer to that of crystal structures than to templates (TM score 0.9 vs 0.7 and square inner product 0.92 vs 0.88). Furthermore, long-range molecular dynamics simulations on comparative models of RNase 1 and Angiogenin showed significant differences in the conformational sampling of conserved active-site residues that characterize differences in their activity levels. Similar analyses on two EGFR kinase variant models highlight the effect of mutations on the functional state-specific αC helix motions and these results corroborate with the previous experimental observations. Thus, our study adds confidence to the use of comparative models in understanding protein dynamics.     

How do visual neurons respond in the real world?

Natural visual stimuli have been reported to evoke neural responses with brief, intense firing events. This has important implications for how visual information is encoded. A recent experiment measured the responses of visual neurons in the natural environment, circumventing many limitations of laboratory simulations while still retaining tight experimental control. Under these conditions, neurons fired intensely at precise, discrete times, validating that this firing regime is relevant to natural vision.     

How do neurons die in neurodegenerative diseases?

Given that neurons are post-mitotic cells, their life span is generally long enough to reach that of humans. However, sometimes neurons die without recognizable causes, as a result of a process called neurodegeneration. Apart from when gene mutations can be correlated with disease, it is difficult to pinpoint molecules that are responsible for neuronal death. Therefore, neurons living in a 'sick state' for many years might reveal important information about neuronal death. Systematic and extensive single-neuron analysis of 'sick' neurons is expected to provide clues to the mechanisms of neurodegeneration. Moreover, the elimination of putative triggering and promoting factors involved in neurodegenerative disease might prevent disease progression.     

How well do we understand the clusters found in microarray data?

We wished to quantify the state-of-the-art of our understanding of clusters in microarray data. To do this we systematically compared the clusters produced on sets of microarray data using a representative set of clustering algorithms (hierarchical, k-means, and a modified version of QT_CLUST) with the annotation schemes MIPS, GeneOntology and GenProtEC. We assumed that if a cluster reflected known biology its members would share related ontological annotations. This assumption is the basis of "guilt-by-association" and is commonly used to assign the putative function of proteins. To statistically measure the relationship between cluster and annotation we developed a new predictive discriminatory measure. We found that the clusters found in microarray data do not in general agree with functional annotation classes. Although many statistically significant relationships can be found, the majority of clusters are not related to known biology (as described in annotation ontologies). This implies that use of guilt-by-association is not supported by annotation ontologies. Depending on the estimate of the amount of noise in the data, our results suggest that bioinformatics has only codified a small proportion of the biological knowledge required to understand microarray data.     

How well can the accuracy of comparative protein structure models be predicted?

Comparative structure models are available for two orders of magnitude more protein sequences than are experimentally determined structures. These models, however, suffer from two limitations that experimentally determined structures do not: They frequently contain significant errors, and their accuracy cannot be readily assessed. We have addressed the latter limitation by developing a protocol optimized specifically for predicting the Calpha root-mean-squared deviation (RMSD) and native overlap (NO3.5A) errors of a model in the absence of its native structure. In contrast to most traditional assessment scores that merely predict one model is more accurate than others, this approach quantifies the error in an absolute sense, thus helping to determine whether or not the model is suitable for intended applications. The assessment relies on a model-specific scoring function constructed by a support vector machine. This regression optimizes the weights of up to nine features, including various sequence similarity measures and statistical potentials, extracted from a tailored training set of models unique to the model being assessed: If possible, we use similarly sized models with the same fold; otherwise, we use similarly sized models with the same secondary structure composition. This protocol predicts the RMSD and NO3.5A errors for a diverse set of 580,317 comparative models of 6174 sequences with correlation coefficients (r) of 0.84 and 0.86, respectively, to the actual errors. This scoring function achieves the best correlation compared to 13 other tested assessment criteria that achieved correlations ranging from 0.35 to 0.71.     

How do cross-migration models arise?

In this paper we present a general method to derive spatio-temporal population models mechanistically. We consider a system of multiple species living in a patchy habitat in which the local population of each species consists of some behavioural groups. We then formulate a continuous-time model where a small positive parameter is present, measuring the time scale of behavioural transitions relative to that of giving birth, death and migration among patches. By the singular perturbation method the model is reduced to a lower dimensional one in which the migration terms are, in general, nonlinear and related to the reaction terms describing the local dynamics. Two examples demonstrating the emergence of cross-migration models, i.e., the models in which the per-capita migration rate of one species depends on the density of some other species, are given.     

How do toxicants affect epidemiological dynamics?

Populations are formed of their constituent interacting individuals, each with their own respective within‐host biological processes. Infection not only spreads within the host organism but also spreads between individuals. Here we propose and study a multilevel model which links the within‐host statuses of immunity and parasite density to population epidemiology under sublethal and lethal toxicant exposure. We analyse this nested model in order to better understand how toxicants impact the spread of disease within populations. We demonstrate that outbreak of infection within a population is completely determined by the level of toxicant exposure, and that it is maximised by intermediate toxicant dosage. We classify the population epidemiology into five phases of increasing toxicant exposure and calculate the conditions under which disease will spread, showing that there exists a threshold toxicant level under which epidemics will not occur. In general, higher toxicant load results in either extinction of the population or outbreak of infection. The within‐host statuses of the individual host also determine the outcome of the epidemic at the population level. We discuss applications of our model in the context of environmental epidemiology, predicting that increased exposure to toxicants could result in greater risk of epidemics within ecological systems. We predict that reducing sublethal toxicant exposure below our predicted safe threshold could contribute to controlling population level disease and infection.     

What do simple models reveal about the population dynamics of a cooperatively breeding species?

Research on cooperatively breeding species has shown that their population dynamics differ from those of conventional breeders. Populations of cooperators are structured into groups, and group‐level Allee effects are likely common. We assess the ability of phenomenological models, lacking explicit group structure, to describe population dynamics in cooperative meerkats Suricata suricatta, and we assess potential Allee effects at the population level. Using maximum likelihood model fitting and information theoretic model selection, applied to time series data from a wild meerkat population, we find simple models that incorporate rainfall and conventional density dependence to be the most parsimonious of the models considered. Detecting no population‐level Allee effect, we conclude that explicit consideration of population structure will be key to understanding the mechanisms behind population dynamics in cooperatively breeding species.     

How well do the existing and proposed reserve networks represent vertebrate species in Chile?

Increasingly, biogeographical knowledge and analysis are playing a fundamental role in assessing the representativeness of biodiversity in protected areas, and in identifying critical areas for conservation. With almost 20% of the country assigned to protected areas, Chile is well above the conservation target (i.e. 10–12%) proposed by many international conservation organizations. Moreover, the Chilean government has recently proposed new conservation priority sites to improve the current protected area network. Here, we used all 653 terrestrial vertebrate species present in continental Chile to assess the performance of the existing and proposed reserve networks. Using geographical information systems, we overlaid maps of species distribution, current protected areas, and proposed conservation priority sites to assess how well each species is represented within these networks. Additionally, we performed a systematic reserve selection procedure to identify alternative conservation areas for expanding the current reserve system. Our results show that over 13% of the species are not covered by any existing protected area, and that 73% of Chilean vertebrate species can be considered partial gaps, with only a small fraction of their geographical ranges currently under protection. The coverage is also deficient for endemic (species confined to Chile) and threatened species. While the proposed priority sites do increase coverage, we found that there are still several gaps and these are not the most efficient choices. Both the gap analysis and the reserve selection analysis identified important areas to be added to the existing reserve system, mostly in northern and central Chile. This study underscores the need for a systematic conservation planning approach to redefine the conservation priority sites in order to maximize the representation of species, particularly endemic and threatened species.     

How do misassigned paternities affect the estimation of heritability in the wild?

Studies of birds have recently played an important role in the increasing success of quantitative genetics applied to natural populations. However, these studies mostly base their pedigree relationships on social information, despite the known widespread genetic polygamy in avian species. Here, we study the influence of misassigned paternities, combined with the effect of pedigree size and depth, on the estimation of heritability. First, we compute simulations of a polygenic trait for two levels of heritability (0.1 and 0.4), several extra-pair paternity rates (ranging from 5% to 40%), and varying sample sizes (20, 50 and 100 broods) or pedigree depth (2 or 4 generations). We compare heritability estimates from the social and the genetic pedigree, running a restricted maximum-likelihood 'animal model'. Social pedigree underestimates heritability by an average of 0-17% for 5-20% extra-pair paternities and by up to 18% for 40% extra-pair paternities and a heritability of 0.4. Second, we identifyied extra-pair offspring using microsatellite loci in two populations of blue tits (Parus caeruleus) showing high levels of extra-pair paternities (15% and 25% of extra-pair offspring). We compare heritabilities of tarsus length and body mass estimated with pedigrees of increasing accuracy. These analyses suggest that the bias induced by misassigned paternities on heritability estimation depends on the level of heritability and the rate of paternity error. Typical rates of extra-pair paternities in birds (around 20% of offspring) should result in an underestimation of heritability of less than 15% when estimated over a minimum of 100 broods.     

How well do laboratory experiments explain field patterns of zooplankton emergence?

1. We conducted a laboratory experiment to explore potential mechanisms driving variation in zooplankton emergence from diapausing eggs observed in Oneida Lake, NY, U.S.A. We hypothesized that variation in timing of ice-out (date of thawing of ice) between 1994 and 1995, which resulted in variation in photoperiod–temperature cues, contributed to the differences in the observed field patterns. Environmental chambers were used to establish weekly photoperiod–temperature combinations that reproduced natural conditions in Oneida Lake in 1994 and 1995. In addition, a third treatment ('dark') exposed eggs only to the changes in temperature. We recorded zooplankton emergence for 2.5 simulated ice-free seasons.
2. Nine cladoceran taxa were found to hatch, but only Daphnia pulicaria in large numbers. Hatching of D. pulicaria was recorded throughout the season in the two light treatments and sporadically in the dark treatment. The early ice-out treatment had the highest emergence, followed by the late ice-out and dark treatments. Among taxa, there was temporal segregation with five hatching in the early weeks of sampling and two taxa hatching during the middle weeks. Alona hatched late in the first year, but earlier in the second year.
3. We compared our laboratory results of D. pulicaria hatching with the field data obtained by Cáceres (1998) . Hatching was continuous in the laboratory, whereas a synchronous spring emergence was found in the field. However, in both the laboratory and the field, more D. pulicaria hatched under conditions reflecting ice-out occurring in March as opposed to April. Because differences in rates and timing of emergence can affect the population and community dynamics of pelagic systems, we suggest caution when applying laboratory results to field populations.     

How well can the exponential-growth coalescent approximate constant-rate birth–death population dynamics?

One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth–death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R₀ close to 1, but substantial differences remain for large R₀. In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.     

How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?

We use mean field techniques to compute the distribution of excitatory and inhibitory firing rates in large networks of randomly connected spiking quadratic integrate and fire neurons. These techniques are based on the assumption that activity is asynchronous and Poisson. For most parameter settings these assumptions are strongly violated; nevertheless, so long as the networks are not too synchronous, we find good agreement between mean field prediction and network simulations. Thus, much of the intuition developed for randomly connected networks in the asynchronous regime applies to mildly synchronous networks.     

Should models of disease dynamics in herbivorous insects include the effects of variability in host-plant foliage quality?

Interactions between insects and their baculovirus pathogens are often described using simple disease models. Baculoviruses, however, are transmitted when insects consume virus-contaminated foliage, and foliage variability, whether within or between host-plant species, can affect viral infectiousness. Insect-baculovirus interactions may thus be embedded in a tritrophic interaction with the insect's host plant, but disease models include only the host and the pathogen. We tested these models by measuring the transmission of a baculovirus of gypsy moths (Lymantria dispar) on red oak (Quercus rubra) and white oak (Quercus alba) in the field in six experiments over four years. In all experiments, there were only weak effects of host-tree species, and in only one did the best-fitting model include tree species effects. These weak effects of foliage variability on transmission were not due to a lack of foliage variability on viral infectiousness, because when larvae were force-fed virus-contaminated foliage, infection rates were higher on white oak. Our results suggest that feeding behavior plays an important role in baculovirus transmission and that models can usefully describe baculovirus dynamics even without including foliage variability. Our work provides a clear example of how two-species models are sometimes sufficient to describe what appear to be tritrophic interactions.