Disentangling direct from indirect relationships in association networks.
Ontology highlight
ABSTRACT:
SUBMITTER: Xiao N
PROVIDER: S-EPMC8764688 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Predicting protein structure from primary sequence is one of the ultimate challenges in computational biology. Given the large amount of available sequence data, the analysis of co-evolution, i.e., statistical dependency, between columns in multiple alignments of protein domain sequences remains one of the most promising avenues for predicting residues that are contacting in the structure. A key impediment to this approach is that strong statistical dependencies are also observed for many residue pairs that are distal in the structure. Using a comprehensive analysis of protein domains with available three-dimensional structures we show that co-evolving contacts very commonly form chains that percolate through the protein structure, inducing indirect statistical dependencies between many distal pairs of residues. We characterize the distributions of length and spatial distance traveled by these co-evolving contact chains and show that they explain a large fraction of observed statistical dependencies between structurally distal pairs. We adapt a recently developed Bayesian network model into a rigorous procedure for disentangling direct from indirect statistical dependencies, and we demonstrate that this method not only successfully accomplishes this task, but also allows contacts with weak statistical dependency to be detected. To illustrate how additional information can be incorporated into our method, we incorporate a phylogenetic correction, and we develop an informative prior that takes into account that the probability for a pair of residues to contact depends strongly on their primary-sequence distance and the amount of conservation that the corresponding columns in the multiple alignment exhibit. We show that our model including these extensions dramatically improves the accuracy of contact prediction from multiple sequence alignments.
| S-EPMC2793430 | biostudies-literature
Project description:BackgroundEcological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not.ResultsWe present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges-87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors.ConclusionsTo reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses. Video abstract.
| S-EPMC8620190 | biostudies-literature
Project description:Reverse engineering of biological pathways involves an iterative process between experiments, data processing, and theoretical analysis. Despite concurrent advances in quality and quantity of data as well as computing resources and algorithms, difficulties in deciphering direct and indirect network connections are prevalent. Here, we adopt the notions of abstraction, emulation, benchmarking, and validation in the context of discovering features specific to this family of connectivities. After subjecting benchmark synthetic circuits to perturbations, we inferred the network connections using a combination of nonparametric single-cell data resampling and modular response analysis. Intriguingly, we discovered that recovered weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is markedly different between topologies. Our results point to a conceptual advance for reverse engineering beyond weight inference. Investigating topological changes under differential perturbations may address the longstanding problem of discriminating direct and indirect connectivities in biological networks.
| S-EPMC4611647 | biostudies-literature
Project description:Climate is a major driver of species diversity. However, its effect can be either direct due to species physiological tolerances or indirect, whereby wetter climates facilitate more complex vegetation and consequently higher diversity due to greater resource availability. Yet, studies quantifying both direct and indirect effects of climate on multiple dimensions of diversity are rare. We used extensive data on species distributions, morphological and ecological traits, and vegetation across Australia to quantify both direct (water availability) and indirect (habitat diversity and canopy height) effects of climate on the species richness (SR), phylogenetic diversity (PD), and functional diversity (FD) of 536 species of birds. Path analyses revealed that SR increased with wetter climates through both direct and indirect effects, lending support for the influence of both physiological tolerance and vegetation complexity. However, residual PD and residual FD (adjusted for SR by null models) were poorly predicted by environmental conditions. Thus, the FD and PD of Australian birds mostly evolved in concert with SR, with the possible exception of the higher-than-expected accumulation of avian lineages in wetter and more productive areas in northern and eastern Australia (with high residual PD), permitted probably by older biome age.
| S-EPMC6195560 | biostudies-literature
Project description:Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, i.e., through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example, when trying to improve crop yield and simultaneously control plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most current methods require all genetic variance to be explained by a small number of quantitative trait loci (QTL) with fixed effects. Only a few authors have considered the "missing heritability" case, where contributions of many undetectable QTL are modeled with random effects. Usually, these are treated as nuisance terms that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such an MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here, we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits; and (2) we can test the existence of direct genetic effects, and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.
| S-EPMC7153926 | biostudies-literature
Project description:As fragmented landscapes become increasingly common around the world, managing the spatial arrangement of landscape elements (i.e., landscape configuration) may help to promote the conservation of biodiversity. However, the relative effects of landscape configuration on different dimensions of biodiversity across species assemblages are largely unknown. Thus, a key challenge consists in understanding when it is necessary to focus on landscape configuration, in addition to landscape composition, to achieve multifunctional landscapes. We tested the effects of landscape composition (the percentage of tree cover and built infrastructure) and landscape configuration (degree of fragmentation) on landscape-level species richness and different metrics of functional diversity of urban birds. We collected data on different bird guilds (nectarivores/frugivores, insectivores) from Brisbane, Australia. Using structural equation models, we found that landscape structure (landscape composition and configuration) affected functional diversity via two main pathways: (1) through effects of landscape composition, mediated by landscape configuration (indirect effects), and (2) through direct ("independent") effects of landscape composition and configuration, filtering species with extreme trait values. Our results show that landscape-level species richness declined with the extent of built infrastructure, but patterns of trait diversity did not necessarily correlate with this variable. Landscape configuration had a stronger mediating effect on some metrics of the functional diversity of insectivores than on the functional diversity of frugivores/nectarivores. In addition, fragmentation increased the effects of built infrastructure for some traits (body size and dispersal capacity), but not for others (habitat plasticity and foraging behavior). These results suggest that differential approaches to managing landscape structure are needed depending on whether the focus is on protecting functional diversity or species richness and what the target guild is. Managing landscape fragmentation in areas with high levels of built infrastructure is important if the objective is to protect insectivore species with uncommon traits, even if it is not possible to preserve high levels of species richness. However, if the target is to enhance both functional diversity and species richness of multiple guilds, the focus should be on improving composition through the reduction of negative effects of built infrastructure, rather than promoting specific landscape configurations in growing cities.
| S-EPMC10077913 | biostudies-literature
Project description:The authors use recent methodology in causal inference to disentangle the direct and indirect effects that operate through a mediator in an exposure-response association paradigm. They demonstrate how total effects can be partitioned into direct and indirect effects even when the exposure and mediator interact. The impact of bias due to unmeasured confounding on the exposure-response association is assessed through a series of sensitivity analyses. These methods are applied to a problem in perinatal epidemiology to examine the extent to which the effect of abruption on perinatal mortality is mediated through preterm delivery. Data on over 26 million US singleton births (1995-2002) were utilized. Risks of mortality among abruption and nonabruption births were 102.7 and 6.2 per 1,000 births, respectively. Risk ratios of the natural direct and indirect (preterm delivery-mediated) effects of abruption on mortality were 10.18 (95% confidence interval: 9.80, 10.58) and 1.35 (95% confidence interval: 1.33, 1.38), respectively. The proportion of increased mortality risk mediated through preterm delivery was 28.1%, with even higher proportions associated with deliveries at earlier gestational ages. Sensitivity analyses underscore that the qualitative conclusions of some mediated effects and substantial direct effects are reasonably robust to unmeasured confounding of a fairly considerable magnitude.
| S-EPMC3159429 | biostudies-literature
Project description:The duration of maternal care, an important life-history trait affecting population dynamics, varies greatly within species. Yet, our understanding of its predictors is limited, mostly correlative and subject to misinterpretations, due to difficulties to disentangle the role of maternal- and offspring-related characteristics. We conducted path analysis on a dataset including 217 brown bear litters captured over a 29-year period in two populations in Sweden ('North' and 'South') facing contrasting environmental conditions to identify and quantify the causes of variation in the duration of maternal care (1.5 or 2.5 years). We showed that the causal determinants of the duration of maternal care were context-dependent. Contrary to their expected central role in the determination of the duration of maternal care, yearling mass and its direct determinants (i.e. litter size and maternal mass) were only important in the North population, where environmental conditions are harsher and the cost of extended maternal care presumably higher. In the South, the duration of maternal care was not caused by yearling mass nor any maternal or litter characteristics. Extension of maternal care may thus result from factors independent from maternal and offspring condition in the South, such as an artificial hunting-induced selection for longer maternal care through the legal protection of family groups. Our results provide an important contribution to our very limited knowledge of the direct and indirect determinants of the duration of maternal care and highlight the importance of accounting for the environmental context when assessing maternal reproductive tactics.
| S-EPMC7894530 | biostudies-literature
Project description:Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems ["GPS"]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions-individuals occurring at the same location, but at different times-while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer-resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.
| S-EPMC10040956 | biostudies-literature
Project description:BackgroundPlant biodiversity can affect trophic interactions in many ways, including direct bottom-up effects on insects, but is negatively affected by agricultural intensification. Grassland intensification promotes plant productivity, resulting in changes in plant community composition, and impacts on higher trophic levels. Here, we use a novel grassland management experiment combining manipulations of cutting and fertilization with experimental changes in plant functional group composition (independent of management effects) to disentangle the direct and indirect effects of agricultural management on insect herbivore diversity and abundance. We used leafhoppers as model organisms as they are a key insect taxon in grasslands and react rapidly to management changes. Leafhoppers were sampled between May and September 2010 using standardized sweep netting and pan traps.ResultsPlant diversity, functional group composition and management regime in grasslands affected leafhopper species richness and abundance. Higher cutting frequencies directly led to decreasing leafhopper species richness, presumably due to the higher disturbance frequency and the reduction in food-resource heterogeneity. In contrast, fertilizer application had only a small indirect negative effect via enhanced aboveground plant biomass, reduced plant diversity and changes in functional group composition. The manipulated increase in grass cover had contrasting direct and indirect effects on leafhopper species richness: grass cover directly increased leafhopper species richness, but negatively affected plant diversity, which in turn was positively related to leafhopper species richness. In conclusion, insect diversity is driven in complex direct and indirect ways by grassland management, including changes in functional group composition.ConclusionsThe availability of preferred food sources and the frequency of disturbance are important direct and indirect drivers of leafhopper species richness, interacting in complex ways with plant diversity and food resource heterogeneity.
| S-EPMC3945068 | biostudies-literature