scholarly journals Ploidy frequencies in plants with ploidy heterogeneity: fitting a general gametic model to empirical population data

2013 ◽  
Vol 280 (1751) ◽  
pp. 20122387 ◽  
Author(s):  
Jan Suda ◽  
Tomáš Herben
Keyword(s):  
Natural Populations ◽  
Evolutionary Process ◽  
Optimization Procedure ◽  
Population Data ◽  
Unreduced Gametes ◽  
Unreduced Gamete ◽  
Model Parameters ◽  
Main Mode ◽  
Ploidy Levels ◽  
Polyploid Formation

Genome duplication (polyploidy) is a recurrent evolutionary process in plants, often conferring instant reproductive isolation and thus potentially leading to speciation. Outcome of the process is often seen in the field as different cytotypes co-occur in many plant populations. Failure of meiotic reduction during gametogenesis is widely acknowledged to be the main mode of polyploid formation. To get insight into its role in the dynamics of polyploidy generation under natural conditions, and coexistence of several ploidy levels, we developed a general gametic model for diploid–polyploid systems. This model predicts equilibrium ploidy frequencies as functions of several parameters, namely the unreduced gamete proportions and fertilities of higher ploidy plants. We used data on field ploidy frequencies for 39 presumably autopolyploid plant species/populations to infer numerical values of the model parameters (either analytically or using an optimization procedure). With the exception of a few species, the model fit was very high. The estimated proportions of unreduced gametes (median of 0.0089) matched published estimates well. Our results imply that conditions for cytotype coexistence in natural populations are likely to be less restrictive than previously assumed. In addition, rather simple models show sufficiently rich behaviour to explain the prevalence of polyploids among flowering plants.

scholarly journals Inferring Linkage Disequilibrium Between a Polymorphic Marker Locus and a Trait Locus in Natural Populations

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 457-467 ◽  
Author(s):  
Z W Luo ◽  
S H Tao ◽  
Z-B Zeng
Keyword(s):  
Linkage Disequilibrium ◽  
Allele Frequency ◽  
Random Mating ◽  
Natural Populations ◽  
Polymorphic Marker ◽  
Marker Locus ◽  
Model Parameters ◽  
Phenotypic Variance ◽  
Wide Range ◽  
Trait Locus

Abstract Three approaches are proposed in this study for detecting or estimating linkage disequilibrium between a polymorphic marker locus and a locus affecting quantitative genetic variation using the sample from random mating populations. It is shown that the disequilibrium over a wide range of circumstances may be detected with a power of 80% by using phenotypic records and marker genotypes of a few hundred individuals. Comparison of ANOVA and regression methods in this article to the transmission disequilibrium test (TDT) shows that, given the genetic variance explained by the trait locus, the power of TDT depends on the trait allele frequency, whereas the power of ANOVA and regression analyses is relatively independent from the allelic frequency. The TDT method is more powerful when the trait allele frequency is low, but much less powerful when it is high. The likelihood analysis provides reliable estimation of the model parameters when the QTL variance is at least 10% of the phenotypic variance and the sample size of a few hundred is used. Potential use of these estimates in mapping the trait locus is also discussed.

scholarly journals On the mod resc Model and the Evolution of Wolbachia Compatibility Types

Genetics ◽  
2001 ◽  
Vol 159 (4) ◽  
pp. 1415-1422 ◽  
Author(s):  
Sylvain Charlat ◽  
Claire Calmet ◽  
Hervé Merçot
Keyword(s):  
Cytoplasmic Incompatibility ◽  
Natural Populations ◽  
Evolutionary Process ◽  
Embryonic Mortality ◽  
Step Process ◽  
Zygote Development ◽  
Specific Manner

Abstract Cytoplasmic incompatibility (CI) is induced by the endocellular bacterium Wolbachia. It results in an embryonic mortality occurring when infected males mate with uninfected females. The mechanism involved is currently unknown, but the mod resc model allows interpretation of all observations made so far. It postulates the existence of two bacterial functions: modification (mod) and rescue (resc). The mod function acts in the males' germline, before Wolbachia are shed from maturing sperm. If sperm is affected by mod, zygote development will fail unless resc is expressed in the egg. Interestingly, CI is also observed in crosses between infected males and infected females when the two partners bear different Wolbachia strains, demonstrating that mod and resc interact in a specific manner: Two Wolbachia strains are compatible with each other only if they harbor the same compatibility type. Here we focus on the evolutionary process involved in the emergence of new compatibility types from ancestral ones. We argue that new compatibility types are likely to evolve under a wider range of conditions than previously thought, through a two-step process. First, new mod variants can arise by mutation and spread by drift. This is possible because mod is expressed in males and Wolbachia is transmitted by females. Second, once such a mod variant achieves a certain frequency, it can create the conditions for the deterministic invasion of a new resc variant, allowing the invasion of a new mod resc pair. Furthermore, we show that a stable polymorphism might be maintained in natural populations, allowing the long-term existence of “suicidal” Wolbachia strains.

scholarly journals Male meiosis and pollen morphology in diploid Indonesian wild bananas and cultivars

The Nucleus ◽  
2021 ◽  
Author(s):  
Fajarudin Ahmad ◽  
Yuyu S. Poerba ◽  
Gert H. J. Kema ◽  
Hans de Jong
Keyword(s):  
Hybrid Sterility ◽  
Pollen Grains ◽  
Chromosome Analysis ◽  
Male Meiosis ◽  
Enzyme Treatment ◽  
Unreduced Gamete ◽  
Ploidy Levels ◽  
Scientific Disciplines ◽  
Sterile Pollen ◽  
Mother Cells

AbstractBreeding of banana is hampered by its genetic complexity, structural chromosome rearrangements and different ploidy levels. Various scientific disciplines, including cytogenetics, linkage mapping, and bioinformatics, are helpful tools in characterising cultivars and wild relatives used in crossing programs. Chromosome analysis still plays a pivotal role in studying hybrid sterility and structural and numerical variants. In this study, we describe the optimisation of the chromosome spreading protocol of pollen mother cells focusing on the effects of standard fixation methods, duration of the pectolytic enzyme treatment and advantages of fluorescence microscopy of DAPI stained cell spreads. We demonstrate the benefits of this protocol on meiotic features of five wild diploid Musa acuminata bananas and a diploid (AA) cultivar banana “Rejang”, with particular attention on pairing configurations and chromosome transmission that may be indicative for translocations and inversions. Pollen slides demonstrate regular-shaped spores except “Rejang”, which shows fertile pollen grains of different size and sterile pollen grains, suggesting partial sterility and unreduced gamete formation that likely resulted from restitutional meiotic divisions.

scholarly journals Modeling Polypharmacy Side Effects with Graph Convolutional Networks

2018 ◽  
Author(s):  
Marinka Zitnik ◽  
Monica Agrawal ◽  
Jure Leskovec
Keyword(s):  
Side Effects ◽  
Drug Interactions ◽  
Protein Interactions ◽  
Side Effect ◽  
Population Data ◽  
Model Parameters ◽  
Mortality And Morbidity ◽  
Convolutional Networks ◽  
Important Challenge ◽  
Complex Relationships

AbstractMotivation: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.Results: Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.Availability: Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.Contact:[email protected]

scholarly journals Transcriptome Analysis of Young Ovaries Reveals Candidate Genes Involved in Gamete Formation in Lantana camara

Plants ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 263 ◽  
Author(s):  
Ze Peng ◽  
Krishna Bhattarai ◽  
Saroj Parajuli ◽  
Zhe Cao ◽  
Zhanao Deng
Keyword(s):  
Candidate Genes ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Gene Families ◽  
Unreduced Gametes ◽  
Lantana Camara ◽  
Unreduced Gamete ◽  
Genomic Resources ◽  
Gamete Formation ◽  
Gamete Production

Lantana (Lantana camara L., Verbenaceae) is an important ornamental crop, yet can be a highly invasive species. The formation of unreduced female gametes (UFGs) is a major factor contributing to its invasiveness and has severely hindered the development of sterile cultivars. To enrich the genomic resources and gain insight into the genetic mechanisms of UFG formation in lantana, we investigated the transcriptomes of young ovaries of two lantana genotypes, GDGHOP-36 (GGO), producing 100% UFGs, and a cultivar Landmark White Lantana (LWL), not producing UFGs. The de novo transcriptome assembly resulted in a total of 90,641 unique transcript sequences with an N50 of 1692 bp, among which, 29,383 sequences contained full-length coding sequences (CDS). There were 214 transcripts associated with the biological processes of gamete production and 10 gene families orthologous to genes known to control unreduced gamete production in Arabidopsis. We identified 925 transcription factor (TF)-encoding sequences, 91 nucleotide-binding site (NBS)-containing genes, and gene families related to drought/salt tolerance and allelopathy. These genomic resources and candidate genes involved in gamete formation will be valuable for developing new tools to control the invasiveness in L. camara, protect native lantana species, and understand the formation of unreduced gametes in plants.

scholarly journals Inference of a Mesoscopic Population Model from Population Spike Trains

2020 ◽  
Vol 32 (8) ◽  
pp. 1448-1498 ◽  
Author(s):  
Alexandre René ◽  
André Longtin ◽  
Jakob H. Macke
Keyword(s):  
Population Model ◽  
Single Neuron ◽  
Simulated Data ◽  
Population Data ◽  
Model Parameters ◽  
Population Activity ◽  
Mcmc Sampling ◽  
Wide Range ◽  
Low Dimensional ◽  
Aggregate Population

Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

Topology Optimization for Piecewise Dynamic Models Using a Genetic Algorithm

2010 ◽  
Author(s):  
Yi Liu ◽  
Dragan Djurdjanovic
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Optimization Procedure ◽  
Nonlinear Dynamic System ◽  
Local Model ◽  
Model Parameters ◽  
Highly Nonlinear ◽  
Node Connectivity

It has been demonstrated in the previous research that the node connectivity in the graph encoding the topological neighborhood relationships between local models in a piecewise dynamic model may significantly affect the cooperative learning process. It was shown that a graph with a larger connectivity leads to a quicker learning adaption due to more rapidly decaying transients of the estimation of local model parameters. In the same time, it was shown that the accuracy could be degraded by a larger bias in the asymptotic portion of the estimations of local model parameters. The efforts in topology optimization should therefore strive towards a high accuracy of the asymptotic portion of the estimator of local model parameters while simultaneously accelerating the decay of the estimation transients. In this paper, we pursue minimization of the residual sum of squares of a piecewise dynamic model after a predetermined number of training steps. The optimization of inter-model topology is implemented via a genetic algorithm that manipulates adjacency matrices of the graph underlying the piecewise dynamic model. An example of applying the topology optimization procedure on a peicewise linear model of a highly nonlinear dynamic system is provided to show the efficacy of the new method.

scholarly journals Bayesian data assimilation provides rapid decision support for vector-borne diseases

2015 ◽  
Vol 12 (108) ◽  
pp. 20150367 ◽  
Author(s):  
Chris P. Jewell ◽  
Richard G. Brown
Keyword(s):  
Decision Support ◽  
Data Assimilation ◽  
Population Data ◽  
Disease Status ◽  
Host Population ◽  
Significant Advance ◽  
Model Parameters ◽  
Vector Borne Diseases ◽  
Vector Borne ◽  
Bayesian Data Assimilation

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks.

Design for Thermo-Acoustic Stability: Modeling of Burner and Flame Dynamics

2013 ◽  
Author(s):  
Stefanie Bade ◽  
Michael Wagner ◽  
Christoph Hirsch ◽  
Thomas Sattelmayer ◽  
Bruno Schuermans
Keyword(s):  
Optimization Procedure ◽  
Parametric Models ◽  
Design Parameters ◽  
Model Parameters ◽  
Geometrical Parameters ◽  
Flame Dynamics ◽  
Stability Model ◽  
Flame Response ◽  
Burner Design ◽  
Acoustic Stability

A Design for Thermo-Acoustic Stability (DeTAS) procedure is presented, that aims at selecting a most stable burner geometry for a given combustor. It is based on the premise that a thermo-acoustic stability model of the combustor can be formulated and that a burner design exists, which has geometric design parameters that sufficiently influence the dynamics of the flame. Describing the flame dynamics in dependence of the geometrical parameters an optimization procedure involving a linear stability model of the target combustor maximizes the damping and thereby yields the optimal geometrical parameters. To demonstrate the procedure on an existing annular combustor a generic burner design was developed that features a significant variability of dynamical flame response in dependence of two geometrical parameters. In this paper the experimentally determined complex burner acoustics and complex flame responses are described in terms of physics based parametric models with excellent agreement between experimental and model data. It is shown that these model parameters correlate uniquely with the variation of the burner geometrical parameters, allowing to interpolate the model with respect to the geometrical parameters. The interpolation is validated with experimental data.

scholarly journals Integrating genealogical and dynamical modelling to infer escape and reversion rates in HIV epitopes

2013 ◽  
Vol 280 (1762) ◽  
pp. 20130696 ◽  
Author(s):  
Duncan Palmer ◽  
John Frater ◽  
Rodney Phillips ◽  
Angela R. McLean ◽  
Gil McVean
Keyword(s):  
Cytotoxic T Lymphocyte ◽  
Evolutionary Process ◽  
Population Data ◽  
Integrated Approach ◽  
Accurate Estimate ◽  
Viral Escape ◽  
Host Immune System ◽  
Systematic Bias ◽  
Key Factors ◽  
Cross Sectional

The rates of escape and reversion in response to selection pressure arising from the host immune system, notably the cytotoxic T-lymphocyte (CTL) response, are key factors determining the evolution of HIV. Existing methods for estimating these parameters from cross-sectional population data using ordinary differential equations (ODEs) ignore information about the genealogy of sampled HIV sequences, which has the potential to cause systematic bias and overestimate certainty. Here, we describe an integrated approach, validated through extensive simulations, which combines genealogical inference and epidemiological modelling, to estimate rates of CTL escape and reversion in HIV epitopes. We show that there is substantial uncertainty about rates of viral escape and reversion from cross-sectional data, which arises from the inherent stochasticity in the evolutionary process. By application to empirical data, we find that point estimates of rates from a previously published ODE model and the integrated approach presented here are often similar, but can also differ several-fold depending on the structure of the genealogy. The model-based approach we apply provides a framework for the statistical analysis and hypothesis testing of escape and reversion in population data and highlights the need for longitudinal and denser cross-sectional sampling to enable accurate estimate of these key parameters.

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