scholarly journals A Multi-State Birth-Death model for Bayesian inference of lineage-specific birth and death rates

2018 ◽  
Author(s):  
Joëlle Barido-Sottani ◽  
Timothy G. Vaughan ◽  
Tanja Stadler
Keyword(s):  
Bayesian Inference ◽  
Phylogenetic Trees ◽  
Environmental Changes ◽  
Morphological Changes ◽  
Model Parameters ◽  
Death Rates ◽  
Joint Inference ◽  
Wide Range ◽  
Prior Hypothesis ◽  
Birth Death

AbstractHeterogeneous populations can lead to important differences in birth and death rates across a phylogeny Taking this heterogeneity into account is thus critical to obtain accurate estimates of the underlying population dynamics. We present a new multi-state birth-death model (MSBD) that can estimate lineage-specific birth and death rates. For species phylogenies, this corresponds to estimating lineage-dependent speciation and extinction rates. Contrary to existing models, we do not require a prior hypothesis on a trait driving the rate differences and we allow the same rates to be present in different parts of the phylogeny. Using simulated datasets, we show that the MSBD model can reliably infer the presence of multiple evolutionary regimes, their positions in the tree, and the birth and death rates associated with each. We also present a re-analysis of two empirical datasets and compare the results obtained by MSBD and by the existing software BAMM. The MSBD model is implemented as a package in the Bayesian inference software BEAST2, which allows joint inference of the phylogeny and the model parameters.Significance statementPhylogenetic trees can inform about the underlying speciation and extinction processes within a species clade. Many different factors, for instance environmental changes or morphological changes, can lead to differences in macroevolutionary dynamics within a clade. We present here a new multi-state birth-death (MSBD) model that can detect these differences and estimate both the position of changes in the tree and the associated macroevolutionary parameters. The MSBD model does not require a prior hypothesis on which trait is driving the changes in dynamics and is thus applicable to a wide range of datasets. It is implemented as an extension to the existing framework BEAST2.

scholarly journals A Multitype Birth–Death Model for Bayesian Inference of Lineage-Specific Birth and Death Rates

2020 ◽  
Vol 69 (5) ◽  
pp. 973-986 ◽  
Author(s):  
Joëlle Barido-Sottani ◽  
Timothy G Vaughan ◽  
Tanja Stadler
Keyword(s):  
Bayesian Inference ◽  
Simulated Data ◽  
Pathogen Transmission ◽  
Type Model ◽  
Model Parameters ◽  
Data Sets ◽  
Death Rates ◽  
Joint Inference ◽  
Extinction Rates ◽  
Birth Death

Abstract Heterogeneous populations can lead to important differences in birth and death rates across a phylogeny. Taking this heterogeneity into account is necessary to obtain accurate estimates of the underlying population dynamics. We present a new multitype birth–death model (MTBD) that can estimate lineage-specific birth and death rates. This corresponds to estimating lineage-dependent speciation and extinction rates for species phylogenies, and lineage-dependent transmission and recovery rates for pathogen transmission trees. In contrast with previous models, we do not presume to know the trait driving the rate differences, nor do we prohibit the same rates from appearing in different parts of the phylogeny. Using simulated data sets, we show that the MTBD model can reliably infer the presence of multiple evolutionary regimes, their positions in the tree, and the birth and death rates associated with each. We also present a reanalysis of two empirical data sets and compare the results obtained by MTBD and by the existing software BAMM. We compare two implementations of the model, one exact and one approximate (assuming that no rate changes occur in the extinct parts of the tree), and show that the approximation only slightly affects results. The MTBD model is implemented as a package in the Bayesian inference software BEAST 2 and allows joint inference of the phylogeny and the model parameters.[Birth–death; lineage specific rates, multi-type model.]

scholarly journals Estimating Epidemic Incidence and Prevalence from Genomic Data

2019 ◽  
Vol 36 (8) ◽  
pp. 1804-1816 ◽  
Author(s):  
Timothy G Vaughan ◽  
Gabriel E Leventhal ◽  
David A Rasmussen ◽  
Alexei J Drummond ◽  
David Welch ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Particle Filtering ◽  
Phylogenetic Trees ◽  
Stochastic Dynamics ◽  
Large Population ◽  
Simulated Data ◽  
Model Parameters ◽  
Epidemiological Models ◽  
Case Count ◽  
Birth Death

Abstract Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death, or behavior change). Birth–death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here, we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth–death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

scholarly journals Streaming stochastic variational Bayes; An improved approach for Bayesian inference with data streams

2019 ◽  
Author(s):  
Nadheesh Jihan ◽  
Malith Jayasinghe ◽  
Srinath Perera
Keyword(s):  
Online Learning ◽  
Bayesian Inference ◽  
Data Streams ◽  
Bayesian Updating ◽  
Variational Inference ◽  
Variational Bayes ◽  
Additional Analysis ◽  
Model Parameters ◽  
Posterior Variance ◽  
Wide Range

Online learning is an essential tool for predictive analysis based on continuous, endless data streams. Adopting Bayesian inference for online settings allows hierarchical modeling while representing the uncertainty of model parameters. Existing online inference techniques are motivated by either the traditional Bayesian updating or the stochastic optimizations. However, traditional Bayesian updating suffers from overconfidence posteriors, where posterior variance becomes too inadequate to adapt to new changes to the posterior. On the other hand, stochastic optimization of variational objective demands exhausting additional analysis to optimize a hyperparameter that controls the posterior variance. In this paper, we present ''Streaming Stochastic Variational Bayes" (SSVB)—a novel online approximation inference framework for data streaming to address the aforementioned shortcomings of the current state-of-the-art. SSVB adjusts its posterior variance duly without any user-specified hyperparameters while efficiently accommodating the drifting patterns to the posteriors. Moreover, SSVB can be easily adopted by practitioners for a wide range of models (i.e. simple regression models to complex hierarchical models) with little additional analysis. We appraised the performance of SSVB against Population Variational Inference (PVI), Stochastic Variational Inference (SVI) and Black-box Streaming Variational Bayes (BB-SVB) using two non-conjugate probabilistic models; multinomial logistic regression and linear mixed effect model. Furthermore, we also discuss the significant accuracy gain with SSVB based inference against conventional online learning models for each task.

scholarly journals Mistreating birth-death models as priors in phylogenetic analysis compromises our ability to compare models

2021 ◽  
Author(s):  
Michael R May ◽  
Carl Rothfels
Keyword(s):  
Prior Distribution ◽  
Phylogenetic Trees ◽  
Model Comparison ◽  
Marginal Likelihood ◽  
Death Process ◽  
Wide Range ◽  
Tree Models ◽  
Extinction Events ◽  
Lineage Diversification ◽  
Birth Death

Time-calibrated phylogenetic trees are fundamental to a wide range of evolutionary studies. Typically, these trees are inferred in a Bayesian framework, with the phylogeny itself treated as a parameter with a prior distribution (a "tree prior"). This prior distribution is often a variant of the stochastic birth-death process, which models speciation events, extinction events, and sampling events (of extinct and/or extant lineages). However, the samples produced by this process are observations, so their probability should be viewed as a likelihood rather than a prior probability. We show that treating the samples as part of the prior results in incorrect marginal likelihood estimates and can result in model-comparison approaches disfavoring the best model within a set of candidate models. The ability to correctly compare the fit of competing tree models is critical to accurate phylogenetic estimates, especially of divergence times, and also to studying the processes that govern lineage diversification. We outline potential remedies, and provide guidance for researchers interested in comparing the fit of competing tree models.

scholarly journals Bayesian inference of metabolic kinetics from genome-scale multiomics data

2018 ◽  
Author(s):  
Peter C. St. John ◽  
Jonathan Strutz ◽  
Linda J. Broadbelt ◽  
Keith E.J. Tyo ◽  
Yannick J. Bomble
Keyword(s):  
Steady State ◽  
Bayesian Inference ◽  
Kinetic Models ◽  
Large Scale ◽  
Biological Data ◽  
Model Parameters ◽  
Enzymatic Reactions ◽  
Rate Laws ◽  
Wide Range ◽  
Computationally Intensive

SummaryModern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, integrating these data sources to generate predictions of steady-state metabolism typically requires a kinetic description of the enzymatic reactions that occur within a cell. Parameterizing these kinetic models from biological data can be computationally difficult, especially as the amount of data increases. Robust methods must also be able to quantify the uncertainty in model parameters as a function of the available data, which can be particularly computationally intensive. The field of Bayesian inference offers a wide range of methods for estimating distributions in parameter uncertainty. However, these techniques are poorly suited to kinetic metabolic modeling due to the complex kinetic rate laws typically employed and the resulting dynamic system that must be solved. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and variational inference methods. We demonstrate that detailed information on the posterior distribution of kinetic model parameters can be obtained efficiently at a variety of different problem scales, including large-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and developing new, efficient strain designs.

scholarly journals Swapping Birth and Death: Symmetries and Transformations in Phylodynamic Models

2018 ◽  
Author(s):  
Tanja Stadler ◽  
Mike Steel
Keyword(s):  
Bayesian Inference ◽  
Maximum Likelihood ◽  
Death Rate ◽  
Evolutionary Trees ◽  
Rate Parameter ◽  
Death Rates ◽  
Fundamental Symmetries ◽  
Inference Methods ◽  
Curious Feature ◽  
Birth Death

AbstractStochastic birth–death models provide the foundation for studying and simulating evolutionary trees in phylodynamics. A curious feature of such models is that they exhibit fundamental symmetries when the birth and death rates are interchanged. In this paper, we explain and formally derive these transformational symmetries. We also show that these transformational symmetries (encoded in algebraic identities) are preserved even when taxa at the present are sampled with some probability. However, these extended symmetries require the death rate parameter to sometimes take a negative value. In the last part of this paper, we describe the relevance of these transformations and their application to computational phylodynamics, particularly to maximum likelihood and Bayesian inference methods, as well as to model selection. Phylodynamics, phylogenetics, speciation-extinction models, birth-death models, algebraic symmetries, maximum likelihood, Bayesian inference

scholarly journals Estimating epidemic incidence and prevalence from genomic data

2017 ◽  
Author(s):  
Timothy G. Vaughan ◽  
Gabriel E. Leventhal ◽  
David A. Rasmussen ◽  
Alexei J. Drummond ◽  
David Welch ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Particle Filtering ◽  
Phylogenetic Trees ◽  
Stochastic Dynamics ◽  
Large Population ◽  
Simulated Data ◽  
Model Parameters ◽  
Epidemiological Models ◽  
Case Count ◽  
Birth Death

AbstractModern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death or behaviour change). Birth-death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models, and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth-death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

scholarly journals Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models

2017 ◽  
Vol 14 (134) ◽  
pp. 20170340 ◽  
Author(s):  
Aidan C. Daly ◽  
Jonathan Cooper ◽  
David J. Gavaghan ◽  
Chris Holmes
Keyword(s):  
Bayesian Inference ◽  
Bayesian Methods ◽  
Sequential Monte Carlo ◽  
Model Parameters ◽  
Biological Models ◽  
Exact Inference ◽  
Modelling Studies ◽  
Approximate Bayesian ◽  
Approximate Bayesian Inference ◽  
Abc Methods

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.

scholarly journals Individual Expression of Hepatitis A Virus 3C Protease Induces Ferroptosis in Human Cells In Vitro

2021 ◽  
Vol 22 (15) ◽  
pp. 7906
Author(s):  
Alexey A. Komissarov ◽  
Maria A. Karaseva ◽  
Marina P. Roschina ◽  
Andrey V. Shubin ◽  
Nataliya A. Lunina ◽  
...  
Keyword(s):  
Cell Death ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Proteolytic Enzymes ◽  
Morphological Changes ◽  
Human Cells ◽  
Mitochondrial Potential ◽  
3C Protease ◽  
Wide Range

Regulated cell death (RCD) is a fundamental process common to nearly all living beings and essential for the development and tissue homeostasis in animals and humans. A wide range of molecules can induce RCD, including a number of viral proteolytic enzymes. To date, numerous data indicate that picornaviral 3C proteases can induce RCD. In most reported cases, these proteases induce classical caspase-dependent apoptosis. In contrast, the human hepatitis A virus 3C protease (3Cpro) has recently been shown to cause caspase-independent cell death accompanied by previously undescribed features. Here, we expressed 3Cpro in HEK293, HeLa, and A549 human cell lines to characterize 3Cpro-induced cell death morphologically and biochemically using flow cytometry and fluorescence microscopy. We found that dead cells demonstrated necrosis-like morphological changes including permeabilization of the plasma membrane, loss of mitochondrial potential, as well as mitochondria and nuclei swelling. Additionally, we showed that 3Cpro-induced cell death was efficiently blocked by ferroptosis inhibitors and was accompanied by intense lipid peroxidation. Taken together, these results indicate that 3Cpro induces ferroptosis upon its individual expression in human cells. This is the first demonstration that a proteolytic enzyme can induce ferroptosis, the recently discovered and actively studied type of RCD.

scholarly journals An Evaluation of Morphological Changes and Deformability of Suspended Red Blood Cells Prepared Using Whole Blood with Different Hemoglobin Levels of Tibetans

2021 ◽  
pp. 1-10
Author(s):  
Rui Zhong ◽  
Dingding Han ◽  
Xiaodong Wu ◽  
Hong Wang ◽  
Wanjing Li ◽  
...  
Keyword(s):  
Red Blood Cells ◽  
Whole Blood ◽  
Blood Cells ◽  
Morphological Changes ◽  
Osmotic Fragility ◽  
Spherical Shape ◽  
Hypoxic Environment ◽  
Wide Range ◽  
Group A ◽  
Cell Membrane Protein

Background: The hypoxic environment stimulates the human body to increase the levels of hemoglobin (HGB) and hematocrit and the number of red blood cells. Such enhancements have individual differences, leading to a wide range of HGB in Tibetans’ whole blood (WB). Study Design: WB of male Tibetans was divided into 3 groups according to different HGB (i.e., A: >120 but ≤185 g/L, B: >185 but ≤210 g/L, and C: >210 g/L). Suspended red blood cells (SRBC) processed by collected WB and stored in standard conditions were examined aseptically on days 1, 14, 21, and 35 after storage. The routine biochemical indexes, deformability, cell morphology, and membrane proteins were tested. Results: Mean corpuscular volume, adenosine triphosphate, pH, and deformability were not different in group A vs. those in storage (p > 0.05). The increased rate of irreversible morphology of red blood cells was different among the 3 groups, but there was no difference in the percentage of red blood cells with an irreversible morphology after 35 days of storage. Group C performed better in terms of osmotic fragility and showed a lower rigid index than group A. Furthermore, SDS-PAGE revealed similar cross-linking degrees of cell membrane protein but the band 3 protein of group C seemed to experience weaker clustering than that of group A as detected by Western Blot analysis after 35 days of storage. Conclusions: There was no difference in deformability or morphological changes in the 3 groups over the 35 days of storage. High HGB levels of plateau SRBC did not accelerate the RBC change from a biconcave disc into a spherical shape and it did not cause a reduction in deformability during 35 days of preservation in bank conditions.

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