Bayesian Variable Selection with Joint Modeling of Categorical and Survival Outcomes: An Application to Individualizing Chemotherapy Treatment in Advanced Colorectal Cancer

Summary Colorectal cancer is the second leading cause of cancer related deaths in the United States, with more than 130,000 new cases of colorectal cancer diagnosed each year. Clinical studies have shown that genetic alterations lead to different responses to the same treatment, despite the morphologic similarities of tumors. A molecular test prior to treatment could help in determining an optimal treatment for a patient with regard to both toxicity and efficacy. This article introduces a statistical method appropriate for predicting and comparing multiple endpoints given different treatment options and molecular profiles of an individual. A latent variable‐based multivariate regression model with structured variance covariance matrix is considered here. The latent variables account for the correlated nature of multiple endpoints and accommodate the fact that some clinical endpoints are categorical variables and others are censored variables. The mixture normal hierarchical structure admits a natural variable selection rule. Inference was conducted using the posterior distribution sampling Markov chain Monte Carlo method. We analyzed the finite‐sample properties of the proposed method using simulation studies. The application to the advanced colorectal cancer study revealed associations between multiple endpoints and particular biomarkers, demonstrating the potential of individualizing treatment based on genetic profiles.     

Modeling tails for collinear data with outliers in the English Longitudinal Study of Ageing: Quantile profile regression

Research has shown that high blood glucose levels are important predictors of incident diabetes. However, they are also strongly associated with other cardiometabolic risk factors such as high blood pressure, adiposity, and cholesterol, which are also highly correlated with one another. The aim of this analysis was to ascertain how these highly correlated cardiometabolic risk factors might be associated with high levels of blood glucose in older adults aged 50 or older from wave 2 of the English Longitudinal Study of Ageing (ELSA). Due to the high collinearity of predictor variables and our interest in extreme values of blood glucose we proposed a new method, called quantile profile regression, to answer this question. Profile regression, a Bayesian nonparametric model for clustering responses and covariates simultaneously, is a powerful tool to model the relationship between a response variable and covariates, but the standard approach of using a mixture of Gaussian distributions for the response model will not identify the underlying clusters correctly, particularly with outliers in the data or heavy tail distribution of the response. Therefore, we propose quantile profile regression to model the response variable with an asymmetric Laplace distribution, allowing us to model more accurately clusters that are asymmetric and predict more accurately for extreme values of the response variable and/or outliers. Our new method performs more accurately in simulations when compared to Normal profile regression approach as well as robustly when outliers are present in the data. We conclude with an analysis of the ELSA.     

Bait-ER: A Bayesian method to detect targets of selection in Evolve-and-Resequence experiments

For over a decade, experimental evolution has been combined with high-throughput sequencing techniques. In so-called Evolve-and-Resequence (E&R) experiments, populations are kept in the laboratory under controlled experimental conditions where their genomes are sampled and allele frequencies monitored. However, identifying signatures of adaptation in E&R datasets is far from trivial, and it is still necessary to develop more efficient and statistically sound methods for detecting selection in genome-wide data. Here, we present Bait-ER – a fully Bayesian approach based on the Moran model of allele evolution to estimate selection coefficients from E&R experiments. The model has overlapping generations, a feature that describes several experimental designs found in the literature. We tested our method under several different demographic and experimental conditions to assess its accuracy and precision, and it performs well in most scenarios. Nevertheless, some care must be taken when analysing trajectories where drift largely dominates and starting frequencies are low. We compare our method with other available software and report that ours has generally high accuracy even for trajectories whose complexity goes beyond a classical sweep model. Furthermore, our approach avoids the computational burden of simulating an empirical null distribution, outperforming available software in terms of computational time and facilitating its use on genome-wide data. We implemented and released our method in a new open-source software package that can be accessed at https://doi.org/10.5281/zenodo.7351736 .     

Generalization in Ecology and Evolutionary Biology: From Hypothesis to Paradigm

We argue that broad, simplegeneralizations, not specifically linked tocontingencies, will rarely approach truth in ecologyand evolutionary biology. This is because mostinteresting phenomena have multiple, interactingcauses. Instead of looking for single universaltheories to explain the great diversity of naturalsystems, we suggest that it would be profitable todevelop general explanatory frameworks. A frameworkshould clearly specify focal levels. The process orpattern that we wish to study defines our level offocus. The set of potential and actual states at thefocal level interacts with conditions at thecontiguous lower and upper levels of organization,through sets of many-to-one and one-to-manyconnections. The number of initiating conditions andtheir permutations at the lower level define thepotential states at the focal level, whereas theactual state is constrained by the upper-levelboundary conditions. The most useful generalizationsare explanatory frameworks, which are road maps tosolutions, rather than solutions themselves. Suchframeworks outline what is understood about boundaryconditions and initiating conditions so that aninvestigator can pick and choose what is required toeffectively understand a specific event or situation. We discuss these relationships in terms of examplesinvolving sex ratio and mating behavior, competitivehierarchies, insect life-histories and the evolutionof sex.     

Modeling Complex Phenotypes: Generalized Linear Models Using Spectrogram Predictors of Animal Communication Signals

Summary A major goal of evolutionary biology is to understand the dynamics of natural selection within populations. The strength and direction of selection can be described by regressing relative fitness measurements on organismal traits of ecological significance. However, many important evolutionary characteristics of organisms are complex, and have correspondingly complex relationships to fitness. Secondary sexual characteristics such as mating displays are prime examples of complex traits with important consequences for reproductive success. Typically, researchers atomize sexual traits such as mating signals into a set of measurements including pitch and duration, in order to include them in a statistical analysis. However, these researcher‐defined measurements are unlikely to capture all of the relevant phenotypic variation, especially when the sources of selection are incompletely known. In order to accommodate this complexity we propose a Bayesian dimension‐reduced spectrogram generalized linear model that directly incorporates representations of the entire phenotype (one‐dimensional acoustic signal) into the model as a predictor while accounting for multiple sources of uncertainty. The first stage of dimension reduction is achieved by treating the spectrogram as an “image” and finding its corresponding empirical orthogonal functions. Subsequently, further dimension reduction is accomplished through model selection using stochastic search variable selection. Thus, the model we develop characterizes key aspects of the acoustic signal that influence sexual selection while alleviating the need to extract higher‐level signal traits a priori. This facet of our approach is fundamental and has the potential to provide additional biological insight, as is illustrated in our analysis.     

Dynamics in a discrete two-species competition model: coexistence and over-compensation

The dynamic features of an over-compensating discrete two-species competition system with stable coexistence are recaptured, and it is shown how the probabilities of the different possible ecological scenarios, e.g. coexistence, may be calculated when the assumption of no over-compensation is loosened. A Bayesian methodology for calculating the probability that stable oscillations or chaos may occur in plant populations or communities is outlined. The methodology is exemplified using an experimental population of Arabidopsis thaliana. It is concluded that, when making ecological predictions it is preferable and possibly important to test for the possibility of chaotic population dynamics due to over-compensation rather than assuming a priori that over-compensation does not occur.     

Improving Risk Assessment: Research Opportunities in Dose Response Modeling to Improve Risk Assessment

Substantial improvements in dose response modeling for risk assessment may result from recent and continuing advances in biological research, biochemical techniques, biostatistical/mathematical methods and computational power. This report provides a ranked set of recommendations for proposed research to advance the state of the art in dose response modeling. The report is the result of a meeting of invited workgroup participants charged with identifying five areas of research in dose response modeling that could be incorporated in a national agenda to improve risk assessment methods. Leading topics of emphasis are interindividual variability, injury risk assessment modeling, and procedures to incorporate distributional methods and mechanistic considerations into now-standard methods of deriving a reference dose (RfD), reference concentration (RfC), minimum risk level (MRL) or similar dose-response parameter estimates.     

Quantifying the Community: Using Bayesian Learning Networks to find Structure and Conduct Inference in Invasions Biology

One of the key obstacles to better understanding, anticipating, and managing biological invasions is the difficulty researchers face when trying to quantify the many important aspects of the communities that affect and are affected by non-indigenous species (NIS). Bayesian Learning Networks (BLNs) combine graphical models with multivariate Bayesian statistics to provide an analytical tool for the quantification of communities. BLNs can determine which components of a natural system influence which others, quantify this influence, and provide inferential analysis of parameter changes when changes in network variables are hypothesized or observed. After a brief explanation of these three functions of BLNs, a simulated network is analyzed for structure, parameter estimation, and inference. Discussion of this approach to invasions biology is explored and expanded applications for BLNs are then offered.     

Continuous Covariates in Mark‐Recapture‐Recovery Analysis: A Comparison of Methods

Summary Time varying, individual covariates are problematic in experiments with marked animals because the covariate can typically only be observed when each animal is captured. We examine three methods to incorporate time varying, individual covariates of the survival probabilities into the analysis of data from mark‐recapture‐recovery experiments: deterministic imputation, a Bayesian imputation approach based on modeling the joint distribution of the covariate and the capture history, and a conditional approach considering only the events for which the associated covariate data are completely observed (the trinomial model). After describing the three methods, we compare results from their application to the analysis of the effect of body mass on the survival of Soay sheep (Ovis aries) on the Isle of Hirta, Scotland. Simulations based on these results are then used to make further comparisons. We conclude that both the trinomial model and Bayesian imputation method perform best in different situations. If the capture and recovery probabilities are all high, then the trinomial model produces precise, unbiased estimators that do not depend on any assumptions regarding the distribution of the covariate. In contrast, the Bayesian imputation method performs substantially better when capture and recovery probabilities are low, provided that the specified model of the covariate is a good approximation to the true data‐generating mechanism.     

From mosaic to patchwork: Matching lipids and proteins in membrane organization

Abstract

Biological membranes encompass and compartmentalize cells and organelles and are a prerequisite to life as we know it. One defining feature of membranes is an astonishing diversity of building blocks. The mechanisms and principles organizing the thousands of proteins and lipids that make up membrane bilayers in cells are still under debate. Many terms and mechanisms have been introduced over the years to account for certain phenomena and aspects of membrane organization and function. Recently, the different viewpoints – focusing on lipids vs. proteins or physical vs. molecular driving forces for membrane organization – are increasingly converging. Here we review the basic properties of biological membranes and the most common theories for lateral segregation of membrane components before discussing an emerging model of a self-organized, multi-domain membrane or ‘patchwork membrane'.     

A Bootstrap-Based Covariate Selection Method for Modeling the Risk of Lightning-Induced Fires at a Local Scale: A Case Study in Northwest Spain

In lightning-induced fire risk prediction models, the number of potential predictors is usually high, with some redundancy among them. It is therefore important to select the best subset of predictors that obtain models with the greatest discrimination capacity. With this aim in mind, the logistic generalized linear model was used to estimate lightning-induced fire occurrence using a case study of the province of León (northwest Spain). A bootstrap-based test was used to obtain the optimal number of predictors and to model this optimal number of predictors displaying the largest area under the receiver operating characteristics curve. The results show that of the 16 variables initially considered, only three were necessary to obtain the model with the best discriminatory capacity for estimating lightning-induced fire occurrence. Moreover, this model can be considered equivalent to another nine alternative models with three covariates. Both the optimal and the equivalent models are useful in the spatially explicit assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and the design of long-term wildfire management strategies. The methodology used for this case study can be applied to other wildfire risk assessment situations where multiple and interconnected covariates are available.     

From fluorescence polarization to Quenchbody: Recent progress in fluorescent reagentless biosensors based on antibody and other binding proteins

Recently, antibody-based fluorescent biosensors are receiving considerable attention as a suitable biomolecule for diagnostics, namely, homogeneous immunoassay and also as an imaging probe. To date, several strategies for “reagentless biosensors” based on antibodies and natural and engineered binding proteins have been described. In this review, several approaches are introduced including a recently described fluorescent antibody-based biosensor Quenchbody, which works on the principle of fluorescence quenching of attached dye and its antigen-dependent release. The merits and possible demerits of each approach are discussed. This article is part of a Special Issue entitled: Recent advances in molecular engineering of antibody.     

Recent advances in features generation for membrane protein sequences: From multiple sequence alignment to pre-trained language models

Membrane proteins play a crucial role in various cellular processes and are essential components of cell membranes. Computational methods have emerged as a powerful tool for studying membrane proteins due to their complex structures and properties that make them difficult to analyze experimentally. Traditional features for protein sequence analysis based on amino acid types, composition, and pair composition have limitations in capturing higher-order sequence patterns. Recently, multiple sequence alignment (MSA) and pre-trained language models (PLMs) have been used to generate features from protein sequences. However, the significant computational resources required for MSA-based features generation can be a major bottleneck for many applications. Several methods and tools have been developed to accelerate the generation of MSAs and reduce their computational cost, including heuristics and approximate algorithms. Additionally, the use of PLMs such as BERT has shown great potential in generating informative embeddings for protein sequence analysis. In this review, we provide an overview of traditional and more recent methods for generating features from protein sequences, with a particular focus on MSAs and PLMs. We highlight the advantages and limitations of these approaches and discuss the methods and tools developed to address the computational challenges associated with features generation. Overall, the advancements in computational methods and tools provide a promising avenue for gaining deeper insights into the function and properties of membrane proteins, which can have significant implications in drug discovery and personalized medicine.     

Physiology in ecological niche modeling: using zebra mussel's upper thermal tolerance to refine model predictions through Bayesian analysis

Climate change and human-mediated dispersal are increasingly influencing species’ geographic distributions. Ecological niche models (ENMs) are widely used in forecasting species’ distributions, but are weak in extrapolation to novel environments because they rely on available distributional data and do not incorporate mechanistic information, such as species’ physiological response to abiotic conditions. To improve accuracy of ENMs, we incorporated physiological knowledge through Bayesian analysis. In a case study of the zebra mussel Dreissena polymorpha, we used native and global occurrences to obtain native and global models representing narrower and broader understanding of zebra mussel’ response to temperature. We also obtained thermal limit and survival information for zebra mussel from peer-reviewed literature and used the two types of information separately and jointly to calibrate native models. We showed that, compared to global models, native models predicted lower relative probability of presence along zebra mussel's upper thermal limit, suggesting the shortcoming of native models in predicting zebra mussel's response to warm temperature. We also found that native models showed improved prediction of relative probability of presence when thermal limit was used alone, and best approximated global models when both thermal limit and survival data were used. Our result suggests that integration of physiological knowledge enhances extrapolation of ENM in novel environments. Our modeling framework can be generalized for other species or other physiological limits and may incorporate evolutionary information (e.g. evolved thermal tolerance), thus has the potential to improve predictions of species’ invasive potential and distributional response to climate change.     

Heat over heritability: Increasing body size in response to global warming is not stabilized by genetic effects in Bechstein's bats

How well populations can cope with global warming will often depend on the evolutionary potential and plasticity of their temperature-sensitive, fitness-relevant traits. In Bechstein's bats (Myotis bechsteinii), body size has increased over the last decades in response to warmer summers. If this trend continues it may threaten populations as larger females exhibit higher mortality. To assess the evolutionary potential of body size, we applied a Bayesian ‘animal model’ to estimate additive genetic variance, heritability and evolvability of body size, based on a 25-year pedigree of 332 wild females. Both heritability and additive genetic variance were reduced in hot summers compared to average and cold summers, while evolvability of body size was generally low. This suggests that the observed increase in body size was mostly driven by phenotypic plasticity. Thus, if warm summers continue to become more frequent, body size likely increases further and the resulting fitness loss could threaten populations.     

Dynamics of age- and size-structured populations in fluctuating environments: Applications of stochastic matrix models to natural populations

Recent developments of the theory of stochastic matrix modeling have made it possible to estimate general properties of age- and size-structured populations in fluctuating environments. However, applications of the theory to natural populations are still few. The empirical studies which have used stochastic matrix models are reviewed here to examine whether predictions made by the theory can be generally found in wild populations. The organisms studied include terrestrial grasses and herbs, a seaweed, a fish, a reptile, a deer and some marine invertebrates. In all the studies, the stochastic population growth rate (ln λ _s ) was no greater than the deterministic population growth rate determined using average vital rates, suggesting that the model based only on average vital rates may overestimate growth rates of populations in fluctuating environments. Factors affecting ln λ _s include the magnitude of variation in vital rates, probability distribution of random environments, fluctuation in different types of vital rates, covariances between vital rates, and autocorrelation between successive environments. However, comprehensive rules were hardly found through the comparisons of the empirical studies. Based on shortcomings of previous studies, I address some important subjects which should be examined in future studies.