Aspiration dynamics generate robust predictions in heterogeneous populations.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhou L
PROVIDER: S-EPMC8166829 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionTime-to-event data from clinical trials are routinely extrapolated using parametric models to estimate the cost effectiveness of novel therapies, but how this approach performs in the presence of heterogeneous populations remains unknown.MethodsWe performed a simulation study of seven scenarios with varying exponential distributions modelling treatment and prognostic effects across subgroup and complement populations, with follow-up typical of clinical trials used to appraise the cost effectiveness of therapies by agencies such as the UK National Institute for Health and Care Excellence (NICE). We compared established and emerging methods of estimating population life-years (LYs) using parametric models. We also proved analytically that an exponential model fitted to censored heterogeneous survival times sampled from two distinct exponential distributions will produce a biased estimate of the hazard rate and LYs.ResultsLYs are underestimated by the methods in the presence of heterogeneity, resulting in either under- or overestimation of the incremental benefit. In scenarios where the overestimation of benefit is likely, which is of interest to the healthcare provider, the method of taking the average LYs from all plausible models has the least bias. LY estimates from complete Kaplan-Meier curves have high variation, suggesting mature data may not be a reliable solution. We explore the effect of increasing trial sample size and accounting for detected treatment-subgroup interactions.ConclusionsThe bias associated with heterogeneous populations suggests that NICE may need to be more cautious when appraising therapies and to consider model averaging or the separate modelling of subgroups when heterogeneity is suspected or detected.
| S-EPMC8738626 | biostudies-literature
Project description:Individuals routinely differ in how they present with psychiatric illnesses and in how they respond to treatment. This heterogeneity, when overlooked in data analysis, can lead to misspecified models and distorted inferences. While several methods exist to handle various forms of heterogeneity in latent variable models, their implementation in applied research requires additional layers of model crafting, which might be a reason for their underutilization. In response, we present a robust estimation approach based on the expectation-maximization (EM) algorithm. Our method makes minor adjustments to EM to enable automatic detection of population heterogeneity and to recognize individuals who are inadequately explained by the assumed model. Each individual is associated with a probability that reflects how likely their data were to have been generated from the assumed model. The individual-level probabilities are simultaneously estimated and used to weight each individual's contribution in parameter estimation. We examine the utility of our approach for Gaussian mixture models and linear factor models through several simulation studies, drawing contrasts with the EM algorithm. We demonstrate that our method yields inferences more robust to population heterogeneity or other model misspecifications than EM does. We hope that the proposed approach can be incorporated into the model-building process to improve population-level estimates and to shed light on subsets of the population that demand further attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
| S-EPMC9035483 | biostudies-literature
Project description:Understanding stem cell (SC) population dynamics is essential for developing models that can be used in basic science and medicine, to aid in predicting cells fate. These models can be used as tools e.g. in studying patho-physiological events at the cellular and tissue level, predicting (mal)functions along the developmental course, and personalized regenerative medicine. Using time-lapsed imaging and statistical tools, we show that the dynamics of SC populations involve a heterogeneous structure consisting of multiple sub-population behaviors. Using non-Gaussian statistical approaches, we identify the co-existence of fast and slow dividing subpopulations, and quiescent cells, in stem cells from three species. The mathematical analysis also shows that, instead of developing independently, SCs exhibit a time-dependent fractal behavior as they interact with each other through molecular and tactile signals. These findings suggest that more sophisticated models of SC dynamics should view SC populations as a collective and avoid the simplifying homogeneity assumption by accounting for the presence of more than one dividing sub-population, and their multi-fractal characteristics.
| S-EPMC4001100 | biostudies-literature
Project description:Recent experimental work in the field of synthetic protocell biology has shown that prebiotic vesicles are able to 'steal' lipids from each other. This phenomenon is driven purely by asymmetries in the physical state or composition of the vesicle membranes, and, when lipid resource is limited, translates directly into competition amongst the vesicles. Such a scenario is interesting from an origins of life perspective because a rudimentary form of cell-level selection emerges. To sharpen intuition about possible mechanisms underlying this behaviour, experimental work must be complemented with theoretical modelling. The aim of this paper is to provide a coarse-grain mathematical model of protocell lipid competition. Our model is capable of reproducing, often quantitatively, results from core experimental papers that reported distinct types vesicle competition. Additionally, we make some predictions untested in the lab, and develop a general numerical method for quickly solving the equilibrium point of a model vesicle population.
| S-EPMC4097352 | biostudies-literature
Project description:Antigen presenting cells (APC) of the mononuclear phagocytic system include dendritic cells (DCs) and macrophages (Macs) which are essential mediators of innate and adaptive immune responses. Many of the biological functions attributed to these cell subsets have been elucidated using models that utilize in vitro-matured cells derived from common progenitors. However, it has recently been shown that monocyte culture systems generate heterogeneous populations of cells, DCs, and Macs. In light of these findings, we analyzed the most commonly used bovine in vitro-derived APC models and compared them to bona fide DCs. Here, we show that bovine monocyte-derived DCs and Macs can be differentiated on the basis of CD11c and MHC class II (MHCII) expression and that in vitro conditions generate a heterologous group of both DCs and Macs with defined and specific biological activities. In addition, skin-migrating macrophages present in the bovine afferent lymph were identified and phenotyped for the first time. RNA sequencing analyses showed that these monophagocytic cells have distinct transcriptomic profiles similar to those described in other species. These results have important implications for the interpretation of data obtained using in vitro systems.
| S-EPMC6450137 | biostudies-literature
Project description:Real populations have been shown to be heterogeneous, in which some individuals have many more contacts than others. This fact contrasts with the traditional homogeneous setting used in studies of evolutionary game dynamics. We incorporate heterogeneity in the population by studying games on graphs, in which the variability in connectivity ranges from single-scale graphs, for which heterogeneity is small and associated degree distributions exhibit a Gaussian tale, to scale-free graphs, for which heterogeneity is large with degree distributions exhibiting a power-law behavior. We study the evolution of cooperation, modeled in terms of the most popular dilemmas of cooperation. We show that, for all dilemmas, increasing heterogeneity favors the emergence of cooperation, such that long-term cooperative behavior easily resists short-term noncooperative behavior. Moreover, we show how cooperation depends on the intricate ties between individuals in scale-free populations.
| S-EPMC1413882 | biostudies-literature
Project description:MotivationIdentifying alterations in gene expression associated with different clinical states is important for the study of human biology. However, clinical samples used in gene expression studies are often derived from heterogeneous mixtures with variable cell-type composition, complicating statistical analysis. Considerable effort has been devoted to modeling sample heterogeneity, and presently, there are many methods that can estimate cell proportions or pure cell-type expression from mixture data. However, there is no method that comprehensively addresses mixture analysis in the context of differential expression without relying on additional proportion information, which can be inaccurate and is frequently unavailable.ResultsIn this study, we consider a clinically relevant situation where neither accurate proportion estimates nor pure cell expression is of direct interest, but where we are rather interested in detecting and interpreting relevant differential expression in mixture samples. We develop a method, Cell-type COmputational Differential Estimation (CellCODE), that addresses the specific statistical question directly, without requiring a physical model for mixture components. Our approach is based on latent variable analysis and is computationally transparent; it requires no additional experimental data, yet outperforms existing methods that use independent proportion measurements. CellCODE has few parameters that are robust and easy to interpret. The method can be used to track changes in proportion, improve power to detect differential expression and assign the differentially expressed genes to the correct cell type.
| S-EPMC4426841 | biostudies-literature
Project description:Dynamic RNA-protein interactions underpin numerous molecular control mechanisms in biology. However, relatively little is known about the kinetic landscape of protein interactions with full-length RNAs. The extent to which interaction kinetics vary for the same RNA element across the transcriptome and the molecular determinants of variability therefore remain poorly defined. Moreover, it is unclear how one protein-RNA interaction might be transduced by RNA to kinetically impact a second. We report a parallelized, real-time single-molecule fluorescence assay for protein interaction kinetics on eukaryotic mRNA populations obtained from cells. We observed ∼100-fold heterogeneity for interactions of the translation initiation factor eIF4E with the universal mRNA 5' cap structure, dominated by steric effects on barrier-height variability for association. We also found that an RNA helicase, eIF4A, independently accelerated eIF4E-cap association. These data support a kinetic mechanism for how mRNA can determine the sensitivity of its translation to reduction in cellular eIF4E concentrations. They also support the view that global RNA structure significantly modulates protein-RNA interaction dynamics and can facilitate real-time communication between protein interactions at distinct sites.
| S-EPMC8062131 | biostudies-literature
Project description:A key issue in drug development is to understand the hidden relationships among drugs and targets. Computational methods for novel drug target predictions can greatly reduce time and costs compared with experimental methods. In this paper, we propose a network based computational approach for novel drug and target association predictions. More specifically, a heterogeneous drug-target graph, which incorporates known drug-target interactions as well as drug-drug and target-target similarities, is first constructed. Based on this graph, a novel graph-based inference method is introduced. Compared with two state-of-the-art methods, large-scale cross-validation results indicate that the proposed method can greatly improve novel target predictions.
| S-EPMC3605000 | biostudies-literature
Project description:BackgroundIt is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation.ResultsWe incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset.ConclusionsWe propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation.
| S-EPMC6927100 | biostudies-literature