PLoS Computational Biology最新文献_第2页

Assessing microbiome engraftment extent following fecal microbiota transplant with q2-fmt. 利用q2-fmt评估粪便微生物群移植后微生物群的植入程度。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-31 DOI: 10.1371/journal.pcbi.1013299

Chloe Herman, Evan Bolyen, Anthony Simard, Liz Gehret, J Gregory Caporaso

引用次数: 0

Multitask deep learning for the emulation and calibration of an agent-based malaria transmission model. 基于智能体的疟疾传播模型的多任务深度学习仿真与标定。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-31 DOI: 10.1371/journal.pcbi.1013330

Agastya Mondal, Rushil Anirudh, Prashanth Selvaraj

引用次数: 0

Mathematical modelling of mechanotransduction via RhoA signalling pathways. 通过RhoA信号通路的机械转导的数学建模。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-31 DOI: 10.1371/journal.pcbi.1013305

Sofie Verhees, Chandrasekhar Venkataraman, Mariya Ptashnyk

引用次数: 0

CellMet: Extracting 3D shape and topology metrics from confluent cells within tissues. CellMet：从组织内的融合细胞中提取3D形状和拓扑指标。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-30 DOI: 10.1371/journal.pcbi.1013260

Sophie Theis, Mario A Mendieta-Serrano, Bernardo Chapa-Y-Lazo, Juliet Chen, Timothy E Saunders

{"title":"CellMet: Extracting 3D shape and topology metrics from confluent cells within tissues.","authors":"Sophie Theis, Mario A Mendieta-Serrano, Bernardo Chapa-Y-Lazo, Juliet Chen, Timothy E Saunders","doi":"10.1371/journal.pcbi.1013260","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1013260","url":null,"abstract":"During development and tissue repair, cells reshape and reconfigure to ensure organs take specific shapes. This process is inherently three-dimensional (3D). Yet, in part due to limitations in imaging and data analysis, cell shape analysis within tissues has largely been studied in two-dimensions (2D), e.g., the Drosophila wing disc. With recent advances in imaging and machine learning, there has been significant progress in our understanding of 3D cell and tissue shape in vivo. However, even after gaining 3D segmentation of cells, it remains challenging to extract cell shape metrics beyond volume and surface area for cells within densely packed tissues. To address the challenge of extracting 3D cell shape metrics from dense tissues, we have developed the Python package CellMet. This user-friendly tool enables extraction of quantitative shape information from 3D cell and tissue segmentations, including cell face properties, cell twist, and cell rearrangements in 3D. Our method will improve the analysis of 3D cell shape and the understanding of cell organisation within tissues. Our tool is open source, available at https://github.com/TimSaundersLab/CellMet.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013260"},"PeriodicalIF":3.6,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144754098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion. 决策准则中试对试波动的贝叶斯层次模型。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-29 DOI: 10.1371/journal.pcbi.1013291

Robin Vloeberghs, Anne E Urai, Kobe Desender, Scott W Linderman

{"title":"A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion.","authors":"Robin Vloeberghs, Anne E Urai, Kobe Desender, Scott W Linderman","doi":"10.1371/journal.pcbi.1013291","DOIUrl":"10.1371/journal.pcbi.1013291","url":null,"abstract":"Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations. Here, we introduce hMFC (Hierarchical Model for Fluctuations in Criterion), a Bayesian framework designed to estimate slow fluctuations in the decision criterion from limited data. We first showcase the importance of considering fluctuations in decision criterion: incorrectly assuming a stable criterion gives rise to apparent history effects and underestimates perceptual sensitivity. We then present a hierarchical estimation procedure capable of reliably recovering the underlying state of the fluctuating decision criterion with as few as 500 trials per participant, offering a robust tool for researchers with typical human datasets. Critically, hMFC does not only accurately recover the state of the underlying decision criterion, it also effectively deals with the confounds caused by criterion fluctuations. Lastly, we provide code and a comprehensive demo to enable widespread application of hMFC in decision-making research.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013291"},"PeriodicalIF":3.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144744414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions. PoweREST：用于检测两种情况下差异表达基因的空间转录组学实验的统计功率估计。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-29 eCollection Date: 2025-07-01 DOI: 10.1371/journal.pcbi.1013293

Lan Shui, Anirban Maitra, Ying Yuan, Ken Lau, Harsimran Kaur, Liang Li, Ziyi Li

{"title":"PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.","authors":"Lan Shui, Anirban Maitra, Ying Yuan, Ken Lau, Harsimran Kaur, Liang Li, Ziyi Li","doi":"10.1371/journal.pcbi.1013293","DOIUrl":"10.1371/journal.pcbi.1013293","url":null,"abstract":"Recent advancements in spatial transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost for current ST data generation techniques restricts the large-scale application of ST. Consequently, maximization of the use of available resources to achieve robust statistical power for ST data is a pressing need. One fundamental question in ST analysis is detection of differentially expressed genes (DEGs) under different conditions using ST data. Such DEG analyses are performed frequently, but their power calculations are rarely discussed in the literature. To address this gap, we developed PoweREST, a power estimation tool designed to support the power calculation for DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments and after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application that allows users to interactively calculate and visualize study power along with relevant parameters.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013293"},"PeriodicalIF":3.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12316394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144744416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DNFE: Directed network flow entropy for detecting tipping points during biological processes. 用于检测生物过程中临界点的定向网络流熵。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-29 eCollection Date: 2025-07-01 DOI: 10.1371/journal.pcbi.1013336

Xueqing Peng, Rui Qiao, Peiluan Li, Luonan Chen

{"title":"DNFE: Directed network flow entropy for detecting tipping points during biological processes.","authors":"Xueqing Peng, Rui Qiao, Peiluan Li, Luonan Chen","doi":"10.1371/journal.pcbi.1013336","DOIUrl":"10.1371/journal.pcbi.1013336","url":null,"abstract":"Typically, in dynamic biological processes, there is a critical state or tipping point that marks the transition from one stable state to another, surpassing which a considerable qualitative shift takes place. Identifying this tipping point and its driving network is essential to avert or delay disastrous outcomes. However, most traditional approaches built upon undirected networks still suffer from a lack of robustness and effectiveness when implemented based on high-dimensional small-sample data, especially for single-cell data. To address this challenge, we develop a directed network flow entropy (DNFE) method, which can transform measured omics data into a directed network. This method is applicable to both single-cell RNA-sequencing (scRNA-seq) and bulk data. Applying this algorithm to six real datasets, including three single-cell datasets, two bulk tumor datasets, and a blood dataset, the method is proved to be effective not only in identifying critical states, as well as their dynamic network biomarkers, but also in helping explore regulatory relationships between genes. Numerical simulation results demonstrate that the DNFE algorithm is robust across various noise levels and outperforms existing methods in detecting tipping points. Furthermore, the numerical simulations for 100-node and 1000-node gene regulatory networks illustrate the method's application for large-scale data. The DNFE method predicts active transcription factors, and further identified \"dark genes\", which are usually overlooked with traditional methods.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013336"},"PeriodicalIF":3.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12316398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144744415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling. Python包定量表型从化学暴露与基准剂量建模。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-28 eCollection Date: 2025-07-01 DOI: 10.1371/journal.pcbi.1013337

David J Degnan, Lisa M Bramer, Lisa Truong, Robyn L Tanguay, Sara M Gosline, Katrina M Waters

{"title":"bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling.","authors":"David J Degnan, Lisa M Bramer, Lisa Truong, Robyn L Tanguay, Sara M Gosline, Katrina M Waters","doi":"10.1371/journal.pcbi.1013337","DOIUrl":"10.1371/journal.pcbi.1013337","url":null,"abstract":"Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral data as proportions with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve (bmdrc) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals (https://srp.pnnl.gov). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013337"},"PeriodicalIF":3.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12313058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144732913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Algorithms to reconstruct past indels: The deletion-only parsimony problem. 重建过去索引的算法：只删除的简约问题。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-28 DOI: 10.1371/journal.pcbi.1012585

Jordan Moutet, Eric Rivals, Fabio Pardi

{"title":"Algorithms to reconstruct past indels: The deletion-only parsimony problem.","authors":"Jordan Moutet, Eric Rivals, Fabio Pardi","doi":"10.1371/journal.pcbi.1012585","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012585","url":null,"abstract":"Ancestral sequence reconstruction is an important task in bioinformatics, with applications ranging from protein engineering to the study of genome evolution. When sequences can only undergo substitutions, optimal reconstructions can be efficiently computed using well-known algorithms. However, accounting for indels in ancestral reconstructions is much harder. First, for biologically-relevant problem formulations, no polynomial-time exact algorithms are available. Second, multiple reconstructions are often equally parsimonious or likely, making it crucial to correctly display uncertainty in the results. Here, we consider a parsimony approach where only deletions are allowed, while addressing the aforementioned limitations. First, we describe an exact algorithm to obtain all the optimal solutions. The algorithm runs in polynomial time if only one solution is sought. Second, we show that all possible optimal reconstructions for a fixed node can be represented using a graph computable in polynomial time. While previous studies have proposed graph-based representations of ancestral reconstructions, this result is the first to offer a solid mathematical justification for this approach. Finally we provide arguments for the relevance of the deletion-only case for the general case.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1012585"},"PeriodicalIF":3.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144732912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recurrent neural networks as neuro-computational models of human speech recognition. 递归神经网络作为人类语音识别的神经计算模型。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-28 DOI: 10.1371/journal.pcbi.1013244

Christian Brodbeck, Thomas Hannagan, James S Magnuson

{"title":"Recurrent neural networks as neuro-computational models of human speech recognition.","authors":"Christian Brodbeck, Thomas Hannagan, James S Magnuson","doi":"10.1371/journal.pcbi.1013244","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1013244","url":null,"abstract":"Human speech recognition transforms a continuous acoustic signal into categorical linguistic units, by aggregating information that is distributed in time. It has been suggested that this kind of information processing may be understood through the computations of a Recurrent Neural Network (RNN) that receives input frame by frame, linearly in time, but builds an incremental representation of this input through a continually evolving internal state. While RNNs can simulate several key behavioral observations about human speech and language processing, it is unknown whether RNNs also develop computational dynamics that resemble human neural speech processing. Here we show that the internal dynamics of long short-term memory (LSTM) RNNs, trained to recognize speech from auditory spectrograms, predict human neural population responses to the same stimuli, beyond predictions from auditory features. Variations in the RNN architecture motivated by cognitive principles further improved this predictive power. Specifically, modifications that allow more human-like phonetic competition also led to more human-like temporal dynamics. Overall, our results suggest that RNNs provide plausible computational models of the cortical processes supporting human speech recognition.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013244"},"PeriodicalIF":3.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144732931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0