PLoS Computational Biology最新文献_第3页

StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling StellarPath：层次-垂直多组学分类器协同稳定标记物和可解释的相似性网络，用于患者特征分析

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2024-04-01 DOI: 10.1371/journal.pcbi.1012022

Luca Giudice, Ahmed Mohamed, T. Malm

{"title":"StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling","authors":"Luca Giudice, Ahmed Mohamed, T. Malm","doi":"10.1371/journal.pcbi.1012022","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012022","url":null,"abstract":"The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients’ relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network’s topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm’s inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients’ pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"46 43","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140796068","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

Optimizing strategies for slowing the spread of invasive species 优化减缓入侵物种扩散的战略

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2024-04-01 DOI: 10.1371/journal.pcbi.1011996

Adam Lampert

{"title":"Optimizing strategies for slowing the spread of invasive species","authors":"Adam Lampert","doi":"10.1371/journal.pcbi.1011996","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1011996","url":null,"abstract":"Invasive species are spreading worldwide, causing damage to ecosystems, biodiversity, agriculture, and human health. A major question is, therefore, how to distribute treatment efforts cost-effectively across space and time to prevent or slow the spread of invasive species. However, finding optimal control strategies for the complex spatial-temporal dynamics of populations is complicated and requires novel methodologies. Here, we develop a novel algorithm that can be applied to various population models. The algorithm finds the optimal spatial distribution of treatment efforts and the optimal propagation speed of the target species. We apply the algorithm to examine how the results depend on the species’ demography and response to the treatment method. In particular, we analyze (1) a generic model and (2) a detailed model for the management of the spongy moth in North America to slow its spread via mating disruption. We show that, when utilizing optimization approaches to contain invasive species, significant improvements can be made in terms of cost-efficiency. The methodology developed here offers a much-needed tool for further examination of optimal strategies for additional cases of interest.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"34 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140782103","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

Indirect reciprocity with Bayesian reasoning and biases 间接互惠与贝叶斯推理和偏见

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2024-04-01 DOI: 10.1371/journal.pcbi.1011979

Bryce Morsky, Joshua B Plotkin, Erol Akçay

{"title":"Indirect reciprocity with Bayesian reasoning and biases","authors":"Bryce Morsky, Joshua B Plotkin, Erol Akçay","doi":"10.1371/journal.pcbi.1011979","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1011979","url":null,"abstract":"Reputations can foster cooperation by indirect reciprocity: if I am good to you then others will be good to me. But this mechanism for cooperation in one-shot interactions only works when people agree on who is good and who is bad. Errors in actions or assessments can produce disagreements about reputations, which can unravel the positive feedback loop between social standing and pro-social behaviour. Cooperators can end up punished and defectors rewarded. Public reputation systems and empathy are two possible mechanisms to promote agreement about reputations. Here we suggest an alternative: Bayesian reasoning by observers. By taking into account the probabilities of errors in action and observation and their prior beliefs about the prevalence of good people in the population, observers can use Bayesian reasoning to determine whether or not someone is good. To study this scenario, we develop an evolutionary game theoretical model in which players use Bayesian reasoning to assess reputations, either publicly or privately. We explore this model analytically and numerically for five social norms (Scoring, Shunning, Simple Standing, Staying, and Stern Judging). We systematically compare results to the case when agents do not use reasoning in determining reputations. We find that Bayesian reasoning reduces cooperation relative to non-reasoning, except in the case of the Scoring norm. Under Scoring, Bayesian reasoning can promote coexistence of three strategic types. Additionally, we study the effects of optimistic or pessimistic biases in individual beliefs about the degree of cooperation in the population. We find that optimism generally undermines cooperation whereas pessimism can, in some cases, promote cooperation.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"407 24","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140791391","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

DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction DBDNMF：用于药物反应预测的双分支深度神经矩阵因式分解方法

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2024-04-01 DOI: 10.1371/journal.pcbi.1012012

Hui Liu, Feng Wang, Jian Yu, Yong Pan, Chaoju Gong, Liang Zhang, Lin Zhang

{"title":"DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction","authors":"Hui Liu, Feng Wang, Jian Yu, Yong Pan, Chaoju Gong, Liang Zhang, Lin Zhang","doi":"10.1371/journal.pcbi.1012012","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012012","url":null,"abstract":"Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computational models that can precisely predict the responses between drugs and cell lines could provide a credible reference for further research. Existing methods of response prediction based on matrix factorization or neural networks have revealed that both linear or nonlinear latent characteristics are applicable and effective for the precise prediction of drug responses. However, the majority of them consider only linear or nonlinear relationships for drug response prediction. Herein, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to address the above-mentioned issues. DBDNMF learns the latent representation of drugs and cell lines through flexible inputs and reconstructs the partially observed matrix through a series of hidden neural network layers. Experimental results on the datasets of Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) show that the accuracy of drug prediction exceeds state-of-the-art drug response prediction algorithms, demonstrating its reliability and stability. The hierarchical clustering results show that drugs with similar response levels tend to target similar signaling pathway, and cell lines coming from the same tissue subtype tend to share the same pattern of response, which are consistent with previously published studies.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"112 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140790789","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

Mathematical model for the role of multiple pericentromeric repeats on heterochromatin assembly 多中心染色体周围重复序列对异染色质组装作用的数学模型

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2024-04-01 DOI: 10.1371/journal.pcbi.1012027

P. Ghimire, Mo Motamedi, Richard Joh

{"title":"Mathematical model for the role of multiple pericentromeric repeats on heterochromatin assembly","authors":"P. Ghimire, Mo Motamedi, Richard Joh","doi":"10.1371/journal.pcbi.1012027","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012027","url":null,"abstract":"Although the length and constituting sequences for pericentromeric repeats are highly variable across eukaryotes, the presence of multiple pericentromeric repeats is one of the conserved features of the eukaryotic chromosomes. Pericentromeric heterochromatin is often misregulated in human diseases, with the expansion of pericentromeric repeats in human solid cancers. In this article, we have developed a mathematical model of the RNAi-dependent methylation of H3K9 in the pericentromeric region of fission yeast. Our model, which takes copy number as an explicit parameter, predicts that the pericentromere is silenced only if there are many copies of repeats. It becomes bistable or desilenced if the copy number of repeats is reduced. This suggests that the copy number of pericentromeric repeats alone can determine the fate of heterochromatin silencing in fission yeast. Through sensitivity analysis, we identified parameters that favor bistability and desilencing. Stochastic simulation shows that faster cell division and noise favor the desilenced state. These results show the unexpected role of pericentromeric repeat copy number in gene silencing and provide a quantitative basis for how the copy number allows or protects repetitive and unique parts of the genome from heterochromatin silencing, respectively.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"61 14","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140795375","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

Ten simple rules for leading a successful undergraduate-intensive research lab 成功领导本科生密集型研究实验室的十条简单规则

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2024-04-01 DOI: 10.1371/journal.pcbi.1011994

Kje Hickman, Geoffrey L. Zahn

{"title":"Ten simple rules for leading a successful undergraduate-intensive research lab","authors":"Kje Hickman, Geoffrey L. Zahn","doi":"10.1371/journal.pcbi.1011994","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1011994","url":null,"abstract":"Participating in mentored research is an enormous benefit to undergraduate students. These immersive experiences can dramatically improve retention and completion rates, especially for students from traditionally underserved populations in STEM disciplines. Scientists typically do not receive any formal training in management or group dynamics before taking on the role of a lab head. Thus, peer forums and shared wisdom are crucial for developing the vision and skills involved with mentorship and leading a successful research lab. Faculty at any institution can help improve student outcomes and the success of their labs by thoughtfully including undergraduates in their research programs. Moreover, faculty at primarily undergraduate institutions have special challenges that are not often acknowledged or addressed in public discussions about best practices for running a lab. Here, we present 10 simple rules for fostering a successful undergraduate research lab. While much of the advice herein is applicable to mentoring undergraduates in any setting, it is especially tailored to the special circumstances found at primarily undergraduate institutions.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"64 10","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140791270","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 probabilistic knowledge graph for target identification 用于目标识别的概率知识图谱

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2024-04-01 DOI: 10.1371/journal.pcbi.1011945

Chang Liu, Kaimin Xiao, Cuinan Yu, Yipin Lei, Kangbo Lyu, Tingzhong Tian, Dan Zhao, Fengfeng Zhou, Haidong Tang, Jianyang Zeng

{"title":"A probabilistic knowledge graph for target identification","authors":"Chang Liu, Kaimin Xiao, Cuinan Yu, Yipin Lei, Kangbo Lyu, Tingzhong Tian, Dan Zhao, Fengfeng Zhou, Haidong Tang, Jianyang Zeng","doi":"10.1371/journal.pcbi.1011945","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1011945","url":null,"abstract":"Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"47 52","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140795923","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

Recombulator-X: A fast and user-friendly tool for estimating X chromosome recombination rates in forensic genetics. Recombulator-X：一种快速且用户友好的工具，用于估计法医遗传学中X染色体重组率。

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2023-09-18 eCollection Date: 2023-09-01 DOI: 10.1371/journal.pcbi.1011474

Serena Aneli, Piero Fariselli, Elena Chierto, Carla Bini, Carlo Robino, Giovanni Birolo

{"title":"Recombulator-X: A fast and user-friendly tool for estimating X chromosome recombination rates in forensic genetics.","authors":"Serena Aneli, Piero Fariselli, Elena Chierto, Carla Bini, Carlo Robino, Giovanni Birolo","doi":"10.1371/journal.pcbi.1011474","DOIUrl":"10.1371/journal.pcbi.1011474","url":null,"abstract":"Genetic markers (especially short tandem repeats or STRs) located on the X chromosome are a valuable resource to solve complex kinship cases in forensic genetics in addition or alternatively to autosomal STRs. Groups of tightly linked markers are combined into haplotypes, thus increasing the discriminating power of tests. However, this approach requires precise knowledge of the recombination rates between adjacent markers. The International Society of Forensic Genetics recommends that recombination rate estimation on the X chromosome is performed from pedigree genetic data while taking into account the confounding effect of mutations. However, implementations that satisfy these requirements have several drawbacks: they were never publicly released, they are very slow and/or need cluster-level hardware and strong computational expertise to use. In order to address these key concerns we developed Recombulator-X, a new open-source Python tool. The most challenging issue, namely the running time, was addressed with dynamic programming techniques to greatly reduce the computational complexity of the algorithm. Compared to the previous methods, Recombulator-X reduces the estimation times from weeks or months to less than one hour for typical datasets. Moreover, the estimation process, including preprocessing, has been streamlined and packaged into a simple command-line tool that can be run on a normal PC. Where previous approaches were limited to small panels of STR markers (up to 15), our tool can handle greater numbers (up to 100) of mixed STR and non-STR markers. In conclusion, Recombulator-X makes the estimation process much simpler, faster and accessible to researchers without a computational background, hopefully spurring increased adoption of best practices.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"19 9","pages":"e1011474"},"PeriodicalIF":4.3,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10538763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10673011","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

Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study. 公共卫生措施的边际效应与中国新冠肺炎疾病负担：一项大型模型研究。

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2023-09-18 eCollection Date: 2023-09-01 DOI: 10.1371/journal.pcbi.1011492

Zengmiao Wang, Peiyi Wu, Lin Wang, Bingying Li, Yonghong Liu, Yuxi Ge, Ruixue Wang, Ligui Wang, Hua Tan, Chieh-Hsi Wu, Marko Laine, Henrik Salje, Hongbin Song

{"title":"Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study.","authors":"Zengmiao Wang, Peiyi Wu, Lin Wang, Bingying Li, Yonghong Liu, Yuxi Ge, Ruixue Wang, Ligui Wang, Hua Tan, Chieh-Hsi Wu, Marko Laine, Henrik Salje, Hongbin Song","doi":"10.1371/journal.pcbi.1011492","DOIUrl":"10.1371/journal.pcbi.1011492","url":null,"abstract":"China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73-38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70-76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89-2.92) million hospital admissions and 0.16 (95% CI, 0.13-0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"19 9","pages":"e1011492"},"PeriodicalIF":4.3,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10538769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10361477","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

Human-environment feedback and the consistency of proenvironmental behavior. 人类环境反馈和环保行为的一致性。

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2023-09-18 eCollection Date: 2023-09-01 DOI: 10.1371/journal.pcbi.1011429

Claire Ecotière, Sylvain Billiard, Jean-Baptiste André, Pierre Collet, Régis Ferrière, Sylvie Méléard

{"title":"Human-environment feedback and the consistency of proenvironmental behavior.","authors":"Claire Ecotière, Sylvain Billiard, Jean-Baptiste André, Pierre Collet, Régis Ferrière, Sylvie Méléard","doi":"10.1371/journal.pcbi.1011429","DOIUrl":"10.1371/journal.pcbi.1011429","url":null,"abstract":"Addressing global environmental crises such as anthropogenic climate change requires the consistent adoption of proenvironmental behavior by a large part of a population. Here, we develop a mathematical model of a simple behavior-environment feedback loop to ask how the individual assessment of the environmental state combines with social interactions to influence the consistent adoption of proenvironmental behavior, and how this feeds back to the perceived environmental state. In this stochastic individual-based model, individuals can switch between two behaviors, 'active' (or actively proenvironmental) and 'baseline', differing in their perceived cost (higher for the active behavior) and environmental impact (lower for the active behavior). We show that the deterministic dynamics and the stochastic fluctuations of the system can be approximated by ordinary differential equations and a Ornstein-Uhlenbeck type process. By definition, the proenvironmental behavior is adopted consistently when, at population stationary state, its frequency is high and random fluctuations in frequency are small. We find that the combination of social and environmental feedbacks can promote the spread of costly proenvironmental behavior when neither, operating in isolation, would. To be adopted consistently, strong social pressure for proenvironmental action is necessary but not sufficient-social interactions must occur on a faster timescale compared to individual assessment, and the difference in environmental impact must be small. This simple model suggests a scenario to achieve large reductions in environmental impact, which involves incrementally more active and potentially more costly behavior being consistently adopted under increasing social pressure for proenvironmentalism.","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"19 9","pages":"e1011429"},"PeriodicalIF":4.3,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10538744/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10310578","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