PLoS Computational Biology最新文献_第9页

PEtab Select: Specification standard and supporting software for automated model selection. PEtab Select：用于自动模型选择的规范标准和支持软件。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-30 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014774

Dilan Pathirana, Frank T Bergmann, Domagoj Doresic, Polina Lakrisenko, Sebastian Persson, Niklas Neubrand, Jens Timmer, Clemens Kreutz, Harald Binder, Marija Cvijovic, Daniel Weindl, Jan Hasenauer

{"title":"PEtab Select: Specification standard and supporting software for automated model selection.","authors":"Dilan Pathirana, Frank T Bergmann, Domagoj Doresic, Polina Lakrisenko, Sebastian Persson, Niklas Neubrand, Jens Timmer, Clemens Kreutz, Harald Binder, Marija Cvijovic, Daniel Weindl, Jan Hasenauer","doi":"10.1371/journal.pcbi.1014774","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1014774","url":null,"abstract":"A central question in mathematical modeling of biological systems is determining which processes are most relevant and how they can be described. There are often competing hypotheses, which yield different models. Model comparison requires parameter optimization and sampling methods. Yet, standards for the specification of model selection problems and the swift evaluation of a broad spectrum of approaches are not available. PEtab Select addresses this challenge by providing a concise, standardized specification of model selection and its associated calibration problems through a new file format standard and software package. The standard facilitates the compact representation of even very large model selection problems; in one example, billions of model alternatives. PEtab Select builds on the PEtab standard for the specification of parameter estimation problems, and enables the use of state-of-the-art modelling and calibration workflows utilizing COPASI, Data2Dynamics, PEtab.jl, and pyPESTO. PEtab Select supports common model selection criteria (e.g., Akaike and Bayesian information criteria) and can be easily extended to use others. To ensure flexibility, PEtab Select implements several model space exploration approaches, including basic brute-force, forward, and backward selection, and also advanced, flexible selection methods. PEtab Select introduces the first standardization of model selection tasks, filling a critical gap in existing computational pipelines. It constitutes an essential contribution to FAIR research software in systems biology by promoting interoperability and reusability in model selection.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014774"},"PeriodicalIF":3.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13638039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114059","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

Spatiotemporal modeling of GPCR signaling: The role of endosomal dynamics and receptor recycling. GPCR信号传导的时空建模：内体动力学和受体循环的作用。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-29 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014790

Chloé Weckel, Juliette Gourdon, Léo Darrigade, Vinesh Jugnarain, Pascale Crépieux, Eric Reiter, Frédéric Jean-Alphonse, Stefan Haar, Romain Yvinec

{"title":"Spatiotemporal modeling of GPCR signaling: The role of endosomal dynamics and receptor recycling.","authors":"Chloé Weckel, Juliette Gourdon, Léo Darrigade, Vinesh Jugnarain, Pascale Crépieux, Eric Reiter, Frédéric Jean-Alphonse, Stefan Haar, Romain Yvinec","doi":"10.1371/journal.pcbi.1014790","DOIUrl":"10.1371/journal.pcbi.1014790","url":null,"abstract":"A growing number of G Protein-Coupled Receptors (GPCRs) have been reported to initiate signaling both at the cell surface and from intracellular compartments such as endosomes. The kinetics and spatial localization of these signals are critical determinants of cellular responses, yet receptor trafficking - including internalization, endosomal sorting, and recycling - remains a pivotal but often overlooked component of theoretical GPCR models. Here, we present a generic GPCR dynamic model that explicitly integrates receptor trafficking and signaling compartmentalization, enabling systematic characterization of the role of endosomal dynamics and receptor recycling on ligand-induced cellular responses. Then, as a case study, a model selection method with high-throughput kinetic data from the follicle-stimulating hormone receptor (FSHR) is used to reveal the impact of receptor trafficking on FSH-induced signaling responses. Although only a small fraction of FSHRs is internalized, these receptors produce a highly active endosomal response, underscoring the strong effect that any drug perturbing receptor trafficking can have. Our approach thus provides a refined tool for the pharmacological characterization of ligands and advances understanding of the spatial organization of FSHR signaling. Beyond this specific receptor, the methodology offers a generalizable strategy for modeling GPCR trafficking dynamics.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014790"},"PeriodicalIF":3.6,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13649145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081881","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

A compartmentalized SCN model explains temperature resistance through interregional phase differences. 划分的SCN模型通过区域间相位差解释了温度电阻。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-29 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014831

Feng Yu, Ling Yang, Jie Yan

{"title":"A compartmentalized SCN model explains temperature resistance through interregional phase differences.","authors":"Feng Yu, Ling Yang, Jie Yan","doi":"10.1371/journal.pcbi.1014831","DOIUrl":"10.1371/journal.pcbi.1014831","url":null,"abstract":"Temperature cycles entrain circadian rhythms in many peripheral tissues, yet the master circadian pacemaker (suprachiasmatic nucleus) in mammals remains largely insensitive to the same input. Despite their similar molecular circuitry, the mechanisms underlying the distinct temperature responses of central and peripheral clocks remain unclear. To address this, we developed a compartmentalized model of the SCN circadian clock that integrates the heat shock protein (HSP) pathway, simulating the response of the SCN network to temperature variations. Using this model, we successfully recapitulated key experimental observations, including the ability of heat stimuli to entrain peripheral clocks to an inverted phase while the SCN maintains its resistance. Further analysis reveals that intrinsic phase heterogeneity among SCN subregions underlies this entrainment resistance. Specifically, the phase difference between the dorsal and ventral SCN leads to divergent phase responses upon simultaneous stimulation, resulting in an attenuated net shift of the overall SCN phase. Moreover, the mode of thermal input, in which the dorsal and ventral regions are stimulated concurrently, may further enhance the SCN's robustness to temperature entrainment. These results suggest that the SCN's resistance to temperature entrainment may differ mechanistically from photic entrainment, and highlight the potential importance of its spatial structure in maintaining circadian stability.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014831"},"PeriodicalIF":3.6,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081887","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

Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data. 深度生成序列模型的归因分配使使用纯正数据的可解释性分析成为可能。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-28 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014805

Robert Frank, Michael Widrich, Rahmad Akbar, Günter Klambauer, Geir Kjetil Sandve, Philippe A Robert, Victor Greiff

{"title":"Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.","authors":"Robert Frank, Michael Widrich, Rahmad Akbar, Günter Klambauer, Geir Kjetil Sandve, Philippe A Robert, Victor Greiff","doi":"10.1371/journal.pcbi.1014805","DOIUrl":"10.1371/journal.pcbi.1014805","url":null,"abstract":"Generative machine learning models offer a powerful framework for therapeutic design, by efficiently exploring large spaces of biological sequences enriched for desirable properties. Unlike supervised learning methods, which require both positive and negative labeled data, generative models such as LSTMs can be trained solely on positively labeled sequences, for example, high-affinity antibodies. This is particularly advantageous in biological settings where negative data are scarce, unreliable, or biologically ill-defined. However, the lack of attribution methods for generative models has hindered the ability to extract interpretable biological insights from such models. To address this gap, we developed Generative Attribution Metric Analysis (GAMA), an attribution method for autoregressive generative models based on Integrated Gradients. We assessed GAMA using synthetic datasets with known ground truths to characterize its statistical behavior and validate its ability to recover biologically relevant features. We further demonstrated the utility of GAMA by applying it to experimental antibody-antigen binding data. GAMA enables model interpretability and the validation of generative sequence design strategies without the need for negative training data.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014805"},"PeriodicalIF":3.6,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13638040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075657","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

From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode. 从可观察的发酵数据到隐藏的细胞状态：在灌注模式下混合营养梭菌共培养的建模研究。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-28 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014759

Juhyeon Kim, Hangjun Cho, Jin Hong Mok, Hyeongmin Seo, Joseph Sang-Il Kwon

{"title":"From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode.","authors":"Juhyeon Kim, Hangjun Cho, Jin Hong Mok, Hyeongmin Seo, Joseph Sang-Il Kwon","doi":"10.1371/journal.pcbi.1014759","DOIUrl":"10.1371/journal.pcbi.1014759","url":null,"abstract":"Microbial cocultures exhibit complex population dynamics that are difficult to interpret because internal physiological states are only partially observable. In particular, active and dormant cell states can influence system-level behavior but are rarely resolved from standard fermentation measurements. In this study, we present a hybrid modeling framework that combines structured population-state reconstruction with sparse identification of nonlinear dynamics (SINDy) to analyze a Clostridium acetobutylicum-Clostridium ljungdahlii coculture under perfusion mode. The framework estimates active Cac and Clj biomass-equivalent trajectories from observable biomass and activity measurements, reconstructs dormant populations as model-constrained latent states, and uses these states to identify extracellular metabolite dynamics. After accounting for first-principles perfusion transport, SINDy identified sparse biological reaction terms associated with organic acid turnover, solvent formation, and acetone-to-isopropanol conversion. The resulting model captured active-biomass and metabolite trajectories and suggested that the coculture dynamics are consistent with acid-associated state transitions and sequential metabolic exchange. This framework provides a transparent strategy for interpreting partially observed microbial cocultures while explicitly treating dormant biomass as a latent reconstructed state.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014759"},"PeriodicalIF":3.6,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075696","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

Using iPALM to determine protein organisation in cardiac muscle Z-discs. 应用iPALM测定心肌z盘蛋白组织。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-28 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014309

Oliver Umney, Alistair P Curd, Heather L Martin, Tarek Lewis, Anna Ah-San Tang, Thembaninkosi Gaule, Iain W Manfield, Harikrushnan Balasubramanian, Satya Khuon, Jesse Aaron, Michelle Peckham

{"title":"Using iPALM to determine protein organisation in cardiac muscle Z-discs.","authors":"Oliver Umney, Alistair P Curd, Heather L Martin, Tarek Lewis, Anna Ah-San Tang, Thembaninkosi Gaule, Iain W Manfield, Harikrushnan Balasubramanian, Satya Khuon, Jesse Aaron, Michelle Peckham","doi":"10.1371/journal.pcbi.1014309","DOIUrl":"10.1371/journal.pcbi.1014309","url":null,"abstract":"Sarcomeres, the basic repeating unit of striated muscle, are joined together by crosslinked actin filaments found at the boundaries of muscle sarcomeres, termed Z-discs. Z-discs play a key role in cardiac signalling and disease, however, the arrangement and function of many of the proteins present in the Z-disc remain to be understood. Here, we determined the organisation of 3 key proteins, ZASP, ɑ-actinin-2 and the Z1Z2 epitope of titin, located within the Z-disc. We fluorescently labelled these proteins in cardiac myofibrils using Adhirons specific to each protein and used interferometric photoactivated localization microscopy (iPALM) to obtain the 3D position of these proteins to a high precision (<10nm in x,y,z). We then used PERPL (Pattern Extraction from Relative Positions of Localisations: available at https://github.com/oubino/z_disk/releases/tag/v0.0.3) to analyse patterns in the relative positions of the proteins and reveal their underlying organisation. This analysis revealed that ZASP and ɑ-actinin-2 have a similar repeating organisation, but that the organisation of Z1Z2 is different.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014309"},"PeriodicalIF":3.6,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13638043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075731","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

Spatial cell-cell communication inference based on cell-spot-ligand-receptor heterogeneous graphs. 基于细胞-点配体-受体异质图的空间细胞-细胞通信推断。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-28 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014835

Xiong Li, Dongding Wu, Yuejin Zhang, Min Chen, Chong Liu

{"title":"Spatial cell-cell communication inference based on cell-spot-ligand-receptor heterogeneous graphs.","authors":"Xiong Li, Dongding Wu, Yuejin Zhang, Min Chen, Chong Liu","doi":"10.1371/journal.pcbi.1014835","DOIUrl":"10.1371/journal.pcbi.1014835","url":null,"abstract":"Cell-cell communication is essential for tissue homeostasis and disease-associated microenvironment remodeling. Spatial transcriptomics enables the study of communication events in situ, but single-cell-resolution inference from spot-based data remains challenging because of uncertain cell-to-spot mapping, limited use of local spatial information, and false positives from ligand-receptor co-expression. Existing methods increasingly emphasize spatial proximity in cell-cell communication inference, while integrating biologically informative signals remains important for improving the interpretability of candidate communication events. Here, we present SpaHCC, a heterogeneous graph learning framework that integrates single-cell transcriptomics, spatial transcriptomics, and prior ligand-receptor knowledge for spatial cell-cell communication inference. By jointly leveraging molecular features, spatial neighborhood information, and cellular functional states, SpaHCC prioritizes candidate communication events and supports their biological contextualization through pathway, transcription factor, and receptor-side response analyses. Benchmark and spatial consistency analyses showed that SpaHCC recovered spatially coherent candidate communication signals. In Alzheimer's disease data, SpaHCC identified recurrent excitatory neuron-associated candidate patterns, including NRXN- and FLRT2-related modules associated with synaptic remodeling and inflammation-related transcriptional programs. Cross-patient analyses showed recurrence of selected AD-associated candidate patterns, while representative cSCC and PDAC analyses illustrated the spatial interpretability of the framework in additional disease contexts. Overall, SpaHCC provides an effective and interpretable framework for spatially resolved cell-cell communication analysis in complex diseases.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014835"},"PeriodicalIF":3.6,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075738","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

Fast structural search for classification of gut bacterial mucin O-glycan degrading enzymes. 肠道细菌粘蛋白o -聚糖降解酶分类的快速结构搜索。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-25 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014034

Mert Erden, Tyler Schult, Karin Yanagi, Jugal Kishore Sahoo, David L Kaplan, Lenore J Cowen, Kyongbum Lee

{"title":"Fast structural search for classification of gut bacterial mucin O-glycan degrading enzymes.","authors":"Mert Erden, Tyler Schult, Karin Yanagi, Jugal Kishore Sahoo, David L Kaplan, Lenore J Cowen, Kyongbum Lee","doi":"10.1371/journal.pcbi.1014034","DOIUrl":"10.1371/journal.pcbi.1014034","url":null,"abstract":"The Enzyme Commission (EC) numbering scheme provides a hierarchical way to classify enzymes according to their catalytic functions. While recent protein language model (PLM) based approaches like CLEAN and ProteInter have improved sequence-based EC number prediction, they struggle with fine-grained classification at the deepest hierarchical level. Structure-based approaches for grouping similar proteins using alignment tools excel at finding proteins that share overall global structure, but suffer from high false positive rates when classifying proteins that are globally structurally similar but functional differentiation depends on a localized region. This problem is particularly relevant to EC number prediction, as enzymatic function depends on its catalytic domain, which is a relatively small, specific region of the protein. We introduce Deep Enzyme Function Transfer (DEFT) that harmonizes sequence- and structure-based approaches through the key insight that PLM based annotations of the first two EC number hierarchy levels vastly reduce false positives that are likely to show in purely structure-based EC number prediction. Given an enzyme of interest, DEFT first uses a PLM based method to assign the first two levels of the enzyme's EC number, and then uses a structure-based method to predict the remaining two levels of the EC number. Using benchmarking datasets, we demonstrate that DEFT achieves superior accuracy compared with current state-of-the-art tools for EC number prediction. Furthermore we show that DEFT's computational efficiency enables high-throughput, genome-wide annotations of total enzyme repertoires in organisms. We illustrate this capability by experimentally validating DEFT predicted glycoside hydrolase (GH) profiles of intestinal mucus associated bacteria.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014034"},"PeriodicalIF":3.6,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064043","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

Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data. 基于拓扑的基于agent的时空元数据模型选择参数推理。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-25 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014801

Alyssa R Wenzel, Patrick M Haughey, Kyle C Nguyen, John T Nardini, Jason M Haugh, Kevin B Flores

{"title":"Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data.","authors":"Alyssa R Wenzel, Patrick M Haughey, Kyle C Nguyen, John T Nardini, Jason M Haugh, Kevin B Flores","doi":"10.1371/journal.pcbi.1014801","DOIUrl":"10.1371/journal.pcbi.1014801","url":null,"abstract":"Advances in spatiotemporal single-cell imaging have enabled detailed observations of cell population dynamics and intercellular interactions. However, translating these rich data sets into mechanistic insight remains a significant challenge. Agent-based models (ABMs) are a bottom-up computational framework for investigating the emergent behavior of cell populations that can arise from rules defining the interactions between individual neighboring cells, while topological data analysis (TDA) provides robust descriptors of spatial organization. We present TOPAZ (TOpologically-based Parameter inference for Agent-based model optimiZation), a computational pipeline that integrates TDA with approximate Bayesian computation (ABC), approximate approximate Bayesian computation (AABC), and Bayesian model selection to identify biologically plausible ABMs from spatiotemporal cellular data. TOPAZ uses persistent homology to quantify spatial features of cell trajectories and combines this topological information with parameter inference via ABC and AABC and model comparison using the Bayesian information criterion. We validate TOPAZ using simulations of collective fibroblast movement, demonstrating its ability to accurately recover model parameters and distinguish between a baseline ABM and an extended model that incorporates an alignment interaction. Our results and open-source code demonstrate the utility of TOPAZ as an extensible framework for mechanistic inference and model discrimination in spatial single-cell analysis.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014801"},"PeriodicalIF":3.6,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13641708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064037","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

Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis. 整合零膨胀校正和转录动力学单细胞转录组分析。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2026-09-25 eCollection Date: 2026-09-01 DOI: 10.1371/journal.pcbi.1014779

Chengkai Yang, Yu Liao, Ying Sheng, Feng Jiao

{"title":"Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis.","authors":"Chengkai Yang, Yu Liao, Ying Sheng, Feng Jiao","doi":"10.1371/journal.pcbi.1014779","DOIUrl":"10.1371/journal.pcbi.1014779","url":null,"abstract":"Single-cell transcriptomic data exhibit pervasive zero inflation, while traditional models either neglect this issue or fail to capture transcriptional burst-driven bimodality, hindering accurate gene regulatory studies. This study developed a zero-inflated telegraph model that integrates technical zero correction with the stochastic gene state-switching dynamics of the classical telegraph model. Systematic validation was conducted using synthetic data, human scRNA-seq data from lupus and breast cancer patients, and mouse embryonic stem cell scRNA-seq data. The model showed superior performance: it accurately fits mRNA distributions (including bimodal patterns), reliably estimates effective transcriptional burst parameters while preventing overfitting, thus enables correction of traditional models' regulatory inference bias. It also outperforms conventional approaches in detecting differentially expressed genes, with notable advantages in small samples, and identifies unique disease-related genes (e.g., LDLR, GZMB for lupus, FAIM2, VDR for breast cancer). This biologically interpretable and robust tool advances single-cell transcriptomic analysis.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014779"},"PeriodicalIF":3.6,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064010","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