PLoS Computational Biology最新文献_第10页

Biophysical modeling and experimental analysis of the dynamics of C. elegans body-wall muscle cells.

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-27 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012318

Xuexing Du, Jennifer Crodelle, Victor James Barranca, Songting Li, Yunzhu Shi, Shangbang Gao, Douglas Zhou

{"title":"Biophysical modeling and experimental analysis of the dynamics of C. elegans body-wall muscle cells.","authors":"Xuexing Du, Jennifer Crodelle, Victor James Barranca, Songting Li, Yunzhu Shi, Shangbang Gao, Douglas Zhou","doi":"10.1371/journal.pcbi.1012318","DOIUrl":"10.1371/journal.pcbi.1012318","url":null,"abstract":"This study combines experimental techniques and mathematical modeling to investigate the dynamics of C. elegans body-wall muscle cells. Specifically, by conducting voltage clamp and mutant experiments, we identify key ion channels, particularly the L-type voltage-gated calcium channel (EGL-19) and potassium channels (SHK-1, SLO-2), which are crucial for generating action potentials. We develop Hodgkin-Huxley-based models for these channels and integrate them to capture the cells' electrical activity. To ensure the model accurately reflects cellular responses under depolarizing currents, we develop a parallel simulation-based inference method for determining the model's free parameters. This method performs rapid parallel sampling across high-dimensional parameter spaces, fitting the model to the responses of muscle cells to specific stimuli and yielding accurate parameter estimates. We validate our model by comparing its predictions against cellular responses to various current stimuli in experiments and show that our approach effectively determines suitable parameters for accurately modeling the dynamics in mutant cases. Additionally, we discover an optimal response frequency in body-wall muscle cells, which corresponds to a burst firing mode rather than regular firing mode. Our work provides the first experimentally constrained and biophysically detailed muscle cell model of C. elegans, and our analytical framework combined with robust and efficient parametric estimation method can be extended to model construction in other species.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012318"},"PeriodicalIF":3.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11781704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143053332","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-like face pareidolia emerges in deep neural networks optimized for face and object recognition.

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-27 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012751

Pranjul Gupta, Katharina Dobs

{"title":"Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.","authors":"Pranjul Gupta, Katharina Dobs","doi":"10.1371/journal.pcbi.1012751","DOIUrl":"10.1371/journal.pcbi.1012751","url":null,"abstract":"The human visual system possesses a remarkable ability to detect and process faces across diverse contexts, including the phenomenon of face pareidolia--seeing faces in inanimate objects. Despite extensive research, it remains unclear why the visual system employs such broadly tuned face detection capabilities. We hypothesized that face pareidolia results from the visual system's optimization for recognizing both faces and objects. To test this hypothesis, we used task-optimized deep convolutional neural networks (CNNs) and evaluated their alignment with human behavioral signatures and neural responses, measured via magnetoencephalography (MEG), related to pareidolia processing. Specifically, we trained CNNs on tasks involving combinations of face identification, face detection, object categorization, and object detection. Using representational similarity analysis, we found that CNNs that included object categorization in their training tasks represented pareidolia faces, real faces, and matched objects more similarly to neural responses than those that did not. Although these CNNs showed similar overall alignment with neural data, a closer examination of their internal representations revealed that specific training tasks had distinct effects on how pareidolia faces were represented across layers. Finally, interpretability methods revealed that only a CNN trained for both face identification and object categorization relied on face-like features-such as 'eyes'-to classify pareidolia stimuli as faces, mirroring findings in human perception. Our results suggest that human-like face pareidolia may emerge from the visual system's optimization for face identification within the context of generalized object categorization.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012751"},"PeriodicalIF":3.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143053238","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

Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning.

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-24 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012716

Mohammad Neamul Kabir, Li Rong Wang, Wilson Wen Bin Goh

{"title":"Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning.","authors":"Mohammad Neamul Kabir, Li Rong Wang, Wilson Wen Bin Goh","doi":"10.1371/journal.pcbi.1012716","DOIUrl":"10.1371/journal.pcbi.1012716","url":null,"abstract":"The \"similarity of dissimilarities\" is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilarities alongside similarities provides a more detailed understanding of evolutionary processes, allowing for a deeper exploration of regions that influence biological functionality. For ML models, incorporating dissimilarity measures helps avoid misleading results caused by highly correlated or similar data, addressing confounding issues like the Doppelgänger Effect. This leads to more accurate insights and a stronger understanding of complex biological systems. In the realm of personalized AI and precision medicine, the importance of dissimilarities is paramount. Personalized AI builds local models for each sample by identifying a network of neighboring samples. However, if the neighboring samples are too similar, it becomes difficult to identify factors critical to disease onset for the individual, limiting the effectiveness of personalized interventions or treatments. This paper discusses the \"similarity of dissimilarities\" concept, using protein function prediction, ML, and personalized AI as key examples. Integrating this approach into an analysis allows for the design of better, more meaningful experiments and the development of smarter validation methods, ensuring that the models learn in a meaningful way.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012716"},"PeriodicalIF":3.8,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143033560","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

Social mates dynamically coordinate aggressive behavior to produce strategic territorial defense.

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-24 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012740

Nicole M Moody, Cole M Williams, Sohini Ramachandran, Matthew J Fuxjager

{"title":"Social mates dynamically coordinate aggressive behavior to produce strategic territorial defense.","authors":"Nicole M Moody, Cole M Williams, Sohini Ramachandran, Matthew J Fuxjager","doi":"10.1371/journal.pcbi.1012740","DOIUrl":"10.1371/journal.pcbi.1012740","url":null,"abstract":"Negotiating social dynamics among allies and enemies is a complex problem that often requires individuals to tailor their behavioral approach to a specific situation based on environmental and/or social factors. One way to make these contextual adjustments is by arranging behavioral output into intentional patterns. Yet, few studies explore how behavioral patterns vary across a wide range of contexts, or how allies might interlace their behavior to produce a coordinated response. Here, we investigate the possibility that resident female and male downy woodpeckers guard their breeding territories from conspecific intruders by deploying defensive behavior in context-specific patterns. To study whether this is the case, we use correlation networks to reveal how suites of agonistic behavior are interrelated. We find that residents do organize their defense into definable patterns, with female and male social mates deploying their behaviors non-randomly in a correlated fashion. We then employ spectral clustering analyses to further distill these responses into distinct behavioral motifs. Our results show that this population of woodpeckers adjusts the defensive motifs deployed according to threat context. When we combine this approach with behavioral transition analyses, our results reveal that pair coordination is a common feature of territory defense in this species. However, if simulated intruders are less threatening, residents are more likely to defend solo, where only one bird deploys defensive behaviors. Overall, our study supports the hypothesis that nonhuman animals can pattern their behavior in a strategic and coordinated manner, while demonstrating the power of systems approaches for analyzing multiagent behavioral dynamics.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012740"},"PeriodicalIF":3.8,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11785317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143033654","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

Physiology-informed regularisation enables training of universal differential equation systems for biological applications.

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-23 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012198

Max de Rooij, Balázs Erdős, Natal A W van Riel, Shauna D O'Donovan

{"title":"Physiology-informed regularisation enables training of universal differential equation systems for biological applications.","authors":"Max de Rooij, Balázs Erdős, Natal A W van Riel, Shauna D O'Donovan","doi":"10.1371/journal.pcbi.1012198","DOIUrl":"10.1371/journal.pcbi.1012198","url":null,"abstract":"Systems biology tackles the challenge of understanding the high complexity in the internal regulation of homeostasis in the human body through mathematical modelling. These models can aid in the discovery of disease mechanisms and potential drug targets. However, on one hand the development and validation of knowledge-based mechanistic models is time-consuming and does not scale well with increasing features in medical data. On the other hand, data-driven approaches such as machine learning models require large volumes of data to produce generalisable models. The integration of neural networks and mechanistic models, forming universal differential equation (UDE) models, enables the automated learning of unknown model terms with less data than neural networks alone. Nevertheless, estimating parameters for these hybrid models remains difficult with sparse data and limited sampling durations that are common in biological applications. In this work, we propose the use of physiology-informed regularisation, penalising biologically implausible model behavior to guide the UDE towards more physiologically plausible regions of the solution space. In a simulation study we show that physiology-informed regularisation not only results in a more accurate forecasting of model behaviour, but also supports training with less data. We also applied this technique to learn a representation of the rate of glucose appearance in the glucose minimal model using meal response data measured in healthy people. In that case, the inclusion of regularisation reduces variability between UDE-embedded neural networks that were trained from different initial parameter guesses.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012198"},"PeriodicalIF":3.8,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029430","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

The tumour histopathology "glossary" for AI developers.

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-23 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012708

Soham Mandal, Ann-Marie Baker, Trevor A Graham, Konstantin Bräutigam

{"title":"The tumour histopathology \"glossary\" for AI developers.","authors":"Soham Mandal, Ann-Marie Baker, Trevor A Graham, Konstantin Bräutigam","doi":"10.1371/journal.pcbi.1012708","DOIUrl":"10.1371/journal.pcbi.1012708","url":null,"abstract":"The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analysing histopathology images for prognostic and treatment-predictive insights. However, effective translation of these computational methods requires computational researchers to have at least a basic understanding of histopathology. In this work, we aim to bridge that gap by introducing essential histopathology concepts to support AI developers in their research. We cover the defining features of key cell types, including epithelial, stromal, and immune cells. The concepts of malignancy, precursor lesions, and the tumour microenvironment (TME) are discussed and illustrated. To enhance understanding, we also introduce foundational histopathology techniques, such as conventional staining with hematoxylin and eosin (HE), antibody staining by immunohistochemistry, and including the new multiplexed antibody staining methods. By providing this essential knowledge to the computational community, we aim to accelerate the development of AI algorithms for cancer research.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012708"},"PeriodicalIF":3.8,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029431","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

Data-driven discovery and parameter estimation of mathematical models in biological pattern formation.

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-23 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012689

Hidekazu Hishinuma, Hisako Takigawa-Imamura, Takashi Miura

{"title":"Data-driven discovery and parameter estimation of mathematical models in biological pattern formation.","authors":"Hidekazu Hishinuma, Hisako Takigawa-Imamura, Takashi Miura","doi":"10.1371/journal.pcbi.1012689","DOIUrl":"10.1371/journal.pcbi.1012689","url":null,"abstract":"Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have been made empirically. In the present study, we propose a data-driven approach to validate the applicability of mathematical models. Specifically, we developed methods to automatically select the appropriate mathematical models based on the patterns of interest and to estimate the model parameters. For model selection, we employed Contrastive Language-Image Pre-training (CLIP) for zero-shot feature extraction, mapping the given pattern images to latent space and specifying the appropriate model. For parameter estimation, we developed a novel technique that rapidly performs approximate Bayesian inference based on Natural Gradient Boosting (NGBoost). This method allows for parameter estimation under minimal constraints; i.e., it does not require time-series data or initial conditions and is applicable to various types of mathematical models. We tested the method with Turing patterns and demonstrated its high accuracy and correspondence to analytical features. Our strategy enables efficient validation of mathematical models using spatial patterns.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012689"},"PeriodicalIF":3.8,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029382","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

Trajectory inference from single-cell genomics data with a process time model. 基于过程时间模型的单细胞基因组数据轨迹推断。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-21 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012752

Meichen Fang, Gennady Gorin, Lior Pachter

{"title":"Trajectory inference from single-cell genomics data with a process time model.","authors":"Meichen Fang, Gennady Gorin, Lior Pachter","doi":"10.1371/journal.pcbi.1012752","DOIUrl":"10.1371/journal.pcbi.1012752","url":null,"abstract":"Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of \"pseudotime\" that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of \"process time\" via a principled modeling approach to formulating trajectories and inferring latent variables corresponding to timing of cells subject to a biophysical process. Our implementation of this approach, called Chronocell, provides a biophysical formulation of trajectories built on cell state transitions. The Chronocell model is identifiable, making parameter inference meaningful. Furthermore, Chronocell can interpolate between trajectory inference, when cell states lie on a continuum, and clustering, when cells cluster into discrete states. By using a variety of datasets ranging from cluster-like to continuous, we show that Chronocell enables us to assess the suitability of datasets and reveals distinct cellular distributions along process time that are consistent with biological process times. We also compare our parameter estimates of degradation rates to those derived from metabolic labeling datasets, thereby showcasing the biophysical utility of Chronocell. Nevertheless, based on performance characterization on simulations, we find that process time inference can be challenging, highlighting the importance of dataset quality and careful model assessment.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012752"},"PeriodicalIF":3.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11760028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010336","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

Data-driven model discovery and model selection for noisy biological systems. 噪声生物系统的数据驱动模型发现与模型选择。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-21 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012762

Xiaojun Wu, MeiLu McDermott, Adam L MacLean

{"title":"Data-driven model discovery and model selection for noisy biological systems.","authors":"Xiaojun Wu, MeiLu McDermott, Adam L MacLean","doi":"10.1371/journal.pcbi.1012762","DOIUrl":"10.1371/journal.pcbi.1012762","url":null,"abstract":"Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). However, SINDy struggles with realistic levels of biological noise and is limited in its ability to incorporate prior knowledge of the system. We propose a data-driven framework for model discovery and model selection using hybrid dynamical systems: partial models containing missing terms. Neural networks are used to approximate the unknown dynamics of a system, enabling the denoising of the data while simultaneously learning the latent dynamics. Simulations from the fitted neural network are then used to infer models using sparse regression. We show, via model selection, that model discovery using hybrid dynamical systems outperforms alternative approaches. We find it possible to infer models correctly up to high levels of biological noise of different types. We demonstrate the potential to learn models from sparse, noisy data in application to a canonical cell state transition using data derived from single-cell transcriptomics. Overall, this approach provides a practical framework for model discovery in biology in cases where data are noisy and sparse, of particular utility when the underlying biological mechanisms are partially but incompletely known.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012762"},"PeriodicalIF":3.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11753677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010252","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

Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases. 神经退行性疾病病理蛋白扩散的基于连接体的生物物理模型。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-21 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012743

Peng Ren, Xuehua Cui, Xia Liang

{"title":"Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases.","authors":"Peng Ren, Xuehua Cui, Xia Liang","doi":"10.1371/journal.pcbi.1012743","DOIUrl":"10.1371/journal.pcbi.1012743","url":null,"abstract":"Neurodegenerative diseases are a group of disorders characterized by progressive degeneration or death of neurons. The complexity of clinical symptoms and irreversibility of disease progression significantly affects individual lives, leading to premature mortality. The prevalence of neurodegenerative diseases keeps increasing, yet the specific pathogenic mechanisms remain incompletely understood and effective treatment strategies are lacking. In recent years, convergent experimental evidence supports the \"prion-like transmission\" assumption that abnormal proteins induce misfolding of normal proteins, and these misfolded proteins propagate throughout the neural networks to cause neuronal death. To elucidate this dynamic process in vivo from a computational perspective, researchers have proposed three connectome-based biophysical models to simulate the spread of pathological proteins: the Network Diffusion Model, the Epidemic Spreading Model, and the agent-based Susceptible-Infectious-Removed model. These models have demonstrated promising predictive capabilities. This review focuses on the explanations of their fundamental principles and applications. Then, we compare the strengths and weaknesses of the models. Building upon this foundation, we introduce new directions for model optimization and propose a unified framework for the evaluation of connectome-based biophysical models. We expect that this review could lower the entry barrier for researchers in this field, accelerate model optimization, and thereby advance the clinical translation of connectome-based biophysical models.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012743"},"PeriodicalIF":3.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010251","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