PLoS Computational Biology最新文献_第3页

DNA spontaneously wrapping around a histone core prefers negative supercoiling: A Brownian dynamics study.

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-28 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012362

Chunhong Long, Hongqiong Liang, Biao Wan

{"title":"DNA spontaneously wrapping around a histone core prefers negative supercoiling: A Brownian dynamics study.","authors":"Chunhong Long, Hongqiong Liang, Biao Wan","doi":"10.1371/journal.pcbi.1012362","DOIUrl":"10.1371/journal.pcbi.1012362","url":null,"abstract":"In eukaryotes, DNA achieves a highly compact structure primarily due to its winding around the histone cores. The nature wrapping of DNA around histone core form a 1.7 left-handed superhelical turns, contributing to negative supercoiling in chromatin. During transcription, negative supercoils generated behind the polymerase during transcription may play a role in triggering nucleosome reassembly. To elucidate how supercoils influence the dynamics of wrapping of DNA around the histone cores, we developed a novel model to simulate the intricate interplay between DNA and histone. Our simulations reveal that both positively and negatively supercoiled DNAs are capable of wrapping around histone cores to adopt the nucleosome conformation. Notably, our findings confirm a strong preference for negative supercoiled DNA during nucleosome wrapping, and reveal that the both of the negative writhe and twist are beneficial to the formation of the DNA wrapping around histone. Additionally, the simulations of the multiple nucleosomes on the same DNA template indicate that the nucleosome tends to assemble in proximity to the original nucleosome. This advancement in understanding the spontaneous formation of nucleosomes may offer insights into the complex dynamics of chromatin assembly and the fundamental mechanisms governing the structure and function of chromatin.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012362"},"PeriodicalIF":3.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11793753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143060425","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

Ten simple rules to complete successfully a computational MSc thesis project.

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-01-28 eCollection Date: 2025-01-01 DOI: 10.1371/journal.pcbi.1012756

Edoardo Saccenti, Cristina Furlan

引用次数: 0

Specificity and tunability of efflux pumps: A new role for the proton gradient?

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.1012772

Matthew Gerry, Duncan Kirby, Boian S Alexandrov, Dvira Segal, Anton Zilman

{"title":"Specificity and tunability of efflux pumps: A new role for the proton gradient?","authors":"Matthew Gerry, Duncan Kirby, Boian S Alexandrov, Dvira Segal, Anton Zilman","doi":"10.1371/journal.pcbi.1012772","DOIUrl":"10.1371/journal.pcbi.1012772","url":null,"abstract":"Efflux pumps that transport antibacterial drugs out of bacterial cells have broad specificity, commonly leading to broad spectrum resistance and limiting treatment strategies for infections. It remains unclear how efflux pumps can maintain this broad spectrum specificity to diverse drug molecules while limiting the efflux of other cytoplasmic content. We have investigated the origins of this broad specificity using theoretical models informed by the experimentally determined structural and kinetic properties of efflux pumps. We developed a set of mathematical models describing operation of efflux pumps as a discrete cyclic stochastic process across a network of states characterizing pump conformations and the presence/absence of bound ligands and protons. These include a minimal three-state model that lends itself to clear analytic calculations as well as a five-state model that relaxes some of the simpler model's most strict assumptions. We found that the pump specificity is determined not solely by the drug affinity to the pump-as is commonly assumed-but it is also directly affected by the periplasmic pH and the transmembrane potential. Therefore, changes to the proton concentration gradient and voltage drop across the membrane can influence how effective the pump is at extruding a particular drug molecule. Furthermore, we found that while both the proton concentration gradient across the membrane and the transmembrane potential contribute to the thermodynamic force driving the pump, their effects on the efflux enter not strictly in a combined proton motive force. Rather, they have two distinguishable effects on the overall throughput. These results highlight the unexpected effects of thermodynamic driving forces out of equilibrium and illustrate how efflux pump structure and function are conducive to the emergence of multidrug resistance.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012772"},"PeriodicalIF":3.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11798531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143053273","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

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