PLoS Computational Biology最新文献

{"title":"The role of oscillations in grid cells' toroidal topology.","authors":"Giovanni di Sarra, Siddharth Jha, Yasser Roudi","doi":"10.1371/journal.pcbi.1012776","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012776","url":null,"abstract":"<p><p>Persistent homology applied to the activity of grid cells in the Medial Entorhinal Cortex suggests that this activity lies on a toroidal manifold. By analyzing real data and a simple model, we show that neural oscillations play a key role in the appearance of this toroidal topology. To quantitatively monitor how changes in spike trains influence the topology of the data, we first define a robust measure for the degree of toroidality of a dataset. Using this measure, we find that small perturbations ( ~ 100 ms) of spike times have little influence on both the toroidality and the hexagonality of the ratemaps. Jittering spikes by  ~ 100-500 ms, however, destroys the toroidal topology, while still having little impact on grid scores. These critical jittering time scales fall in the range of the periods of oscillations between the theta and eta bands. We thus hypothesized that these oscillatory modulations of neuronal spiking play a key role in the appearance and robustness of toroidal topology and the hexagonal spatial selectivity is not sufficient. We confirmed this hypothesis using a simple model for the activity of grid cells, consisting of an ensemble of independent rate-modulated Poisson processes. When these rates were modulated by oscillations, the network behaved similarly to the real data in exhibiting toroidal topology, even when the position of the fields were perturbed. In the absence of oscillations, this similarity was substantially lower. Furthermore, we find that the experimentally recorded spike trains indeed exhibit temporal modulations at the eta and theta bands, and that the ratio of the power in the eta band to that of the theta band, [Formula: see text], correlates with the critical jittering time at which the toroidal topology disappears.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012776"},"PeriodicalIF":3.8,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143067524","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}

{"title":"The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis.","authors":"Toviah Moldwin, Li Shay Azran, Idan Segev","doi":"10.1371/journal.pcbi.1012754","DOIUrl":"10.1371/journal.pcbi.1012754","url":null,"abstract":"<p><p>Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how these theoretically-derived rules relate to biological mechanisms of plasticity in the brain, or how these different rules might be mechanistically implemented in different contexts and brain regions. This study shows that the calcium control hypothesis, which relates synaptic plasticity in the brain to the calcium concentration ([Ca2+]) in dendritic spines, can produce a diverse array of learning rules. We propose a simple, perceptron-like neuron model, the calcitron, that has four sources of [Ca2+]: local (following the activation of an excitatory synapse and confined to that synapse), heterosynaptic (resulting from the activity of other synapses), postsynaptic spike-dependent, and supervisor-dependent. We demonstrate that by modulating the plasticity thresholds and calcium influx from each calcium source, we can reproduce a wide range of learning and plasticity protocols, such as Hebbian and anti-Hebbian learning, frequency-dependent plasticity, and unsupervised recognition of frequently repeating input patterns. Moreover, by devising simple neural circuits to provide supervisory signals, we show how the calcitron can implement homeostatic plasticity, perceptron learning, and BTSP-inspired one-shot learning. Our study bridges the gap between theoretical learning algorithms and their biological counterparts, not only replicating established learning paradigms but also introducing novel rules.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012754"},"PeriodicalIF":3.8,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11835382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143067520","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}

{"title":"Spherical harmonics texture extraction for versatile analysis of biological objects.","authors":"Oane Gros, Josiah B Passmore, Noa O Borst, Dominik Kutra, Wilco Nijenhuis, Timothy Fuqua, Lukas C Kapitein, Justin M Crocker, Anna Kreshuk, Simone Köhler","doi":"10.1371/journal.pcbi.1012349","DOIUrl":"10.1371/journal.pcbi.1012349","url":null,"abstract":"<p><p>The characterization of phenotypes in cells or organisms from microscopy data largely depends on differences in the spatial distribution of image intensity. Multiple methods exist for quantifying the intensity distribution - or image texture - across objects in natural images. However, many of these texture extraction methods do not directly adapt to 3D microscopy data. Here, we present Spherical Texture extraction, which measures the variance in intensity per angular wavelength by calculating the Spherical Harmonics or Fourier power spectrum of a spherical or circular projection of the angular mean intensity of the object. This method provides a 20-value characterization that quantifies the scale of features in the spherical projection of the intensity distribution, giving a different signal if the intensity is, for example, clustered in parts of the volume or spread across the entire volume. We apply this method to different systems and demonstrate its ability to describe various biological problems through feature extraction. The Spherical Texture extraction characterizes biologically defined gene expression patterns in Drosophila melanogaster embryos, giving a quantitative read-out for pattern formation. Our method can also quantify morphological differences in Caenorhabditis elegans germline nuclei, which lack a predefined pattern. We show that the classification of germline nuclei using their Spherical Texture outperforms a convolutional neural net when training data is limited. Additionally, we use a similar pipeline on 2D cell migration data to extract the polarization direction and quantify the alignment of fluorescent markers to the migration direction. We implemented the Spherical Texture method as a plugin in ilastik to provide a parameter-free and data-agnostic application to any segmented 3D or 2D dataset. Additionally, this technique can also be applied through a Python package to provide extra feature extraction for any object classification pipeline or downstream analysis.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012349"},"PeriodicalIF":3.8,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11798461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143067517","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}

{"title":"Correction: Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation.","authors":"Phoebe Asplin, Matt J Keeling, Rebecca Mancy, Edward M Hill","doi":"10.1371/journal.pcbi.1012791","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012791","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1371/journal.pcbi.1012096.].</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012791"},"PeriodicalIF":3.8,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11778760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143067501","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}

{"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":"<p><p>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.</p>","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}

{"title":"Ten simple rules to complete successfully a computational MSc thesis project.","authors":"Edoardo Saccenti, Cristina Furlan","doi":"10.1371/journal.pcbi.1012756","DOIUrl":"10.1371/journal.pcbi.1012756","url":null,"abstract":"<p><p>The thesis project is an essential step to obtain an MSc degree. Within STEM and Life Sciences disciplines, computational theses have specific characteristics that differentiate them from wet laboratory ones. In this article, we present Ten simple rules to direct and support Master students who are about to start a computational research project for their Master thesis. We begin by recommending defining the personal learning goals for the project; we then highlight specific pitfalls that computational students might encounter during their work, such as procrastination by computation or wasting time while attempting to reinvent computational tools. We provide the students a series of suggestions on how to work following FAIR principles, learn new computing languages, and think ahead for computational challenges. We hope that these 10 simple rules will provide Master students with a framework for the successful completion of their computational thesis.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012756"},"PeriodicalIF":3.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143060426","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}