PLoS Computational Biology最新文献

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

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

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

{"title":"Balancing central control and sensory feedback produces adaptable and robust locomotor patterns in a spiking, neuromechanical model of the salamander spinal cord.","authors":"Alessandro Pazzaglia, Andrej Bicanski, Andrea Ferrario, Jonathan Arreguit, Dimitri Ryczko, Auke Ijspeert","doi":"10.1371/journal.pcbi.1012101","DOIUrl":"10.1371/journal.pcbi.1012101","url":null,"abstract":"<p><p>This study introduces a novel neuromechanical model employing a detailed spiking neural network to explore the role of axial proprioceptive sensory feedback, namely stretch feedback, in salamander locomotion. Unlike previous studies that often oversimplified the dynamics of the locomotor networks, our model includes detailed simulations of the classes of neurons that are considered responsible for generating movement patterns. The locomotor circuits, modeled as a spiking neural network of adaptive leaky integrate-and-fire neurons, are coupled to a three-dimensional mechanical model of a salamander with realistic physical parameters and simulated muscles. In open-loop simulations (i.e., without sensory feedback), the model replicates locomotor patterns observed in-vitro and in-vivo for swimming and trotting gaits. Additionally, a modular descending reticulospinal drive to the central pattern generation network allows to accurately control the activation, frequency and phase relationship of the different sections of the limb and axial circuits. In closed-loop swimming simulations (i.e. including axial stretch feedback), systematic evaluations reveal that intermediate values of feedback strength increase the tail beat frequency and reduce the intersegmental phase lag, contributing to a more coordinated, faster and energy-efficient locomotion. Interestingly, the result is conserved across different feedback topologies (ascending or descending, excitatory or inhibitory), suggesting that it may be an inherent property of axial proprioception. Moreover, intermediate feedback strengths expand the stability region of the network, enhancing its tolerance to a wider range of descending drives, internal parameters' modifications and noise levels. Conversely, high values of feedback strength lead to a loss of controllability of the network and a degradation of its locomotor performance. Overall, this study highlights the beneficial role of proprioception in generating, modulating and stabilizing locomotion patterns, provided that it does not excessively override centrally-generated locomotor rhythms. This work also underscores the critical role of detailed, biologically-realistic neural networks to improve our understanding of vertebrate locomotion.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012101"},"PeriodicalIF":3.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010320","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":"A Structural Model of Truncated Gaussia princeps Luciferase Elucidating the Crucial Catalytic Function of No.76 Arginine towards Coelenterazine Oxidation.","authors":"Nan Wu, Zhi-Chao Xu, Kai-Dong Du, Shen Huang, Naohiro Kobayashi, Yutaka Kuroda, Yan-Hong Bai","doi":"10.1371/journal.pcbi.1012722","DOIUrl":"10.1371/journal.pcbi.1012722","url":null,"abstract":"<p><p>Gaussia Luciferase (GLuc) is a renowned reporter protein that can catalyze the oxidation of coelenterazine (CTZ) and emit a bright light signal. GLuc comprises two consecutive repeats that form the enzyme body and a central putative catalytic cavity. However, deleting the C-terminal repeat only limited reduces the activity (over 30% residual luminescence intensity detectable), despite being a key part of the cavity. How does the remaining GLuc (tGLuc) catalyze CTZ? To address this question, we built a structural model of tGLuc by removing the C-terminal repeat from the resolved structure of intact GLuc, and verified that the cavity-forming component in GLuc remains stable and provides an open-mouth cavity in tGLuc during 500 ns MD simulations in water. Docking simulation and a followed umbrella sampling analysis further revealed that the cavity on tGLuc has a high affinity for CTZ, with a binding energy of up to -114 kJ/mol. Moreover, R76, a validated activity-critical amino acid residue, resides in the cavity and forms a stable hydrogen bond with CTZ. Then, we constructed a cluster model to examine the CTZ oxidation pathway in the cavity using Density Functional Theory (DFT) calculations. The result showed that the pathway consists of four elementary reactions, with the highest Gibbs energy barrier being 65.4 kJ/mol. Both intramolecular electron transfer and the convergence of S1/S0 potential energy surfaces occurred in the last elementary reaction, which was regarded as the reported Chemically-Initiated-Electron-Exchange-Luminescence (CIEEL) reaction. Geometry and wavefunction analysis on the pathway indicated that R76 plays a vital role in CTZ oxidation, which first anchors the environmental oxygen molecule and induces it to form a singlet biradical state, facilitating its attack on CTZ. Subsequently, R76 and the adjacent Q88, positioned near R76 through the tGLuc refolding process, stabilize the transition states and facilitate the emergence of radical electrons on CTZ at the onset of the CIEEL reaction, which contributes to the subsequent intramolecular electron transfer and the production of excited amide product. This study provides a comprehensive explanation of tGLuc's catalytic mechanism. However, it is important to note that these findings are specific to tGLuc and may not extend to other CTZ-based luciferases, particularly those lacking arginine in their catalytic cavities, which likely operate via distinct mechanisms.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012722"},"PeriodicalIF":3.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010317","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":"Extracting the fingerprints of sequences of random rhythmic auditory stimuli from electrophysiological data.","authors":"Fernando A Najman, Antonio Galves, Marcela Svarc, Claudia D Vargas","doi":"10.1371/journal.pcbi.1012765","DOIUrl":"10.1371/journal.pcbi.1012765","url":null,"abstract":"<p><p>It has been classically conjectured that the brain assigns probabilistic models to sequences of stimuli. An important issue associated with this conjecture is the identification of the classes of models used by the brain to perform this task. We address this issue by using a new clustering procedure for sets of electroencephalographic (EEG) data recorded from participants exposed to a sequence of auditory stimuli generated by a stochastic chain. This clustering procedure indicates that the brain uses the recurrent occurrences of a regular auditory stimulus in order to build a model.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012765"},"PeriodicalIF":3.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11785292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010256","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":"Adaptation optimizes sensory encoding for future stimuli.","authors":"Jiang Mao, Constantin A Rothkopf, Alan A Stocker","doi":"10.1371/journal.pcbi.1012746","DOIUrl":"10.1371/journal.pcbi.1012746","url":null,"abstract":"<p><p>Sensory neurons continually adapt their response characteristics according to recent stimulus history. However, it is unclear how such a reactive process can benefit the organism. Here, we test the hypothesis that adaptation actually acts proactively in the sense that it optimally adjusts sensory encoding for future stimuli. We first quantified human subjects' ability to discriminate visual orientation under different adaptation conditions. Using an information theoretic analysis, we found that adaptation leads to a reallocation of coding resources such that encoding accuracy peaks at the mean orientation of the adaptor while total coding capacity remains constant. We then asked whether this characteristic change in encoding accuracy is predicted by the temporal statistics of natural visual input. Analyzing the retinal input of freely behaving human subjects showed that the distribution of local visual orientations in the retinal input stream indeed peaks at the mean orientation of the preceding input history (i.e., the adaptor). We further tested our hypothesis by analyzing the internal sensory representations of a recurrent neural network trained to predict the next frame of natural scene videos (PredNet). Simulating our human adaptation experiment with PredNet, we found that the network exhibited the same change in encoding accuracy as observed in human subjects. Taken together, our results suggest that adaptation-induced changes in encoding accuracy prepare the visual system for future stimuli.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012746"},"PeriodicalIF":3.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010319","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":"BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.","authors":"Natalie R Davidson, Fan Zhang, Casey S Greene","doi":"10.1371/journal.pcbi.1012742","DOIUrl":"10.1371/journal.pcbi.1012742","url":null,"abstract":"<p><p>While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012742"},"PeriodicalIF":3.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010249","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":"Integration of unpaired single cell omics data by deep transfer graph convolutional network.","authors":"Yulong Kan, Yunjing Qi, Zhongxiao Zhang, Xikeng Liang, Weihao Wang, Shuilin Jin","doi":"10.1371/journal.pcbi.1012625","DOIUrl":"10.1371/journal.pcbi.1012625","url":null,"abstract":"<p><p>The rapid advance of large-scale atlas-level single cell RNA sequences and single-cell chromatin accessibility data provide extraordinary avenues to broad and deep insight into complex biological mechanism. Leveraging the datasets and transfering labels from scRNA-seq to scATAC-seq will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the lower capable of preserving fine-grained cell populations and intrinsic or extrinsic heterogeneity between datasets. Here, we present a robust deep transfer model based graph convolutional network, scTGCN, which achieves versatile performance in preserving biological variation, while achieving integration hundreds of thousands cells in minutes with low memory consumption. We show that scTGCN is powerful to the integration of mouse atlas data and multimodal data generated from APSA-seq and CITE-seq. Thus, scTGCN shows high label transfer accuracy and effectively knowledge transfer across different modalities.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012625"},"PeriodicalIF":3.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11778791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010333","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":"Optimal control of agent-based models via surrogate modeling.","authors":"Luis L Fonseca, Lucas Böttcher, Borna Mehrad, Reinhard C Laubenbacher","doi":"10.1371/journal.pcbi.1012138","DOIUrl":"10.1371/journal.pcbi.1012138","url":null,"abstract":"<p><p>This paper describes and validates an algorithm to solve optimal control problems for agent-based models (ABMs). For a given ABM and a given optimal control problem, the algorithm derives a surrogate model, typically lower-dimensional, in the form of a system of ordinary differential equations (ODEs), solves the control problem for the surrogate model, and then transfers the solution back to the original ABM. It applies to quite general ABMs and offers several options for the ODE structure, depending on what information about the ABM is to be used. There is a broad range of applications for such an algorithm, since ABMs are used widely in the life sciences, such as ecology, epidemiology, and biomedicine and healthcare, areas where optimal control is an important purpose for modeling, such as for medical digital twin technology.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 1","pages":"e1012138"},"PeriodicalIF":3.8,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984424","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}