PLOS Computational Biology: New ArticlesPLOShttps://journals.plos.org/ploscompbiol/webmaster@plos.orghttps://journals.plos.org/ploscompbiol/feed/atomAll PLOS articles are Open Access.https://journals.plos.org/ploscompbiol/resource/img/favicon.icohttps://journals.plos.org/ploscompbiol/resource/img/favicon.ico2024-03-19T03:34:59ZContinuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and NilearnHao-Ting WangSteven L. MeislerHanad SharmarkeNatasha ClarkeNicolas GensollenChristopher J. MarkiewiczFrançois PaugamBertrand ThirionPierre Bellec10.1371/journal.pcbi.10119422024-03-18T14:00:00Z2024-03-18T14:00:00Z<p>by Hao-Ting Wang, Steven L. Meisler, Hanad Sharmarke, Natasha Clarke, Nicolas Gensollen, Christopher J. Markiewicz, François Paugam, Bertrand Thirion, Pierre Bellec</p>
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.The condition for dynamic stability in humans walking with feedback controlHendrik ReimannSjoerd M. Bruijn10.1371/journal.pcbi.10118612024-03-18T14:00:00Z2024-03-18T14:00:00Z<p>by Hendrik Reimann, Sjoerd M. Bruijn</p>
The walking human body is mechanically unstable. Loss of stability and falling is more likely in certain groups of people, such as older adults or people with neuromotor impairments, as well as in certain situations, such as when experiencing conflicting or distracting sensory inputs. Stability during walking is often characterized biomechanically, by measures based on body dynamics and the base of support. Neural control of upright stability, on the other hand, does not factor into commonly used stability measures. Here we analyze stability of human walking accounting for both biomechanics and neural control, using a modeling approach. We define a walking system as a combination of biomechanics, using the well known inverted pendulum model, and neural control, using a proportional-derivative controller for foot placement based on the state of the center of mass at midstance. We analyze this system formally and show that for any choice of system parameters there is always one periodic orbit. We then determine when this periodic orbit is stable, i.e. how the neural control gain values have to be chosen for stable walking. Following the formal analysis, we use this model to make predictions about neural control gains and compare these predictions with the literature and existing experimental data. The model predicts that control gains should increase with decreasing cadence. This finding appears in agreement with literature showing stronger effects of visual or vestibular manipulations at different walking speeds.Metabolic symbiosis between oxygenated and hypoxic tumour cells: An agent-based modelling studyPahala Gedara JayathilakePedro VictoriClara E. PavilletChang Heon LeeDimitrios VoukantsisAna MiarAnjali AroraAdrian L. HarrisKarl J. MortenFrancesca M. Buffa10.1371/journal.pcbi.10119442024-03-15T14:00:00Z2024-03-15T14:00:00Z<p>by Pahala Gedara Jayathilake, Pedro Victori, Clara E. Pavillet, Chang Heon Lee, Dimitrios Voukantsis, Ana Miar, Anjali Arora, Adrian L. Harris, Karl J. Morten, Francesca M. Buffa</p>
Deregulated metabolism is one of the hallmarks of cancer. It is well-known that tumour cells tend to metabolize glucose via glycolysis even when oxygen is available and mitochondrial respiration is functional. However, the lower energy efficiency of aerobic glycolysis with respect to mitochondrial respiration makes this behaviour, namely the Warburg effect, counter-intuitive, although it has now been recognized as source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis. We employed a multi-scale approach that integrates multi-agent modelling, diffusion-reaction, stoichiometric equations, and Boolean networks to study metabolic cooperation between hypoxic and oxygenated cells exposed to varying oxygen, nutrient, and inhibitor concentrations. The results show that the cooperation reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the oxygen level was found to be decreased by symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic populations may gradually reach quasi-equilibrium. A sensitivity analysis using Latin hypercube sampling and partial rank correlation shows that the symbiotic dynamics depends on properties of the specific cell such as the minimum glucose level needed for glycolysis. Our results suggest that strategies that block glucose transporters may be more effective to reduce tumour growth than those blocking lactate intake transporters.Bayesian inference of relative fitness on high-throughput pooled competition assaysManuel Razo-MejiaMadhav ManiDmitri Petrov10.1371/journal.pcbi.10119372024-03-15T14:00:00Z2024-03-15T14:00:00Z<p>by Manuel Razo-Mejia, Madhav Mani, Dmitri Petrov</p>
The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.For long-term sustainable software in bioinformaticsLuis Pedro Coelho10.1371/journal.pcbi.10119202024-03-15T14:00:00Z2024-03-15T14:00:00Z<p>by Luis Pedro Coelho</p>Evolutionary graph theory beyond pairwise interactions: Higher-order network motifs shape times to fixation in structured populationsYang Ping KuoOana Carja10.1371/journal.pcbi.10119052024-03-15T14:00:00Z2024-03-15T14:00:00Z<p>by Yang Ping Kuo, Oana Carja</p>
To design population topologies that can accelerate rates of solution discovery in directed evolution problems or for evolutionary optimization applications, we must first systematically understand how population structure shapes evolutionary outcome. Using the mathematical formalism of evolutionary graph theory, recent studies have shown how to topologically build networks of population interaction that increase probabilities of fixation of beneficial mutations, at the expense, however, of longer fixation times, which can slow down rates of evolution, under elevated mutation rate. Here we find that moving beyond dyadic interactions in population graphs is fundamental to explain the trade-offs between probabilities and times to fixation of new mutants in the population. We show that higher-order motifs, and in particular three-node structures, allow the tuning of times to fixation, without changes in probabilities of fixation. This gives a near-continuous control over achieving solutions that allow for a wide range of times to fixation. We apply our algorithms and analytic results to two evolutionary optimization problems and show that the rate of solution discovery can be tuned near continuously by adjusting the higher-order topology of the population. We show that the effects of population structure on the rate of evolution critically depend on the optimization landscape and find that decelerators, with longer times to fixation of new mutants, are able to reach the optimal solutions faster than accelerators in complex solution spaces. Our results highlight that no one population topology fits all optimization applications, and we provide analytic and computational tools that allow for the design of networks suitable for each specific task.Ensemble dynamics and information flow deduction from whole-brain imaging dataYu ToyoshimaHirofumi SatoDaiki NagataManami KanamoriMoon Sun JangKoyo KuzeSuzu OeTakayuki TeramotoYuishi IwasakiRyo YoshidaTakeshi IshiharaYuichi Iino10.1371/journal.pcbi.10118482024-03-15T14:00:00Z2024-03-15T14:00:00Z<p>by Yu Toyoshima, Hirofumi Sato, Daiki Nagata, Manami Kanamori, Moon Sun Jang, Koyo Kuze, Suzu Oe, Takayuki Teramoto, Yuishi Iwasaki, Ryo Yoshida, Takeshi Ishihara, Yuichi Iino</p>
The recent advancements in large-scale activity imaging of neuronal ensembles offer valuable opportunities to comprehend the process involved in generating brain activity patterns and understanding how information is transmitted between neurons or neuronal ensembles. However, existing methodologies for extracting the underlying properties that generate overall dynamics are still limited. In this study, we applied previously unexplored methodologies to analyze time-lapse 3D imaging (4D imaging) data of head neurons of the nematode <i>Caenorhabditis elegans</i>. By combining time-delay embedding with the independent component analysis, we successfully decomposed whole-brain activities into a small number of component dynamics. Through the integration of results from multiple samples, we extracted common dynamics from neuronal activities that exhibit apparent divergence across different animals. Notably, while several components show common cooperativity across samples, some component pairs exhibited distinct relationships between individual samples. We further developed time series prediction models of synaptic communications. By combining dimension reduction using the general framework, gradient kernel dimension reduction, and probabilistic modeling, the overall relationships of neural activities were incorporated. By this approach, the stochastic but coordinated dynamics were reproduced in the simulated whole-brain neural network. We found that noise in the nervous system is crucial for generating realistic whole-brain dynamics. Furthermore, by evaluating synaptic interaction properties in the models, strong interactions within the core neural circuit, variable sensory transmission and importance of gap junctions were inferred. Virtual optogenetics can be also performed using the model. These analyses provide a solid foundation for understanding information flow in real neural networks.Impact on backpropagation of the spatial heterogeneity of sodium channel kinetics in the axon initial segmentBenjamin S. M. BarlowAndré LongtinBéla Joós10.1371/journal.pcbi.10118462024-03-15T14:00:00Z2024-03-15T14:00:00Z<p>by Benjamin S. M. Barlow, André Longtin, Béla Joós</p>
In a variety of neurons, action potentials (APs) initiate at the proximal axon, within a region called the axon initial segment (AIS), which has a high density of voltage-gated sodium channels (Na<sub>V</sub>s) on its membrane. In pyramidal neurons, the proximal AIS has been reported to exhibit a higher proportion of Na<sub>V</sub>s with gating properties that are “<i>right-shifted</i>” to more depolarized voltages, compared to the distal AIS. Further, recent experiments have revealed that as neurons develop, the spatial distribution of Na<sub>V</sub> subtypes along the AIS can change substantially, suggesting that neurons tune their excitability by modifying said distribution. When neurons are stimulated axonally, computational modelling has shown that this spatial separation of gating properties in the AIS enhances the backpropagation of APs into the dendrites. In contrast, in the more natural scenario of somatic stimulation, our simulations show that the same distribution can impede backpropagation, suggesting that the choice of orthodromic versus antidromic stimulation can bias or even invert experimental findings regarding the role of Na<sub>V</sub> subtypes in the AIS. We implemented a range of hypothetical Na<sub>V</sub> distributions in the AIS of three multicompartmental pyramidal cell models and investigated the precise kinetic mechanisms underlying such effects, as the spatial distribution of Na<sub>V</sub> subtypes is varied. With axonal stimulation, proximal Na<sub>V</sub> <i>availability</i> dominates, such that concentrating <i>right-shifted</i> Na<sub>V</sub>s in the proximal AIS promotes backpropagation. However, with somatic stimulation, the models are insensitive to <i>availability</i> kinetics. Instead, the higher <i>activation</i> threshold of <i>right-shifted</i> Na<sub>V</sub>s in the AIS impedes backpropagation. Therefore, recently observed developmental changes to the spatial separation and relative proportions of Na<sub>V</sub>1.2 and Na<sub>V</sub>1.6 in the AIS differentially impact <i>activation</i> and <i>availability</i>. The observed effects on backpropagation, and potentially learning via its putative role in synaptic plasticity (e.g. through spike-timing-dependent plasticity), are opposite for orthodromic versus antidromic stimulation, which should inform hypotheses about the impact of the developmentally regulated subcellular localization of these Na<sub>V</sub> subtypes.Unlocking ensemble ecosystem modelling for large and complex networksSarah A. VollertChristopher DrovandiMatthew P. Adams10.1371/journal.pcbi.10119762024-03-14T14:00:00Z2024-03-14T14:00:00Z<p>by Sarah A. Vollert, Christopher Drovandi, Matthew P. Adams</p>
The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models by forecasting populations with and without intervention. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensemble-generation methods become computationally inefficient as the size of the ecosystem network increases, preventing larger networks from being studied. We present a novel sequential Monte Carlo sampling approach for ensemble generation that is orders of magnitude faster than existing approaches. We demonstrate that the methods produce equivalent parameter inferences, model predictions, and tightly constrained parameter combinations using a novel sensitivity analysis method. For one case study, we demonstrate a speed-up from 108 days to 6 hours, while maintaining equivalent ensembles. Additionally, we demonstrate how to identify the parameter combinations that strongly drive feasibility and stability, drawing ecological insight from the ensembles. Now, for the first time, larger and more realistic networks can be practically simulated and analysed.CRISPR-M: Predicting sgRNA off-target effect using a multi-view deep learning networkJialiang SunJun GuoJian Liu10.1371/journal.pcbi.10119722024-03-14T14:00:00Z2024-03-14T14:00:00Z<p>by Jialiang Sun, Jun Guo, Jian Liu</p>
Using the CRISPR-Cas9 system to perform base substitutions at the target site is a typical technique for genome editing with the potential for applications in gene therapy and agricultural productivity. When the CRISPR-Cas9 system uses guide RNA to direct the Cas9 endonuclease to the target site, it may misdirect it to a potential off-target site, resulting in an unintended genome editing. Although several computational methods have been proposed to predict off-target effects, there is still room for improvement in the off-target effect prediction capability. In this paper, we present an effective approach called CRISPR-M with a new encoding scheme and a novel multi-view deep learning model to predict the sgRNA off-target effects for target sites containing indels and mismatches. CRISPR-M takes advantage of convolutional neural networks and bidirectional long short-term memory recurrent neural networks to construct a three-branch network towards multi-views. Compared with existing methods, CRISPR-M demonstrates significant performance advantages running on real-world datasets. Furthermore, experimental analysis of CRISPR-M under multiple metrics reveals its capability to extract features and validates its superiority on sgRNA off-target effect predictions.kCSD-python, reliable current source density estimation with quality controlChaitanya ChintaluriMarta BejtkaWładysław ŚredniawaMichał CzerwińskiJakub M. DzikJoanna Jędrzejewska-SzmekDaniel K. Wójcik10.1371/journal.pcbi.10119412024-03-14T14:00:00Z2024-03-14T14:00:00Z<p>by Chaitanya Chintaluri, Marta Bejtka, Władysław Średniawa, Michał Czerwiński, Jakub M. Dzik, Joanna Jędrzejewska-Szmek, Daniel K. Wójcik</p>
Interpretation of extracellular recordings can be challenging due to the long range of electric field. This challenge can be mitigated by estimating the current source density (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method and related tools to facilitate CSD analysis of experimental data and the interpretation of results. We show how to counter the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for an arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter Notebook tutorial which illustrates a typical analytical workflow and main functionalities useful in validating analysis results.Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteinsMoritz ErteltVikram Khipple MulliganJack B. MaguireSergey LyskovRocco MorettiTorben SchiffnerJens MeilerClara T. Schoeder10.1371/journal.pcbi.10119392024-03-14T14:00:00Z2024-03-14T14:00:00Z<p>by Moritz Ertelt, Vikram Khipple Mulligan, Jack B. Maguire, Sergey Lyskov, Rocco Moretti, Torben Schiffner, Jens Meiler, Clara T. Schoeder</p>
Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta’s protein engineering toolbox that allow for the rational design of PTMs.Mathematical models of <i>Plasmodium vivax</i> transmission: A scoping reviewMd Nurul AnwarLauren SmithAngela DevineSomya MehraCamelia R. WalkerElizabeth IvoryEamon ConwayIvo MuellerJames M. McCawJennifer A. FleggRoslyn I. Hickson10.1371/journal.pcbi.10119312024-03-14T14:00:00Z2024-03-14T14:00:00Z<p>by Md Nurul Anwar, Lauren Smith, Angela Devine, Somya Mehra, Camelia R. Walker, Elizabeth Ivory, Eamon Conway, Ivo Mueller, James M. McCaw, Jennifer A. Flegg, Roslyn I. Hickson</p>
<i>Plasmodium vivax</i> is one of the most geographically widespread malaria parasites in the world, primarily found across South-East Asia, Latin America, and parts of Africa. One of the significant characteristics of the <i>P. vivax</i> parasite is its ability to remain dormant in the human liver as hypnozoites and subsequently reactivate after the initial infection (i.e. relapse infections). Mathematical modelling approaches have been widely applied to understand <i>P. vivax</i> dynamics and predict the impact of intervention outcomes. Models that capture <i>P. vivax</i> dynamics differ from those that capture <i>P. falciparum</i> dynamics, as they must account for relapses caused by the activation of hypnozoites. In this article, we provide a scoping review of mathematical models that capture <i>P. vivax</i> transmission dynamics published between January 1988 and May 2023. The primary objective of this work is to provide a comprehensive summary of the mathematical models and techniques used to model <i>P. vivax</i> dynamics. In doing so, we aim to assist researchers working on mathematical epidemiology, disease transmission, and other aspects of <i>P. vivax</i> malaria by highlighting best practices in currently published models and highlighting where further model development is required. We categorise <i>P. vivax</i> models according to whether a deterministic or agent-based approach was used. We provide an overview of the different strategies used to incorporate the parasite’s biology, use of multiple scales (within-host and population-level), superinfection, immunity, and treatment interventions. In most of the published literature, the rationale for different modelling approaches was driven by the research question at hand. Some models focus on the parasites’ complicated biology, while others incorporate simplified assumptions to avoid model complexity. Overall, the existing literature on mathematical models for <i>P. vivax</i> encompasses various aspects of the parasite’s dynamics. We recommend that future research should focus on refining how key aspects of <i>P. vivax</i> dynamics are modelled, including spatial heterogeneity in exposure risk and heterogeneity in susceptibility to infection, the accumulation of hypnozoite variation, the interaction between <i>P. falciparum</i> and <i>P. vivax</i>, acquisition of immunity, and recovery under superinfection.Computational prediction of protein interactions in single cells by proximity sequencingJunjie XiaHoang Van PhanLuke VistainMengjie ChenAly A. KhanSavaş Tay10.1371/journal.pcbi.10119152024-03-14T14:00:00Z2024-03-14T14:00:00Z<p>by Junjie Xia, Hoang Van Phan, Luke Vistain, Mengjie Chen, Aly A. Khan, Savaş Tay</p>
Proximity sequencing (Prox-seq) simultaneously measures gene expression, protein expression and protein complexes on single cells. Using information from dual-antibody binding events, Prox-seq infers surface protein dimers at the single-cell level. Prox-seq provides multi-dimensional phenotyping of single cells in high throughput, and was recently used to track the formation of receptor complexes during cell signaling and discovered a novel interaction between CD9 and CD8 in naïve T cells. The distribution of protein abundance can affect identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model of Prox-seq and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq data, which resulted in more accurate and robust quantification of protein complexes. Finally, our Prox-seq model offers a simple way to investigate the behavior of Prox-seq data under various biological conditions and guide users toward selecting the best analysis method for their data.An analytically tractable, age-structured model of the impact of vector control on mosquito-transmitted infectionsEmma L. DavisT. Déirdre HollingsworthMatt J. Keeling10.1371/journal.pcbi.10114402024-03-14T14:00:00Z2024-03-14T14:00:00Z<p>by Emma L. Davis, T. Déirdre Hollingsworth, Matt J. Keeling</p>
Vector control is a vital tool utilised by malaria control and elimination programmes worldwide, and as such it is important that we can accurately quantify the expected public health impact of these methods. There are very few previous models that consider vector-control-induced changes in the age-structure of the vector population and the resulting impact on transmission. We analytically derive the steady-state solution of a novel age-structured deterministic compartmental model describing the mosquito feeding cycle, with mosquito age represented discretely by parity—the number of cycles (or successful bloodmeals) completed. Our key model output comprises an explicit, analytically tractable solution that can be used to directly quantify key transmission statistics, such as the effective reproductive ratio under control, <i>R</i><sub>c</sub>, and investigate the age-structured impact of vector control. Application of this model reinforces current knowledge that adult-acting interventions, such as indoor residual spraying of insecticides (IRS) or long-lasting insecticidal nets (LLINs), can be highly effective at reducing transmission, due to the dual effects of repelling and killing mosquitoes. We also demonstrate how larval measures can be implemented in addition to adult-acting measures to reduce <i>R</i><sub>c</sub> and mitigate the impact of waning insecticidal efficacy, as well as how mid-ranges of LLIN coverage are likely to experience the largest effect of reduced net integrity on transmission. We conclude that whilst well-maintained adult-acting vector control measures are substantially more effective than larval-based interventions, incorporating larval control in existing LLIN or IRS programmes could substantially reduce transmission and help mitigate any waning effects of adult-acting measures.Bayesian inference is facilitated by modular neural networks with different time scalesKohei IchikawaKunihiko Kaneko10.1371/journal.pcbi.10118972024-03-13T14:00:00Z2024-03-13T14:00:00Z<p>by Kohei Ichikawa, Kunihiko Kaneko</p>
Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference by the brain, the prior distribution must be acquired and represented by sampling noisy external inputs. However, the mechanism by which neural activities represent such distributions has not yet been elucidated. Our findings reveal that networks with modular structures, composed of fast and slow modules, are adept at representing this prior distribution, enabling more accurate Bayesian inferences. Specifically, the modular network that consists of a main module connected with input and output layers and a sub-module with slower neural activity connected only with the main module outperformed networks with uniform time scales. Prior information was represented specifically by the slow sub-module, which could integrate observed signals over an appropriate period and represent input means and variances. Accordingly, the neural network could effectively predict the time-varying inputs. Furthermore, by training the time scales of neurons starting from networks with uniform time scales and without modular structure, the above slow-fast modular network structure and the division of roles in which prior knowledge is selectively represented in the slow sub-modules spontaneously emerged. These results explain how the prior distribution for Bayesian inference is represented in the brain, provide insight into the relevance of modular structure with time scale hierarchy to information processing, and elucidate the significance of brain areas with slower time scales.End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imagingTakehiro AjiokaNobuhiro NakaiOkito YamashitaToru Takumi10.1371/journal.pcbi.10110742024-03-13T14:00:00Z2024-03-13T14:00:00Z<p>by Takehiro Ajioka, Nobuhiro Nakai, Okito Yamashita, Toru Takumi</p>
Deep learning is a powerful tool for neural decoding, broadly applied to systems neuroscience and clinical studies. Interpretable and transparent models that can explain neural decoding for intended behaviors are crucial to identifying essential features of deep learning decoders in brain activity. In this study, we examine the performance of deep learning to classify mouse behavioral states from mesoscopic cortex-wide calcium imaging data. Our convolutional neural network (CNN)-based end-to-end decoder combined with recurrent neural network (RNN) classifies the behavioral states with high accuracy and robustness to individual differences on temporal scales of sub-seconds. Using the CNN-RNN decoder, we identify that the forelimb and hindlimb areas in the somatosensory cortex significantly contribute to behavioral classification. Our findings imply that the end-to-end approach has the potential to be an interpretable deep learning method with unbiased visualization of critical brain regions.AI-Aristotle: A physics-informed framework for systems biology gray-box identificationNazanin Ahmadi DaryakenariMario De FlorioKhemraj ShuklaGeorge Em Karniadakis10.1371/journal.pcbi.10119162024-03-12T14:00:00Z2024-03-12T14:00:00Z<p>by Nazanin Ahmadi Daryakenari, Mario De Florio, Khemraj Shukla, George Em Karniadakis</p>
Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identification (gray-box) in the field of Systems Biology. The proposed framework—named AI-Aristotle—combines the eXtreme Theory of Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural Networks (PINNs) with symbolic regression (SR) techniques for parameter discovery and gray-box identification. We test the accuracy, speed, flexibility, and robustness of AI-Aristotle based on two benchmark problems in Systems Biology: a pharmacokinetics drug absorption model and an ultradian endocrine model for glucose-insulin interactions. We compare the two machine learning methods (X-TFC and PINNs), and moreover, we employ two different symbolic regression techniques to cross-verify our results. To test the performance of AI-Aristotle, we use sparse synthetic data perturbed by uniformly distributed noise. More broadly, our work provides insights into the accuracy, cost, scalability, and robustness of integrating neural networks with symbolic regressors, offering a comprehensive guide for researchers tackling gray-box identification challenges in complex dynamical systems in biomedicine and beyond.It is theoretically possible to avoid misfolding into non-covalent lasso entanglements using small molecule drugsYang JiangCharlotte M. DeaneGarrett M. MorrisEdward P. O’Brien10.1371/journal.pcbi.10119012024-03-12T14:00:00Z2024-03-12T14:00:00Z<p>by Yang Jiang, Charlotte M. Deane, Garrett M. Morris, Edward P. O’Brien</p>
A novel class of protein misfolding characterized by either the formation of non-native noncovalent lasso entanglements in the misfolded structure or loss of native entanglements has been predicted to exist and found circumstantial support through biochemical assays and limited-proteolysis mass spectrometry data. Here, we examine whether it is possible to design small molecule compounds that can bind to specific folding intermediates and thereby avoid these misfolded states in computer simulations under idealized conditions (perfect drug-binding specificity, zero promiscuity, and a smooth energy landscape). Studying two proteins, type III chloramphenicol acetyltransferase (CAT-III) and D-alanyl-D-alanine ligase B (DDLB), that were previously suggested to form soluble misfolded states through a mechanism involving a failure-to-form of native entanglements, we explore two different drug design strategies using coarse-grained structure-based models. The first strategy, in which the native entanglement is stabilized by drug binding, failed to decrease misfolding because it formed an alternative entanglement at a nearby region. The second strategy, in which a small molecule was designed to bind to a non-native tertiary structure and thereby destabilize the native entanglement, succeeded in decreasing misfolding and increasing the native state population. This strategy worked because destabilizing the entanglement loop provided more time for the threading segment to position itself correctly to be wrapped by the loop to form the native entanglement. Further, we computationally identified several FDA-approved drugs with the potential to bind these intermediate states and rescue misfolding in these proteins. This study suggests it is possible for small molecule drugs to prevent protein misfolding of this type.Cortical cell assemblies and their underlying connectivity: An <i>in silico</i> studyAndrás EckerDaniela Egas SantanderSirio Bolaños-PuchetJames B. IsbisterMichael W. Reimann10.1371/journal.pcbi.10118912024-03-11T14:00:00Z2024-03-11T14:00:00Z<p>by András Ecker, Daniela Egas Santander, Sirio Bolaños-Puchet, James B. Isbister, Michael W. Reimann</p>
Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb’s postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation to analyze extracellular viral markersKosaku KitagawaKwang Su KimMasashi IwamotoSanae HayashiHyeongki ParkTakara NishiyamaNaotoshi NakamuraYasuhisa FujitaShinji NakaokaKazuyuki AiharaAlan S. PerelsonLena AllweissMaura DandriKoichi WatashiYasuhito TanakaShingo Iwami10.1371/journal.pcbi.10112382024-03-11T14:00:00Z2024-03-11T14:00:00Z<p>by Kosaku Kitagawa, Kwang Su Kim, Masashi Iwamoto, Sanae Hayashi, Hyeongki Park, Takara Nishiyama, Naotoshi Nakamura, Yasuhisa Fujita, Shinji Nakaoka, Kazuyuki Aihara, Alan S. Perelson, Lena Allweiss, Maura Dandri, Koichi Watashi, Yasuhito Tanaka, Shingo Iwami</p>
Chronic infection with hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. Thus, quantifying and understanding the dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, such study requires repeated liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because liver biopsy is potentially morbid and not common during hepatitis B treatment. We here aimed to develop a noninvasive method for quantifying cccDNA in the liver using surrogate markers in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations, integrates experimental data from in vitro and in vivo investigations. By applying this model, we roughly predicted the amount and dynamics of intrahepatic cccDNA within a certain range using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The noninvasive quantification of cccDNA using our proposed method holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.Using early detection data to estimate the date of emergence of an epidemic outbreakSofía JijónPeter CzupponFrançois BlanquartFlorence Débarre10.1371/journal.pcbi.10119342024-03-08T14:00:00Z2024-03-08T14:00:00Z<p>by Sofía Jijón, Peter Czuppon, François Blanquart, Florence Débarre</p>
While the first infection of an emerging disease is often unknown, information on early cases can be used to date it. In the context of the COVID-19 pandemic, previous studies have estimated dates of emergence (e.g., first human SARS-CoV-2 infection, emergence of the Alpha SARS-CoV-2 variant) using mainly genomic data. Another dating attempt used a stochastic population dynamics approach and the date of the first reported case. Here, we extend this approach to use a larger set of early reported cases to estimate the delay from first infection to the <i>N</i><sup>th</sup> case. We first validate our framework by running our model on simulated data. We then apply our model using data on Alpha variant infections in the UK, dating the first Alpha infection at (median) August 21, 2020 (95% interpercentile range across retained simulations (IPR): July 23–September 5, 2020). Next, we apply our model to data on COVID-19 cases with symptom onset before mid-January 2020. We date the first SARS-CoV-2 infection in Wuhan at (median) November 28, 2019 (95% IPR: November 2–December 9, 2019). Our results fall within ranges previously estimated by studies relying on genomic data. Our population dynamics-based modelling framework is generic and flexible, and thus can be applied to estimate the starting time of outbreaks in contexts other than COVID-19.teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineeringSøren D. PetersenLucas LevassorChristine M. PedersenJan MadsenLea G. HansenJie ZhangAhmad K. HaidarRasmus J. N. FrandsenJay D. KeaslingTilmann WeberNikolaus SonnenscheinMichael K. Jensen10.1371/journal.pcbi.10119292024-03-08T14:00:00Z2024-03-08T14:00:00Z<p>by Søren D. Petersen, Lucas Levassor, Christine M. Pedersen, Jan Madsen, Lea G. Hansen, Jie Zhang, Ahmad K. Haidar, Rasmus J. N. Frandsen, Jay D. Keasling, Tilmann Weber, Nikolaus Sonnenschein, Michael K. Jensen</p>
Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisionsJessica S. YuNeda Bagheri10.1371/journal.pcbi.10119172024-03-08T14:00:00Z2024-03-08T14:00:00Z<p>by Jessica S. Yu, Neda Bagheri</p>
Computational models enable scientists to understand observed dynamics, uncover rules underlying behaviors, predict experimental outcomes, and generate new hypotheses. There are countless modeling approaches that can be used to characterize biological systems, further multiplied when accounting for the variety of model design choices. Many studies focus on the impact of model parameters on model output and performance; fewer studies investigate the impact of model design choices on biological insight. Here we demonstrate why model design choices should be deliberate and intentional in context of the specific research system and question. In this study, we analyze agnostic and broadly applicable modeling choices at three levels—system, cell, and environment—within the same agent-based modeling framework to interrogate their impact on temporal, spatial, and single-cell emergent dynamics. We identify key considerations when making these modeling choices, including the (i) differences between qualitative vs. quantitative results driven by choices in system representation, (ii) impact of cell-to-cell variability choices on cell-level and temporal trends, and (iii) relationship between emergent outcomes and choices of nutrient dynamics in the environment. This generalizable investigation can help guide the choices made when developing biological models that aim to characterize spatial-temporal dynamics.Mechanistic insights into ligand dissociation from the SARS-CoV-2 spike glycoproteinTimothy HasseEsra ManteiRezvan ShahoeiShristi PawnikarJinan WangYinglong MiaoYu-ming M. Huang10.1371/journal.pcbi.10119552024-03-07T14:00:00Z2024-03-07T14:00:00Z<p>by Timothy Hasse, Esra Mantei, Rezvan Shahoei, Shristi Pawnikar, Jinan Wang, Yinglong Miao, Yu-ming M. Huang</p>
The COVID-19 pandemic, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spurred an urgent need for effective therapeutic interventions. The spike glycoprotein of the SARS-CoV-2 is crucial for infiltrating host cells, rendering it a key candidate for drug development. By interacting with the human angiotensin-converting enzyme 2 (ACE2) receptor, the spike initiates the infection of SARS-CoV-2. Linoleate is known to bind the spike glycoprotein, subsequently reducing its interaction with ACE2. However, the detailed mechanisms underlying the protein-ligand interaction remain unclear. In this study, we characterized the pathways of ligand dissociation and the conformational changes associated with the spike glycoprotein by using ligand Gaussian accelerated molecular dynamics (LiGaMD). Our simulations resulted in eight complete ligand dissociation trajectories, unveiling two distinct ligand unbinding pathways. The preference between these two pathways depends on the gate distance between two α-helices in the receptor binding domain (RBD) and the position of the N-linked glycan at N343. Our study also highlights the essential contributions of K417, N121 glycan, and N165 glycan in ligand unbinding, which are equally crucial in enhancing spike-ACE2 binding. We suggest that the presence of the ligand influences the motions of these residues and glycans, consequently reducing accessibility for spike-ACE2 binding. These findings enhance our understanding of ligand dissociation from the spike glycoprotein and offer significant implications for drug design strategies in the battle against COVID-19.Modeling circuit mechanisms of opposing cortical responses to visual flow perturbationsJ. Galván FraileFranz ScherrJosé J. RamascoAnton ArkhipovWolfgang MaassClaudio R. Mirasso10.1371/journal.pcbi.10119212024-03-07T14:00:00Z2024-03-07T14:00:00Z<p>by J. Galván Fraile, Franz Scherr, José J. Ramasco, Anton Arkhipov, Wolfgang Maass, Claudio R. Mirasso</p>
In an ever-changing visual world, animals’ survival depends on their ability to perceive and respond to rapidly changing motion cues. The primary visual cortex (V1) is at the forefront of this sensory processing, orchestrating neural responses to perturbations in visual flow. However, the underlying neural mechanisms that lead to distinct cortical responses to such perturbations remain enigmatic. In this study, our objective was to uncover the neural dynamics that govern V1 neurons’ responses to visual flow perturbations using a biologically realistic computational model. By subjecting the model to sudden changes in visual input, we observed opposing cortical responses in excitatory layer 2/3 (L2/3) neurons, namely, depolarizing and hyperpolarizing responses. We found that this segregation was primarily driven by the competition between external visual input and recurrent inhibition, particularly within L2/3 and L4. This division was not observed in excitatory L5/6 neurons, suggesting a more prominent role for inhibitory mechanisms in the visual processing of the upper cortical layers. Our findings share similarities with recent experimental studies focusing on the opposing influence of top-down and bottom-up inputs in the mouse primary visual cortex during visual flow perturbations.Bifurcations and bursting in the EpileptorMaria Luisa SaggioViktor Jirsa10.1371/journal.pcbi.10119032024-03-06T14:00:00Z2024-03-06T14:00:00Z<p>by Maria Luisa Saggio, Viktor Jirsa</p>
The Epileptor is a phenomenological model for seizure activity that is used in a personalized large-scale brain modeling framework, the Virtual Epileptic Patient, with the aim of improving surgery outcomes for drug-resistant epileptic patients. Transitions between interictal and ictal states are modeled as bifurcations, enabling the definition of seizure classes in terms of onset/offset bifurcations. This establishes a taxonomy of seizures grounded in their essential underlying dynamics and the Epileptor replicates the activity of the most common class, as observed in patients with focal epilepsy, which is characterized by square-wave bursting properties. The Epileptor also encodes an additional mechanism to account for interictal spikes and spike and wave discharges. Here we use insights from a more generic model for square-wave bursting, based on the Unfolding Theory approach, to guide the bifurcation analysis of the Epileptor and gain a deeper understanding of the model and the role of its parameters. We show how the Epileptor’s parameters can be modified to produce activities for other seizures classes of the taxonomy, as observed in patients, so that the large-scale brain models could be further personalized. Some of these classes have already been described in the literature in the Epileptor, others, predicted by the generic model, are new. Finally, we unveil how the interaction with the additional mechanism for spike and wave discharges alters the bifurcation structure of the main burster.Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening dataChenyu WuEinar Bjarki GunnarssonEven Moa MyklebustAlvaro Köhn-LuqueDagim Shiferaw TadeleJorrit Martijn EnserinkArnoldo FrigessiJasmine FooKevin Leder10.1371/journal.pcbi.10118882024-03-06T14:00:00Z2024-03-06T14:00:00Z<p>by Chenyu Wu, Einar Bjarki Gunnarsson, Even Moa Myklebust, Alvaro Köhn-Luque, Dagim Shiferaw Tadele, Jorrit Martijn Enserink, Arnoldo Frigessi, Jasmine Foo, Kevin Leder</p>
Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (<i>in silico</i>) and experimental data (<i>in vitro</i>), which supports our argument about its advantages.Unsupervised learning of perceptual feature combinationsMinija TamosiunaiteChristian TetzlaffFlorentin Wörgötter10.1371/journal.pcbi.10119262024-03-05T14:00:00Z2024-03-05T14:00:00Z<p>by Minija Tamosiunaite, Christian Tetzlaff, Florentin Wörgötter</p>
In many situations it is behaviorally relevant for an animal to respond to co-occurrences of perceptual, possibly polymodal features, while these features alone may have no importance. Thus, it is crucial for animals to learn such feature combinations in spite of the fact that they may occur with variable intensity and occurrence frequency. Here, we present a novel unsupervised learning mechanism that is largely independent of these contingencies and allows neurons in a network to achieve specificity for different feature combinations. This is achieved by a novel correlation-based (Hebbian) learning rule, which allows for linear weight growth and which is combined with a mechanism for gradually reducing the learning rate as soon as the neuron’s response becomes feature combination specific. In a set of control experiments, we show that other existing advanced learning rules cannot satisfactorily form ordered multi-feature representations. In addition, we show that networks, which use this type of learning always stabilize and converge to subsets of neurons with different feature-combination specificity. Neurons with this property may, thus, serve as an initial stage for the processing of ecologically relevant real world situations for an animal.Decomposing bulk signals to reveal hidden information in processive enzyme reactions: A case study in mRNA translationNadin HaaseWolf HoltkampSimon ChristDag HeinemannMarina V. RodninaSophia Rudorf10.1371/journal.pcbi.10119182024-03-05T14:00:00Z2024-03-05T14:00:00Z<p>by Nadin Haase, Wolf Holtkamp, Simon Christ, Dag Heinemann, Marina V. Rodnina, Sophia Rudorf</p>
Processive enzymes like polymerases or ribosomes are often studied in bulk experiments by monitoring time-dependent signals, such as fluorescence time traces. However, due to biomolecular process stochasticity, ensemble signals may lack the distinct features of single-molecule signals. Here, we demonstrate that, under certain conditions, bulk signals from processive reactions can be decomposed to unveil hidden information about individual reaction steps. Using mRNA translation as a case study, we show that decomposing a noisy ensemble signal generated by the translation of mRNAs with more than a few codons is an ill-posed problem, addressable through Tikhonov regularization. We apply our method to the fluorescence signatures of <i>in-vitro</i> translated LepB mRNA and determine codon-position dependent translation rates and corresponding state-specific fluorescence intensities. We find a significant change in fluorescence intensity after the fourth and the fifth peptide bond formation, and show that both codon position and encoded amino acid have an effect on the elongation rate. This demonstrates that our approach enhances the information content extracted from bulk experiments, thereby expanding the range of these time- and cost-efficient methods.