arXiv Quantitative Biology @arxiv_bio@qoto.org

Cerebral aneurysms are pathological dilations of intracranial arteries that can rupture with devastating consequences, including subarachnoid hemorrhage, stroke, and death. Accumulating evidence indicates that local hemodynamic forces play a critical role in aneurysm initiation, growth, and rupture. Computational fluid dynamics (CFD) and imaging-based techniques have enabled the extraction of various hemodynamic variables to characterize these flow conditions. However, the literature is highly fragmented, with different studies adopting distinct sets of metrics such as wall shear stress (WSS), oscillatory shear index (OSI), wall shear stress gradient (WSSG), relative residence time (RRT), or endothelial cell activation potential (ECAP) making it difficult to compare results or establish standardized methodologies. This paper provides the first comprehensive catalog of hemodynamic variables used in cerebral aneurysm studies to date. By systematically identifying and organizing these parameters based on their physical basis and frequency of use, this work offers a consolidated reference to guide future research. The goal is to support consistent variable selection, enhance reproducibility, and facilitate the design of more robust studies linking vascular biomechanics to aneurysm pathophysiology. This review aims to serve as a foundational resource for researchers and clinicians seeking to incorporate hemodynamic modeling into cerebral aneurysm analysis and risk assessment.

Respiratory viral infections pose a global health burden, yet the cellular immune responses driving protection or pathology remain unclear. Natural infection cohorts often lack pre-exposure baseline data and structured temporal sampling. In contrast, inoculation and vaccination trials generate insightful longitudinal transcriptomic data. However, the scattering of these datasets across platforms, along with inconsistent metadata and preprocessing procedure, hinders AI-driven discovery. To address these challenges, we developed the Human Respiratory Viral Immunization LongitudinAl Gene Expression (HR-VILAGE-3K3M) repository: an AI-ready, rigorously curated dataset that integrates 14,136 RNA-seq profiles from 3,178 subjects across 66 studies encompassing over 2.56 million cells. Spanning vaccination, inoculation, and mixed exposures, the dataset includes microarray, bulk RNA-seq, and single-cell RNA-seq from whole blood, PBMCs, and nasal swabs, sourced from GEO, ImmPort, and ArrayExpress. We harmonized subject-level metadata, standardized outcome measures, applied unified preprocessing pipelines with rigorous quality control, and aligned all data to official gene symbols. To demonstrate the utility of HR-VILAGE-3K3M, we performed predictive modeling of vaccine responders and evaluated batch-effect correction methods. Beyond these initial demonstrations, it supports diverse systems immunology applications and benchmarking of feature selection and transfer learning algorithms. Its scale and heterogeneity also make it ideal for pretraining foundation models of the human immune response and for advancing multimodal learning frameworks. As the largest longitudinal transcriptomic resource for human respiratory viral immunization, it provides an accessible platform for reproducible AI-driven research, accelerating systems immunology and vaccine development against emerging viral threats.

Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, resulting in limited targeted treatment options. Neoadjuvant chemotherapy (NACT) is the standard treatment for early-stage TNBC, with pathologic complete response (pCR) serving as a key prognostic marker; however, only 40-50% of patients with TNBC achieve pCR. Accurate prediction of NACT response is crucial to optimize therapy, avoid ineffective treatments, and improve patient outcomes. In this study, we developed a deep learning model to predict NACT response using pre-treatment hematoxylin and eosin (H&E)-stained biopsy images. Our model achieved promising results in five-fold cross-validation (accuracy: 82%, AUC: 0.86, F1-score: 0.84, sensitivity: 0.85, specificity: 0.81, precision: 0.80). Analysis of model attention maps in conjunction with multiplexed immunohistochemistry (mIHC) data revealed that regions of high predictive importance consistently colocalized with tumor areas showing elevated PD-L1 expression, CD8+ T-cell infiltration, and CD163+ macrophage density - all established biomarkers of treatment response. Our findings indicate that incorporating IHC-derived immune profiling data could substantially improve model interpretability and predictive performance. Furthermore, this approach may accelerate the discovery of novel histopathological biomarkers for NACT and advance the development of personalized treatment strategies for TNBC patients.

The hippocampus orchestrates spatial navigation through collective place cell encodings that form cognitive maps. We reconceptualize the population of place cells as position embeddings approximating multi-scale symmetric random walk transition kernels: the inner product $\langle h(x, t), h(y, t) \rangle = q(y|x, t)$ represents normalized transition probabilities, where $h(x, t)$ is the embedding at location $ x $, and $q(y|x, t)$ is the normalized symmetric transition probability over time $t$. The time parameter $\sqrt{t}$ defines a spatial scale hierarchy, mirroring the hippocampal dorsoventral axis. $q(y|x, t)$ defines spatial adjacency between $x$ and $y$ at scale or resolution $\sqrt{t}$, and the pairwise adjacency relationships $(q(y|x, t), \forall x, y)$ are reduced into individual embeddings $(h(x, t), \forall x)$ that collectively form a map of the environment at sale $\sqrt{t}$. Our framework employs gradient ascent on $q(y|x, t) = \langle h(x, t), h(y, t)\rangle$ with adaptive scale selection, choosing the time scale with maximal gradient at each step for trap-free, smooth trajectories. Efficient matrix squaring $P_{2t} = P_t^2$ builds global representations from local transitions $P_1$ without memorizing past trajectories, enabling hippocampal preplay-like path planning. This produces robust navigation through complex environments, aligning with hippocampal navigation. Experimental results show that our model captures place cell properties -- field size distribution, adaptability, and remapping -- while achieving computational efficiency. By modeling collective transition probabilities rather than individual place fields, we offer a biologically plausible, scalable framework for spatial navigation.

Biological systems represent time from microseconds to years. An important gap in our knowledge concerns the mechanisms for encoding time intervals of hundreds of milliseconds to minutes that matter for tasks like navigation, communication, storage, recall, and prediction of stimulus patterns. A recently identified mechanism in fish thalamic neurons addresses this gap. Representation of intervals between events uses the ubiquitous property of neural fatigue, where firing adaptation sets in quickly during an event. The recovery from fatigue by the next stimulus is a monotonous function of time elapsed. Here we develop a full theory for the representation of intervals, allowing for recovery time scales and sensitivity to past stimuli to vary across cells. Our Bayesian framework combines parametrically heterogeneous stochastic dynamical modeling with interval priors to predict available timing information independent of actual decoding mechanism. A compromise is found between optimally encoding the latest time interval and previous ones, crucial for spatial navigation. Cellular heterogeneity is actually necessary to represent interval sequences, a novel computational role for experimentally observed heterogeneity. This biophysical adaptation-based timing memory shapes spatiotemporal information for efficient storage and recall in target recurrent networks.

Nucleic acids can form diverse non-canonical structures, such as G-quadruplexes (G4s) and i-motifs (iMs), which are critical in biological processes and disease pathways. This study presents an innovative probe design strategy based on groove size differences, leading to the development of BT-Cy-1, a supramolecular cyanine probe optimized by fine-tuning dimer "thickness". BT-Cy-1 demonstrated high sensitivity in detecting structural transitions and variations in G4s and iMs, even in complex environments with excess dsDNA. Applied to clinical blood samples, it revealed significant differences in RNA G4 and iM levels between liver cancer patients and healthy individuals, marking the first report of altered iM levels in clinical samples. This work highlights a novel approach for precise nucleic acid structural profiling, offering insights into their biological significance and potential in disease diagnostics.

Protein fitness optimization involves finding a protein sequence that maximizes desired quantitative properties in a combinatorially large design space of possible sequences. Recent developments in steering protein generative models (e.g diffusion models, language models) offer a promising approach. However, by and large, past studies have optimized surrogate rewards and/or utilized large amounts of labeled data for steering, making it unclear how well existing methods perform and compare to each other in real-world optimization campaigns where fitness is measured by low-throughput wet-lab assays. In this study, we explore fitness optimization using small amounts (hundreds) of labeled sequence-fitness pairs and comprehensively evaluate strategies such as classifier guidance and posterior sampling for guiding generation from different discrete diffusion models of protein sequences. We also demonstrate how guidance can be integrated into adaptive sequence selection akin to Thompson sampling in Bayesian optimization, showing that plug-and-play guidance strategies offer advantages compared to alternatives such as reinforcement learning with protein language models.

Common wisdom indicates that to implement a Dynamical Memory with spiking neurons two ingredients are necessary: recurrence and a neuron population. Here we shall show that the second requirement is not needed. We shall demonstrate that under very general assumptions a single recursive spiking neuron can realize a robust model of a dynamical memory. We demonstrate the implementation of a dynamical memory in both, software and hardware. In the former case, we introduce trivial extensions of the popular aQIF and AdEx models. In the latter, we show traces obtained in a circuit model with a recently proposed memristive spiking neuron. We show that the bistability of the theoretical models can be understood in terms of a self-consistent problem that can be represented geometrically. Our minimal dynamical memory model provides a simplest implementation of an important neuro-computational primitive, which can be useful in navigation system models based on purely spiking dynamics. A one neuron dynamical memory may also provides a natural explanation to the surprising recent observation that the excitation bump in Drosophila's ellipsoidal body is made by just a handful of neurons.

The identification of bitter peptides is crucial in various domains, including food science, drug discovery, and biochemical research. These peptides not only contribute to the undesirable taste of hydrolyzed proteins but also play key roles in physiological and pharmacological processes. However, experimental methods for identifying bitter peptides are time-consuming and expensive. With the rapid expansion of peptide sequence databases in the post-genomic era, the demand for efficient computational approaches to distinguish bitter from non-bitter peptides has become increasingly significant. In this study, we propose a novel stacking-based ensemble learning framework aimed at enhancing the accuracy and reliability of bitter peptide classification. Our method integrates diverse sequence-based feature representations and leverages a broad set of machine learning classifiers. The first stacking layer comprises multiple base classifiers, each trained on distinct feature encoding schemes, while the second layer employs logistic regression to refine predictions using an eight-dimensional probability vector. Extensive evaluations on a carefully curated dataset demonstrate that our model significantly outperforms existing predictive methods, providing a robust and reliable computational tool for bitter peptide identification. Our approach achieves an accuracy of 96.09\% and a Matthews Correlation Coefficient (MCC) of 0.9220 on the independent test set, underscoring its effectiveness and generalizability. To facilitate real-time usage and broader accessibility, we have also developed a user-friendly web server based on the proposed method, which is freely accessible at https://ibitter-stack-webserver.streamlit.app/. This tool enables researchers and practitioners to conveniently screen peptide sequences for bitterness in real-time applications.

Finding interesting phenomena is the core of scientific discovery, but it is a manual, ill-defined concept. We present an integrative pipeline for automating the discovery of interesting simple hypotheses (feature-target relations with effect direction and a potential underlying mechanism) in structured biomedical data. The pipeline combines machine learning, knowledge graphs, literature search and Large Language Models. We formalize "interestingness" as a combination of novelty, utility and plausibility. On 8 major diseases from the UK Biobank, our pipeline consistently recovers risk factors years before their appearance in the literature. 40--53% of our top candidates were validated as interesting, compared to 0--7% for a SHAP-based baseline. Overall, 28% of 109 candidates were interesting to medical experts. The pipeline addresses the challenge of operationalizing "interestingness" scalably and for any target. We release data and code: https://github.com/LinialLab/InterFeat

The integration of plant-based bioregenerative life support systems is a central objective in NASA's Moon to Mars strategy. Arabidopsis thaliana, a model organism with extensive genomic resources, serves as a key species to investigate plant resilience in extraterrestrial environments. We assessed the physiological and gene expression responses of A. thaliana (Col-0) grown in two off-world regolith simulants: LHS-2 (lunar highlands) and MGS-1 (Martian global). Plants exposed to these substrates exhibited significant reductions in root elongation, biomass, and chlorophyll content, along with elevated anthocyanin levels and transcriptional upregulation of stress-related genes including IRT1, PCS1, SOD1, and JAZ1. Evidence of jasmonic acid pathway activation and auxin signaling suppression suggests metal-induced hormonal misregulation. Our integrated analysis of morphological traits, pigment accumulation, and transcriptomic profiles reveals distinct mineral-specific stress responses, offering critical insights into substrate engineering strategies for future space agriculture.

Cell sensory transcription networks are the intracellular computation structure that regulates and drives cellular activity. Activity in these networks determines the the cell's ability to adapt to changes in its environment. Resilient cells successfully identify (classify) and appropriately respond to environmental shifts. We present a model for identification and response to environmental changes in resilient bacteria. This model combines two known motifs in transcription networks: dense overlapping regulons (DORs) and single input modules (SIMs). DORs have the ability to perform cellular decision making and have a network structure similar to that of a shallow neural network, with a number of input transcription factors (TFs) mapping to a distinct set of genes. SIMs contain a master TF that simultaneously activates a number of target genes. Within most observed cell sensory transcription networks, the master transcription factor of SIMs are output genes of a DOR creating a fan-in-fan-out (bowtie) structure in the transcriptional network. We model this hybrid network motif (which we call the DOR2SIM motif) with a superposition of modular nonlinear functions to describe protein signaling in the network and basic mass action kinetics to describe the other chemical reactions in this process. We analyze this model's biological feasibility and capacity to perform classification, the first step in adaptation. We provide sufficient conditions for models of the DOR2SIM motif to classify constant (environmental) inputs. These conditions suggest that generally low monomer degradation rates as well as low expression of source node genes at equilibrium in the DOR component enable classification.

Quantifying similarity between population spike patterns is essential for understanding how neural dynamics encode information. Traditional approaches, which combine kernel smoothing, PCA, and CCA, have limitations: smoothing kernel bandwidths are often empirically chosen, CCA maximizes alignment between patterns without considering the variance explained within patterns, and baseline correlations from stochastic spiking are rarely corrected. We introduce ReBaCCA-ss (Relevance-Balanced Continuum Correlation Analysis with smoothing and surrogating), a novel framework that addresses these challenges through three innovations: (1) balancing alignment and variance explanation via continuum canonical correlation; (2) correcting for noise using surrogate spike trains; and (3) selecting the optimal kernel bandwidth by maximizing the difference between true and surrogate correlations. ReBaCCA-ss is validated on both simulated data and hippocampal recordings from rats performing a Delayed Nonmatch-to-Sample task. It reliably identifies spatio-temporal similarities between spike patterns. Combined with Multidimensional Scaling, ReBaCCA-ss reveals structured neural representations across trials, events, sessions, and animals, offering a powerful tool for neural population analysis.

This paper presents a detailed mathematical investigation into the dynamics of COVID-19 infections through extended Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models. By incorporating demographic factors such as birth and death rates, we enhance the classical Kermack-McKendrick framework to realistically represent long-term disease progression. Using empirical data from four COVID-19 epidemic waves in Orange County, California, between January 2020 and March 2022, we estimate key parameters and perform stability and bifurcation analyses. Our results consistently indicate endemic states characterized by stable spiral equilibria due to reproduction numbers (R0) exceeding unity across all waves. Additionally, the inclusion of vaccination demonstrates the potential to reduce the effective reproduction number below one, shifting the system towards a stable disease-free equilibrium. Our analysis underscores the critical role of latency periods in shaping epidemic dynamics and highlights actionable insights for public health interventions aimed at COVID-19 control and eventual eradication.

The present paper aims to develop a mathematical model concerning the visual perception of spatial information. It is a challenging problem in theoretical neuroscience to investigate how the spatial information of the objects in the physical space is encoded and decoded in the neural processes in the brain. In the past, researchers conjectured the existence of an abstract visual space where spatial information processing takes place. Based on several experimental data it was conjectured that the said psychological manifold is non-Euclidean. However, the consideration of the neural origin of the non-Euclidean character of the visual space was not explicit in the models. In the present paper, we showed that the neural mechanism and specifically the Fisher information contained in the neural population code plays the role of energy-momentum tensor to create the space-dependent metric tensor resulting in a curved space described by a curvature tensor. The theoretical prediction of information geometry regarding the emergence of curved manifolds in the presence of the Fisher information is verified in the present work in the domain of neural processing of spatial information at mid-level vision. Several well-known phenomena of visual optics are analyzed using the notion of non-Euclidean visual space, the geodesics of the space, and the Fisher-Rao metric as the suitable psychometric distance.

Protein fitness landscapes frequently exhibit epistasis, where the effect of a mutation depends on the genetic context in which it occurs, \textit{i.e.}, the rest of the protein sequence. Epistasis increases landscape complexity, often resulting in multiple fitness peaks. In its simplest form, known as global epistasis, fitness is modeled as a non-linear function of an underlying additive trait. In contrast, more complex epistasis arises from a network of (pairwise or many-body) interactions between residues, which cannot be removed by a single non-linear transformation. Recent studies have explored how global and network epistasis contribute to the emergence of functional bottlenecks - fitness landscape topologies where two broad high-fitness basins, representing distinct phenotypes, are separated by a bottleneck that can only be crossed via one or a few mutational paths. Here, we introduce and analyze a simple model of global epistasis with an additive underlying trait. We demonstrate that functional bottlenecks arise with high probability if the model is properly calibrated. Our results underscore the necessity of sufficient heterogeneity in the mutational effects selected by evolution for the emergence of functional bottlenecks. Moreover, we show that the model agrees with experimental findings, at least in small enough combinatorial mutational spaces.

African swine fever (ASF) is a highly contagious viral disease that poses a significant threat to the swine industry, requiring stringent control measures, including movement restrictions that delay pig placements, impacting business continuity. The number and economic impact of unplaced healthy animals due to control zone restrictions remains unmeasured. This study evaluates the economic and epidemiological impacts of control zone placement restrictions during simulated ASF outbreaks in U.S. commercial swine farms. We model the spread of ASF and apply the U.S. National Response Plan (NRP) alongside alternative mitigation strategies, analyzing key metrics such as the number of unplaced pigs, depopulated pigs, infected farms, and total economic losses. Our findings estimate the median number of unplaced pigs in the first year was 153,020 (IQR 27,377 to 1,307,899) under the NRP scenario. Shorter control zone durations (20 to 25 days) effectively reduce the median number of unplaced pigs by 16.7% to 33.5%, whereas longer durations (40 days) increase unplacement numbers by 32%. The median number of depopulated pigs remains broadly consistent across all durations. Expanding the infected zone (5 to 15 km) increases the median number of unplaced pigs by 53.6% to 282% while reducing depopulated pigs by 28.8% to 73.9%, respectively. Economic losses are estimated through a model that includes depopulated and unplaced animals requiring culling. We examined the situations when 5%, 12%, or 20% of unplaced pigs required culling and found that the total cost ranged from zero (no second infection) to over $800 million.

The perceptron has served as a prototypical neuronal learning machine in the physics community interested in neural networks and artificial intelligence, which included Gérard Toulouse as one of its prominent figures. It has also been used as a model of Purkinje cells of the cerebellum, a brain structure involved in motor learning, in the early influential theories of David Marr and James Albus. We review these theories, more recent developments in the field, and highlight questions of current interest.

The code of nature, embedded in DNA and RNA genomes since the origin of life, holds immense potential to impact both humans and ecosystems through genome modeling. Genomic Foundation Models (GFMs) have emerged as a transformative approach to decoding the genome. As GFMs scale up and reshape the landscape of AI-driven genomics, the field faces an urgent need for rigorous and reproducible evaluation. We present OmniGenBench, a modular benchmarking platform designed to unify the data, model, benchmarking, and interpretability layers across GFMs. OmniGenBench enables standardized, one-command evaluation of any GFM across five benchmark suites, with seamless integration of over 31 open-source models. Through automated pipelines and community-extensible features, the platform addresses critical reproducibility challenges, including data transparency, model interoperability, benchmark fragmentation, and black-box interpretability. OmniGenBench aims to serve as foundational infrastructure for reproducible genomic AI research, accelerating trustworthy discovery and collaborative innovation in the era of genome-scale modeling.