Papers

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Quantitative Biology

q-bio Watch Quantitative Biology

Monday 21 September

Biomolecules · Cross-listed from cond-mat.soft

Nucleosome simulations suggest mechanisms of electrostatically-driven mesoscale chromatin evolution

Siddhartha G. Jena

Nucleosomes are structures made up of proteins called histones that bind and compact DNA, driving the mesoscale organization of the chromatin polymer. Although histone proteins have diversified over evolutionary time, their contributions to the corresponding diversification of chromatin structure are poorly understood. Here, we mine protein databases for histones and create in silico nucleosomes for 3241 organisms spanning $>$1.5B years of evolution. Using a combination of electrostatic calculations and coarse-grained molecular dynamics simulations, we reveal extensive biophysical diversification of the nucleosome unit. Finally, we perform coarse-grained oligonucleosomal simulations on a subset of evolutionarily and biophysically divergent nucleosomes, demonstrating dramatic differences in bulk phase behavior of chromatin. Taken together, our results suggest a paradigm in which histones may have evolved to facilitate particular types of mesoscale chromatin behavior.

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Biomolecules · Cross-listed from physics.bio-ph

Towards Accurate Prediction of Mutation-Induced Changes in Protein Structure

Zhuoyi Liu, Alex Calabrese and Corey S. O'Hern

Proteins can possess numerous mutations relative to their wild-type amino acid sequences with minimal impact to their structure and function. However, in other cases, even a single amino acid mutation relative to the wild-type sequence can lead to a large change in structure or even a disease phenotype. While the accuracy of wild-type protein structure prediction has improved significantly in recent years, it remains difficult to accurately predict the structure of mutant proteins. Here, we characterize the local mutation-induced structural changes in proteins for a dataset of wildtype and the corresponding single-amino acid mutant x-ray crystal structures from the Protein Data Bank (PDB). We find that mutation-induced structural changes in these proteins are localized at the site of the mutation, decaying rapidly with increasing spatial distance from the mutation site. In addition, we evaluate how well AlphaFold3 can recapitulate the observed mutation-induced structural deformations in the x-ray crystal structures. We find that the accuracy of the AlphaFold3 predictions decreases strongly with increasing mutation-induced deformation. In contrast to the results for AlphaFold3, the Pearson correlation between a single physical feature, i.e. the change in solvent accessibility, and the mutation-induced deformation does not depend on the magnitude of the deformation. Our results and analyses provide a framework for further studies aimed at predicting the structural changes in proteins caused by single amino acid mutations.

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Populations and Evolution

Phylogenetic Inference and the Stickiness of Fréchet Means, via Precise Asymptotics of an Embedded Random Walk

Adam Quinn Jaffe

A well-known phenomenon in statistical analyses of populations of phylogenetic trees in the Billera-Holmes-Vogtmann space is that the topology of the Fréchet mean tree can contain multifurcations (i.e., internal nodes with more than two children), which raises the practical question of whether this reflects a population-level branching structure (hard polytomy) or merely sampling variability in the data (soft polytomy). This is an instance of the more general phenomenon of "stickiness" in non-Euclidean statistics, whereby the sample Fréchet mean in certain non-positively curved stratified spaces becomes permanently trapped in a lower-dimensional stratum. In this work, we identify a particular multidimensional random walk embedded within the Fréchet mean process, and we show that the time at which stickiness occurs is determined by the largest last-passage time above zero of the coordinates of this random walk. Using this representation, we develop a fully nonparametric procedure for estimating the probability that trifurcations in a sample Fréchet mean tree will bifurcate at some future time if more observations are collected. Lastly, we apply our methodology to a problem in phylogenetics where we consider whether an observed trifurcation in the species tree of primates, glires, and tree shrews is genuinely trifurcated at the population level.

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Quantitative Methods · Cross-listed from quant-ph

Experimental evidence of generalization in quantum machine learning in small-data regime

Leena Anthony, Artemiy Burov, Nicolas Piro et al. (+2 more)

Quantum machine learning is a promising paradigm for learning from limited data, a central bottleneck in domains such as medical imaging, clinical trials, and rare diseases. Quantum convolutional neural networks (QCNNs) are particularly attractive in this setting, combining a hierarchical architecture with strong inductive bias and a parameter count that grows only logarithmically with system size. Their appeal rests on the generalization bounds of Caro et al. (2022), which show that the generalization error of a quantum model scales with the number of trainable parameters rather than with the Hilbert-space dimension, placing QCNNs in a potentially sample-efficient regime. We develop a hardware-compatible QCNN with mid-circuit measurement and classical feed-forward, and show on a binary handwritten-digit task that strong test performance is achievable from as few as 10 training samples, with the generalization error decreasing as the training set grows. At a matched 45-parameter budget the QCNN learns where an equally small classical convolutional network stays at chance, although an unconstrained classical baseline with roughly 25,000 parameters remains strongest when data are plentiful. Transpiling amplitude and angle encoded circuits across image resolutions from 2x2 to 512x512 pixels then exposes the dominant scaling bottleneck: amplitude encoding stays qubit-efficient but grows extremely deep, whereas angle encoding stays shallow but becomes qubit-prohibitive. On the medically motivated BreastMNIST benchmark the QCNN does not surpass the unconstrained classical network, yet it learns consistently above chance using orders of magnitude fewer parameters. Our results indicate that for QCNNs, learning from few samples is attainable in practice, whereas scaling to realistic image data is constrained less by optimization than by data encoding and hardware execution.

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All authors Leena Anthony, Artemiy Burov, Nicolas Piro, Matteo Dal Peraro, Clément Javerzac

Molecular Networks · Cross-listed from math.DS

A Proof of the Global Attractor Conjecture in a Special Case

Carsten Wiuf

We prove the Global Attractor Conjecture for complex balanced mass-action reaction networks whose reachable siphons satisfy two structural conditions, allowing multiple linkage classes. The proof proceeds in two steps. A structural condition relating the stoichiometric space to the reactions active within a boundary face ensures that a stoichiometric compatibility class contains at most one boundary equilibrium with any prescribed zero set. Finiteness of the possible zero sets and connectedness of the $\omega$-limit set then imply that any boundary limit set consists of a single equilibrium. To exclude convergence to such an equilibrium, we consider the embedded reaction network obtained by projecting onto the vanishing species and freezing the concentrations of the surviving species at a positive limit. The structural condition guarantees complex balance of this embedded network, while a further condition on its minimal active linkage classes ensures that an explicit Chetaev function is strictly increasing near the boundary. This excludes the boundary point as an accumulation point whenever the surviving concentrations converge. Consequently, every positive trajectory converges to the unique positive equilibrium in its stoichiometric compatibility class. Examples illustrate the hypotheses and their relation to strong endotacticity.

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Quantitative Methods · Cross-listed from cs.CL

QLoRA Fine-Tuning of Ministral LLM for Sequence-to-Function Protein Annotation

Demian Pavlyshenko and Bohdan Pavlyshenko

Functional annotation of newly sequenced proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far faster than the capacity for manual curation. Most computational approaches consider annotation as multi-label classification over a fixed ontology, which constrains predictions to a predefined label set. In this work we study the the protein annotation as a sequence-to-text generation problem. We fine-tune the 3B-parameter Ministral 3 base model with QLoRA (4-bit NF4 quantization with low-rank adapters) on sequence annotation pairs. We assess predictions with an LLM-as-expert protocol: a GPT model prompted as a senior molecular-biology curator scores organism identification as binary and function annotation quality. We conclude that QLoRA-fine-tuned compact LLMs can generate curator-style annotations with genuine biological value for a substantial subset of proteins. We also discuss future directions in data quality, model scaling, and evidence grounding that are needed to make the approach sufficiently reliable for practical use.

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Biomolecules

Adapting Boltz-2 with limited experimental activity data improves early enrichment in virtual screening

Kairi Furui and Masahito Ohue

Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget. When applying Boltz-2 to virtual screening, a key challenge is how to use limited experimental data from the target assay to improve the prioritization of active compounds. We investigated whether fine-tuning the Boltz-2 affinity heads with a small number of binary activity labels could improve early enrichment of active compounds in hit discovery. We compared fine-tuning with 40-300 labels in a retrospective evaluation on eight MF-PCBA targets. With 300 activity measurements, fine-tuning increased the number of actives in the top 1% by a geometric mean of 1.77-fold across the eight targets and improved average precision (AP) by 2.14-fold relative to the control without fine-tuning. We also investigated whether rescoring a subset of candidates could retain the improvement in hit recovery by reranking only the top-ranked Boltz-2 candidates with the fine-tuned head. Restricting rescoring to approximately 10% of the evaluation set retained hit recovery comparable to full rescoring. These findings show that affinity-head fine-tuning with limited activity labels improves early enrichment with Boltz-2 and that this benefit can be retained when rescoring a restricted set of candidates.

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Biomolecules

Decoding enzyme-substrate interaction topology reveals principles underlying catalytic efficiency and mutational outcomes

Weiren Zhao and Takeyuki Tamura

The enzyme turnover number (kcat) defines catalytic efficiency and constrains quantitative models of metabolism, yet the molecular determinants governing kcat and its response to mutation remain poorly understood. Measurements are sparse and labor-intensive, and most computational approaches provide numerical predictions without explaining how enzyme-substrate interactions shape catalytic outcomes. A central challenge is therefore to identify the topological principles that determine where mutations act and how their functional outcomes are encoded within the enzyme-substrate interaction network. Here, we show that catalytic efficiency and mutational effects can be interpreted through enzyme-substrate interaction topology. We developed Interkcat, an interpretable bidirectional cross-attention framework that captures reciprocal coordination between protein residues and substrate atoms. Optimized on a unified benchmark, Interkcat achieves state-of-the-art predictive performance (R2 = 0.701). From its learned representations, we derive an Interaction Topology Score (ITS) that identifies sequence regions statistically enriched for mutation-sensitive sites without explicit structural inputs. We further demonstrate that higher-order topological features distinguish opposing mutational outcomes: lethal mutations disrupt coordinated networks, whereas activity-preserving or enhancing mutations retain sparse, globally organized coupling. These findings establish interaction topology as a unifying principle linking enzyme sequence, catalytic efficiency, and evolutionary perturbation.

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Quantitative Methods · Cross-listed from cs.CV

Vision Transformers versus convolutional neural networks for fine-grained orchid genus identification in a species-rich, data-poor flora: a controlled benchmark on the Orchidaceae of New Guinea

Reza Saputra, Diah Harnoni Apriyanti, André Schuiteman et al. (+4 more)

New Guinea is the world's richest island flora (~2,856 orchid species), yet most species are represented by only a handful of photographs, far fewer than direct species-level classification requires. Methods for fine-grained identification in such species-rich, data-poor floras are needed, and it remains unclear which backbone architecture and pretraining strategy best support them. We built a two-stage system that first predicts the genus of a query photograph, then retrieves visually similar reference images of candidate species using FAISS. We compared four pretrained backbones – two Vision Transformers (ViTs; DINOv2, BioCLIP 2) and two CNNs (ConvNeXt V2-L, EfficientNetV2-L) – fine-tuned under an identical protocol on a fixed, species-stratified partition of 16,701 photographs spanning 120 genera and 1,350 species, assessing accuracy, calibration, error structure, species retrieval, and open-set detection of novel genera. DINOv2 attained the best genus performance (macro top-1 66.9%, 95% CI 63.7-70.6; global top-1 88.9%); both ViTs outranked both CNNs, and general-purpose self-supervised pretraining (DINOv2) outperformed domain-matched biological pretraining (BioCLIP 2) by 7.1 points of macro top-1. Errors concentrated on two abundant genera acting as error attractors. DINOv2 embeddings achieved species Recall@5 of 86.6% and genus Recall@5 of 98.7%; temperature scaling reduced every backbone's Expected Calibration Error to about 0.03; and a distance-based open-set gate flagged unseen genera (mean AUROC 0.958). A self-supervised Vision-Transformer backbone combined with embedding retrieval is an effective, deployable strategy for fine-grained identification in species-rich, data-poor floras. The system is released as an open web application (the New Guinea Orchid Identifier), offering a practical template for other hyperdiverse, under-documented taxa.

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All authors Reza Saputra, Diah Harnoni Apriyanti, André Schuiteman, Kurt Metzger, Ashley Field, Katharina Nargar, William Edwards

Quantitative Methods

Retracing the Process of Translation: Proteome-wide mapping of stable transcriptomic predictors of protein abundance in cancer cell lines

Johannes Schlüter and Alexander Schönhuth

Understanding the relationship between gene expression and protein abundance is central to molecular and systems biology. While gene expression reflects transcriptional activity, proteins are the functional molecules that determine cellular phenotypes. However, numerous post-transcriptional and translational regulatory layers complicate this relationship, and prior studies have reported only weak to moderate correlations between RNA and protein levels. Predicting protein abundance from transcriptomic data remains challenging, but it is a valuable goal for biological insight, especially when proteomic data is limited or unavailable. In this study, we applied a large-scale, Ridge regression-based feature selection strategy to identify predictive gene expression features for each of 8,423 proteins across 940 cancer cell lines. To our knowledge, this is the first work to perform such comprehensive protein-wise feature selection at this scale. Our analysis revealed both globally predictive and context-specific gene features. These included biologically meaningful modules such as immune-related genes, HOX transcription factor targets, and cytoskeletal components. The models identified stable candidate gene-protein associations that remained interpretable at the level of individual proteins and recurrent transcriptomic predictor patterns. Our approach enables interpretable modeling of protein expression from transcriptomic data and provides insight into transcriptomic features associated with protein abundance. This framework may support hypothesis generation, protein imputation in incomplete datasets, and deeper understanding of post-transcriptional regulation in cancer biology.

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Neurons and Cognition

Binding-Motivated Contextuality: A Cross-Domain Cyclic Test in Perception and Judgment

Adam Y. Shavit

Psychophysics and decision research study perceptual binding and judgment contextuality apart. We argue both are scored against the same cyclic noncontextuality inequalities and share one convex global-consistency geometry – not one cohomology class – though only contextuality is tested, since binding's own residual vanishes here. Building on sheaf formulations of predictive coding (Seely 2025) and contextuality (Abramsky & Brandenburger 2011), a cyclic set of pairwise judgments admits a noncontextual explanation exactly when the cyclic (Suppes-Zanotti or n-cycle) inequalities hold, and its severity is measured by the complete contextual fraction CF (Abramsky, Barbosa & Mansfield 2017) rather than by the Cech invariant, which can miss it (Caru 2017). We build the perceptual arena from two binary judgments per cyclic-dominance pairing, scored against the same inequalities as the survey; an appendix rejects the one-bit alternative on construct-validity grounds. The central test is cross-domain: one cohort performs both arenas, and a shared latent tolerance predicts a positive correlation between their signed cyclic margins V*, the pre-clamp quantities behind CF. Both are inconsistency scores, so general response consistency confounds a bare correlation, and the prediction is therefore confound-residualized against a variance- and reliability-matched control. A positive result would support a shared residual association, not a common causal mechanism, which we prove this design cannot identify at any sample size. All three tests are designed but unrun, and the perceptual arena is a proposed instantiation with pilot gates – a methodological proposal, not a confirmatory report.

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Sunday 20 September

Quantitative Methods

WILSON - a pathology foundation model framework for patient-level analysis and diagnostic text generation

Saghir Alfasly, Wataru Uegami, Sobhan Hemati et al. (+13 more)

Pathologists integrate morphology across magnifications and across the slides of a patient case, whereas pathology foundation models encode thousands of tiles from single slides and aggregate their features. Here we present WILSON, a vision–language foundation model that represents whole-slide images and multi-slide cases as single multi-magnification composite images, trained on approximately 189k slides from Mayo Clinic spanning 42 organs and 829 diagnostic entities using pathology reports as supervision. Without task-specific training, WILSON exceeded a dedicated case-level model on all internal cohorts (macro-F1 0.52 versus 0.38) and matched slide-level models up to 9.4 times larger at 272- to 2,155-fold lower compute. End-to-end fine-tuning on 508 triple-negative breast cancer cases improved histologic subtyping and stromal tumor-infiltrating lymphocyte grading by 0.16 and 0.11 macro-F1. WILSON retrieved matching diagnostic text at 75.6% recall@1 (PRISM, 58.1%) and generated captions closer to report-derived references than PRISM and PRISM2 on the internal cohort and on most external comparisons. Composite images thus offer a compact, clinically aligned computational unit for pathology.

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All authors Saghir Alfasly, Wataru Uegami, Sobhan Hemati, Wenchao Han, Xiaojia Tang, Kevin Thompson, Daniel Stone, Ghazal Alabtah, Saba Yasir, Michael R. Lucas, Eric W. Klee, Cheryl L. Willman, Judy C. Boughey, Matthew P. Goetz, Krishna R. Kalari, H.R. Tizhoosh

Neurons and Cognition · Cross-listed from cs.LG

Matched-Input Estimates Differ in Sign Across Architectures: Auditing EEG Foundation Models on Motor Imagery

Kevin Zhou and Sparsh Roy

Pretrained EEG foundation models are increasingly proposed as general-purpose encoders for brain-computer interfaces, yet recent benchmarks disagree about when their representations transfer to downstream tasks. We audit LaBraM and CBraMod on motor imagery under a validation-locked protocol in which preprocessing, architecture, optimization, freeze depth, checkpoint, temperature, and method selection are determined using training-session data only. On four-class BCI Competition IV-2a, every supervised comparator evaluated here outperforms every foundation-model configuration, including validation-selected fine-tuning. We then examine a key confound: foundation models and task-specific decoders are normally evaluated using different input pipelines. Retraining three supervised architectures on the broadband arrays consumed by the foundation models produces matched-input accuracy differences of opposite sign across architectures: broadband input improves ATCNet by 0.078 accuracy while reducing EEG Conformer accuracy by 0.088. None of the three individual matched-input terms is significant after multiple-comparison correction at n = 9, so we treat the sign variation descriptively rather than as a formal architecture-by-pipeline interaction. These observed sign differences suggest that a single comparator may not provide an architecture-invariant decomposition of a pretrained-versus-supervised performance gap. The four-class deficit also does not reproduce uniformly across motor-imagery datasets: on two-class BNCI2014-004 we cannot detect the same separation between fine-tuned CBraMod and the supervised comparators. Finally, validation-fitted temperature scaling returns foundation-model calibration error to the supervised range despite substantially lower four-class accuracy.

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Neurons and Cognition · Cross-listed from cs.LG

A discrete generative model of neuronal spiking activity on microelectrode arrays

Md Sayed Tanveer, Mohammed A. Mostajo-Radji and Ge Wang

Generative models of neural activity could help characterize tissue dynamics, compare experimental conditions, and simulate population activity for applications ranging from disease and drug-response studies to closed-loop experimentation. Existing approaches, however, typically assume a fixed set of sorted neurons, whereas high-density microelectrode arrays produce extremely sparse, array-wide binary spike volumes in which the observed subset of electrodes varies across assays. We introduce a discrete generative model that represents this activity using a shared vocabulary of spatiotemporal motifs. A residual vector-quantized autoencoder learns the motif vocabulary, while a factorized masked transformer predicts where activity occurs and which motif appears at each active location. We evaluate the model on 31 assays spanning human brain organoids and acute ex vivo human hippocampal tissue. The learned motifs are broadly reused: assay identity explains only $9%$ of the entropy in motif use, and motif overlap across tissue types is comparable to overlap within them. When representation quality is evaluated independently of the generative prior, our approach achieves $5.2\times$ the voxel-level reconstruction average precision of a matched flat tokenizer. For masked completion and free generation, the full model achieves $1.4$–$2.6\times$ the site-level average precision of the matched generative baseline and outperforms it across all four families of generation metrics. These results establish a compact, reusable representation for array-wide spiking activity without learned assay-specific parameters, providing a scalable foundation for generative modeling across diverse neural preparations.

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Neurons and Cognition

From Biological Precursors to Artificial Cognition: Consciousness, Embodiment, and the MEM Architecture

Janusz A. Starzyk and Wiesław L. Galus

This article asks under what conditions artificial intelligence could warrant a rational attribution of consciousness. Linguistic ability, multimodality, memory, planning, action control, and humanoid embodiment are not sufficient evidence of phenomenal experience. Biological precursors such as excitability, homeostasis, neural networks, and hierarchical representation instead identify functions whose counterparts may be engineered. The paper compares conventional LLMs, hybrid h-LLMs, vision-language-action systems, and embodied agents with the Motivated Emotional Mind (MEM) architecture. MEM adds receptor-grounded representations, regulatory self-monitoring and interoception, affect, semblion-based associative memory, and re-entry into lower sensory and interoceptive maps. A related formulation defines a phenomenal state as a dynamic, integrated state of the whole embodied system in which sensor-grounded modal content is recurrently stabilized and coupled to a suprathreshold interoceptive representation of the system's regulatory state. The mathematical model of MEM published on arXiv specifies implementable relations among these components and makes the proposal more precise and testable. Comparative and causal tests in humans, invertebrates, and artificial systems could strengthen or weaken the MEM capabilities. MEM is therefore presented as a falsifiable research program requiring empirical validation and ethical caution.

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Quantitative Methods · Cross-listed from cond-mat.other

THz Spectroscopy of Urine Vapors from Patients with Prostate Cancer and Benign Prostatic Hyperplasia: A Critical Review and Statistical Reanalysis

V. A. Atduev, A. V. Maslennikova, V. L. Vaks et al. (+7 more)

The cancer specificity of serum prostate-specific antigen (PSA) motivates complementary, non-invasive approaches to prostate disease assessment. Building on reported high-resolution terahertz (THz) analysis of urine-derived products, this review integrates spectroscopic evidence with a statistical reanalysis of clinical data from 24 patients with prostate cancer (PC) and 14 with histologically confirmed benign prostatic hyperplasia (BPH). Nonparametric statistical comparison shows higher PSA levels in PC, while overlapping patient distributions and receiver-operating-characteristic analysis with bootstrap uncertainty estimation demonstrate incomplete discrimination from BPH. Fast frequency-sweep spectroscopy characterizes urine-derived volatile and thermal-decomposition products, distinguishing compounds shared between groups from candidate PC-associated assignments, including glycolaldehyde, butyronitrile, pentanenitrile and methyl isocyanate. Glycolaldehyde has been detected by urinary carbonyl profiling, while benzaldehyde and phenol have been reported in urinary volatile studies. Related aldehydes identified by gas chromatography-mass spectrometry and glycine-associated metabolites reported in proton nuclear magnetic resonance studies provide complementary chemical and metabolic context. These correspondences support plausibility of THz findings without establishing PC specificity. Together, the dataset, statistical assessment and comparative literature synthesis support THz urine spectroscopy as a platform for molecular discovery and candidate prioritization. Standardized sample preparation, separation of native urinary constituents from processing-derived products, and validation in independent cohorts remain necessary.

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All authors V. A. Atduev, A. V. Maslennikova, V. L. Vaks, A. P. Bavrina, E. G. Domracheva, M. B. Chernyaeva, V. A. Anfertev, K. A. Atduev, Y. Tawalbeh, M. F. Pereira

Populations and Evolution

Demographic inference of pathogen-infected populations from partially observed transmission forests

Matthew Hall

Genomic epidemiology has several methods for identifying which sampled individuals are linked by close proximity in the transmission chain, whether by grouping them into clusters under a genetic distance threshold or by identifying probable direct transmission pairs. Individuals found to have no sampled neighbours—singletons—are usually set aside. We argue that they are informative. Whether any two sampled individuals prove to be linked depends on the size of the sampling frame and the number of independent lineage introductions into it, and thus the balance of linked and unlinked individuals is itself data about these quantities. We formalise this by treating the intersection of a transmission tree with a sampling frame as a labelled rooted forest, from which a fixed number of nodes are sampled uniformly at random. Using the all-minors matrix-tree theorem, we derive a closed-form expression for the number of labelled rooted $k$-forests on $N$ nodes in which a specified set of nodes is independent. From this we obtain, again in closed form, the probability that a sample of a given size contains no linked individuals at all, and the likelihood of an arbitrary observed configuration of clusters, both when the transmission structure within each is known, and when only the cluster sizes are. The approach is closer to a survey, or to mark-recapture, than to conventional phylodynamic model fitting: the information comes from the linkage structure of a single sample alone with no assumptions regarding pathogen dynamics. We outline three applications: power calculations for prospective studies, a test of the uniform sampling assumption that underlies the reading of clusters as transmission hotspots, and demographic inference itself. We finally set out the assumptions that a more flexible implementation would need to relax.

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Molecular Networks

Minimality in Reflexive and Stoichiometric Autocatalysis

Richard Golnik, Thomas Gatter, Wim Hordijk et al. (+2 more)

Autocatalysis, the ability of a chemical subsystem to sustain its own constituents when supplied with sufficient food molecules, has been closely related to the origin of life on Earth. Emerging from Wilhelm Ostwald's considerations about an explicit autocatalytic reaction, different notions of autocatalysis have been developed over the years. The two most prominent are reflexively autocatalytic F-generated sets (RAFs) and stoichiometric autocatalysis. After having shown that each RAFs is, under reasonable conditions, in general stoichiometrically autocatalytic, we examine here the relationship between the two notions of minimality: irreducible RAFs and autocatalytic cores. To this end, we overcome the obstacle that RAFs and stoichiometric autocatalysis have been formalized in distinct systems of chemical reactions, i.e., catalytic reaction systems (CRS) and chemical reaction networks (CRNs), respectively. We show that reactions in a CRS constitute equivalence classes of reactions of the corresponding CRNs w.r.t. their specific catalyzations. Using the fact that CRN and CRS can be canonically identified whenever each CRS reaction is associated with a single catalyzation, we demonstrate that the Kőnig graph of a monocatalyzed, irreducible RAF is composed of strong blocks devoid of food and waste species that are separated by reaction vertices, each of which contains an autocatalytic core. In fact, a single irreducible RAF can, in general, contain exponentially many autocatalytic cores.

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All authors Richard Golnik, Thomas Gatter, Wim Hordijk, Peter F. Stadler, Nicola Vassena

Neurons and Cognition

Chaotic Dynamics-Regulated Topological Learning for Patient-Specific Preictal State Identification

Zihan Wang, Daixin Li, Guilin Wang et al. (+4 more)

Epileptic seizures arise from complex, nonlinear interactions within brain networks, yet reliable electroencephalographic (EEG) prediction remains challenging due to the nonstationary and heterogeneous nature of neural dynamics. Existing methods typically analyze EEG data as static or weakly time-dependent snapshots, overlooking the intrinsic dynamics and lacking the geometric sensitivity to capture the hierarchical, localized evolution of the epileptogenic zone. To address these limitations, we propose an offline, patient-specific evaluation of chaotic dynamics-regulated topological learning (CDRTL) for distinguishing preictal from interictal EEG states. This framework unifies chaotic dynamics, multiscale algebraic topology, and local network differentiation. Specifically, we partition EEG signals into discrete functional subnets based on correlation strengths, capturing the multi-scale connectivity of the brain. By modeling each node as a Lorenz oscillator, we embed the underlying chaotic dynamics into the network architecture. We then apply the persistent Laplacian to simultaneously extract topological invariants and geometric shape evolution through harmonic and non-harmonic spectral analysis. Additionally, a node-removal topological differentiation strategy isolates localized neural contributions. Our framework was evaluated on the CHB-MIT database using balanced preictal and interictal labels and stratified channel-level cross-validation within each patient. The results support offline discrimination of preictal and interictal channel-level nodes within fixed patient-specific networks. Because representations are constructed from the complete network, including held-out unlabeled nodes, before cross-validation, the reported performance is specific to this transductive setting and does not establish generalization to unseen EEG windows, seizures, or patients.

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All authors Zihan Wang, Daixin Li, Guilin Wang, Mushal Zia, Xiaoqi Wei, Xiang Xiang Wang, Jian Jiang

Saturday 19 September

Quantitative Methods · Cross-listed from cs.CV

Reconstructed holograms and explanation-aware evaluation for low-cost computational pollen analysis in veterinary cytology

Swarn Warshaneyan, Joial Danyal, Blaž Cugmas et al. (+4 more)

Automated pollen analysis supports veterinary cytology, but brightfield microscopy is costlier and more complex than lens-less digital in-line holographic microscopy. We evaluate whether reconstructed holograms can narrow this gap and whether model explanations remain reliable under modality change. Six pollen species were imaged by brightfield and holographic microscopy. Raw, single back-propagation and iterative phase retrieval holograms were evaluated with YOLOv26s detection and MobileNetV4 classification after anchor-based annotation transfer. Six attribution methods were assessed for spatial grounding and faithfulness with the Attribution Health Inspection and Repair (AHIR) protocol, which tests model brittleness under weak noise and corrects attribution-map granularity when needed. Brightfield achieved 0.6890 mAP50-95 (0.8865 mAP50) for detection and 0.9687 macro-F1 (0.9705 accuracy) for classification. Reconstructed holograms narrowed the gap with a task-dependent split: p-type was strongest for detection at 0.5324 mAP50-95 (0.8229 mAP50), while r-type was strongest for classification at 0.7695 macro-F1 (0.7866 accuracy), both far above raw-hologram baselines. Activation-based explanations localized strongly on grains, and region-based methods retained ~60 to ~80% of faithfulness under holography. The holographic detector was highly brittle to weak perturbations, saturating deletion-based evaluation while insertion remained informative. Pixel-level gradient explanations approached random floor, yet spatial smoothing restored p-type gradient faithfulness from 0.05 to 0.51. For holographic classification, perturbation-based explanations remained faithful while gradient-based methods fell below random floor. Reconstruction improves low-cost holographic pollen analysis, while AHIR distinguishes genuine attribution failure from artifacts caused by model brittleness and map granularity.

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All authors Swarn Warshaneyan, Joial Danyal, Blaž Cugmas, Mindaugas Tamošiūnas, Edgars Kviesis-Kipge, Kirishanth Manivannan, Roberts Kadiķis

Quantitative Methods · Cross-listed from eess.SY

Experimental Design for Controller Selection in Synthetic Biology

Eric Palanques-Tost, Ron Weiss and Calin Belta

Synthetic biology enables the design of genetic circuits that act as feedback controllers. These controllers are typically designed using computational models, but mismatch between model and real dynamics can lead to controllers that fail in practice. While methods to address this issue exist, synthetic biology introduces additional structural constraints. Genetic circuits are often highly constrained by experimental limitations, reducing controller design to selection among a limited set of implementable circuits rather than an optimization over a continuous space. As a result, multiple system hypotheses may lead to the same optimal controller within the implementable set. Reducing model uncertainty may therefore be irrelevant when the models lead to the same optimal controller. In this paper, we exploit this structure to develop an algorithm for controller selection in synthetic biology, formulating the problem as a decision-oriented experimental design problem over a finite controller set. We represent plant uncertainty using a set of hypotheses and select experiments to minimize the posterior controller selection risk, rather than global model uncertainty. Across three mechanistic case studies, our method reaches the stopping criterion in fewer experimental rounds than model uncertainty and random experiment selection policies, while maintaining a comparable success rate.

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Populations and Evolution

From daylight to darkness: a nonlocal model of circadian activity cycles

Dan Bell, Timothé Boyer and Hao Wang

An animal's ability to perceive its surroundings, as well as the ecological interactions it experiences, can be strongly influenced by the sleep patterns of both itself and surrounding species. Animals with limited sensory capabilities may struggle to navigate their habitat at night, while in a predator-prey system, it may be detrimental for prey to be inactive while predators are awake and hunting. Here, we develop a general mathematical framework to study these interactions using partial differential equations, incorporating activity cycles and daylight-dependent perception into existing models of animal movement. We show that both the dominant sensory mode and the length of daylight are key factors determining the strength of aggregation in social species. Extending this framework to a two-species predator-prey system, we use game-theoretic approaches to show that the equilibrium sleep strategy of each species is shaped not only by its own sensory capabilities, but also by those of its ecological opponent. Depending on the sensory capabilities of the two species, equilibrium strategies may consist of a single sleep pattern or a combination of multiple sleep patterns.

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Friday 18 September

Quantitative Methods

Improving scoring functions for protein-protein docking with LambdaLoss

Richard Zhu, Darren Xu, Lee-Shin Chu et al. (+1 more)

Modeling protein-protein interactions requires accurate scoring functions that can rank potential poses (conformations) of a protein-protein complex to differentiate near-native poses from incorrect ones. Here, we propose a general framework for improving protein-protein pose ranking and other biomolecular interaction models using the LambdaLoss loss function from the Learning-to-Rank field. We test this framework by fine-tuning the energy prediction head of DFMDock with the LambdaLoss on an augmented dataset of 2.9M decoy poses derived from the DIPS dataset. On targets from the CAPRI score set benchmark, our fine-tuned ranking model LambdaDockScore is better at identifying correct poses in its top-1 and top-5 predictions compared to EuDockScore, a state-of-the-art method. LambdaDockScore also improves upon baseline DFMDock ranking performance for scoring antibody-antigen complexes and protein-protein complexes with very large or small binding interfaces.

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All authors Richard Zhu, Darren Xu, Lee-Shin Chu, Jeffrey J. Gray

Quantitative Methods · Cross-listed from cs.LG

EHR2Trace: Auditable EHR Data Infrastructure for Patient World Models and Clinical Agents

Xinye Yang, Yuli Wang, Cheng Ting Lin et al. (+1 more)

Patient world models and clinical agents aim to predict changes in patients' health and support clinical work. Developing these systems requires reliable histories of patient conditions, treatments, and the information available at each decision. Electronic health records (EHRs) contain these histories, but differences in how events are recorded make them difficult to use consistently. We present EHR2Trace, a system that converts EHRs from different sources into traceable patient events for model training and evaluation. It links events to source records, separates event time from information availability, and distinguishes medication orders, dispensing, and administration. A shared event representation supports both OMOP and MEDS exports, with automated validation and reproducible builds. Across three clinical datasets, EHR2Trace converted 846.4 million events, with every applicable check passing except one unit-consistency check on MIMIC-IV, and detected all 28 injected faults. A controlled prediction experiment showed that assigning later diagnoses to admission time substantially inflated measured performance, and that a model trained on such data lost accuracy when deployed on histories filtered by availability. EHR2Trace provides a reusable data foundation for patient world models and clinical agents, helping researchers inspect patient histories, check conversion decisions, and evaluate models with explicit data rules.

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All authors Xinye Yang, Yuli Wang, Cheng Ting Lin, Harrison Bai

Quantitative Methods

Reactive molecular dynamics simulations of atenolol first steps degradation by 9CL6 ammonia monooxygenase

Pascal Brault, Matthieu Wolf, Yana Gouzi et al. (+2 more)

The persistence of beta-blockers like atenolol in aquatic environments necessitates efficient remediation strategies, such as enzymatic biodegradation. This study employs reactive molecular dynamics (rMD) simulations to investigate the initial degradation steps of atenolol by the oxidoreductase 9CL6 ammonia monooxygenase. Using the ReaxFF reactive forcefield within a simulated aqueous environment, the degradation processes were modeled at 300 K and 350 K over 6.5 nanoseconds. Results indicate initial degradation rates of 24% at 300 K and 18% at 350 K, with each enzyme molecule degrading approximately 20 atenolol molecules before reaching a saturation point. Mass spectrum analysis reveals the primary formation of C8 and C6 fragments at both temperatures. However, at 300 K, additional intermediate fragments (C1, C3, C11, C13) are observed, which are completely bypassed at the higher temperature of 350 K. Furthermore, increasing the temperature to 350 K accelerates the initial degradation phase from 2 ns to 0.5 ns, despite yielding a slightly lower overall degradation rate. Ultimately, these atomistic insights provide a foundational computational methodology for predicting biodegradation rates and pathways of pharmaceutical contaminants at the molecular scale.

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All authors Pascal Brault, Matthieu Wolf, Yana Gouzi, Claire Albasi, Jeanne Trognon

Populations and Evolution · Cross-listed from math.ST

Asymptotics and finite sample bounds for prediction and smoothing in Wright-Fisher hidden Markov models

Luigi M. Malgieri, Filippo Ascolani, Matteo Giordano et al. (+1 more)

We study signal prediction and smoothing in hidden Markov models whose latent signal is a multi-type Wright-Fisher diffusion and whose discrete-time observations are categorical. We consider a repeated-sampling regime with finitely many inspection times and large within-time samples. Under parent-independent mutation, the Bayesian predictive and smoothing laws admit exact representations as mixtures of Dirichlet distributions. We show that the one-step predictive and local smoothing laws converge in total variation to the corresponding Wright-Fisher transition and bridge laws determined by the neighboring population frequencies, thereby justifying plug-in approximations based on empirical proportions. For prediction, we derive explicit finite-sample bounds for this approximation and for bounded predictive functionals such as diversity, together with a finite-sample concentration bound for the marginal smoother. The joint posterior at the inspection times concentrates at the target frequencies and satisfies a Bernstein-von Mises limit with distinct interior and boundary regimes, with Gaussian and Gamma limits on different scales. The results allow arbitrary dependence across inspection times. A key technical ingredient is a fixed-interval tail bound for the coalescent-based dual death process.

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All authors Luigi M. Malgieri, Filippo Ascolani, Matteo Giordano, Matteo Ruggiero

Neurons and Cognition · Cross-listed from cs.LG

BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings

Alexandre Andre, Shivashriganesh P. Mahato, Vinam Arora et al. (+39 more)

Advances in large-scale neural recording have made it possible to collect data across many animals and distributed brain regions, raising the question of whether this scale can be exploited to learn general-purpose neural representations transferable across diverse downstream tasks. Yet, progress toward this goal has been limited by fragmented evaluation protocols and a narrow focus on individual task domains. Here, we present BrainWideBench, a benchmark for evaluating across-animal transfer on multi-region neural recordings, built on the International Brain Laboratory Brainwide Map dataset of neural and behavioral recordings spanning 276 brain regions from 139 mice performing a sensory-guided decision-making task. The benchmark is organized around three complementary task suites that evaluate whether learned representations support downstream decoding of behavior, can predict masked or future neural activity, and can recover biologically meaningful anatomical organization. With this benchmark, we systematically evaluate pretraining methods across transfer settings, including finetuning on downstream objectives and zero-shot generalization to unseen animals. Our results confirm pretraining improves performance over matched single-session baselines, but we show current methods exhibit heterogeneity in transfer capabilities: gains depend strongly on the alignment between pretraining objectives and downstream tasks. No single approach performs uniformly well across all three suites, and most methods are designed to only address a subset of them. Together, these findings suggest that learning representations that jointly generalize across behavior, dynamics, and anatomy remains an open challenge. By providing a unified and reproducible evaluation suite, BrainWideBench establishes a framework for measuring progress toward general-purpose models of the mouse brain.

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All authors Alexandre Andre, Shivashriganesh P. Mahato, Vinam Arora, Keshav Balaji, Divyansha Lachi, Nanda H. Krishna, Jingyun Xiao, Yizi Zhang, Ximeng Mao, Wenrui Ma, Han Yu, International Brain Laboratory, Daniel Birman, Niccolò Bonacchi, Gaelle A. Chapuis, Joana A. Catarino, Felicia Davatolhagh, Mayo Faulkner, Laura Freitas-Silva, Fei Hu, Julia M. Huntenburg, Anup Khanal, Inês Laranjeira, Petrina Lau, Guido T. Meijer, Nathaniel J. Miska, Jean-Paul Noel, Alejandro Pan-Vazquez, Georg Raiser, Cyrille Rossant, Karolina Z. Socha, Anne E. Urai, Miles J. Wells, Steven J. West, Olivier Winter, Blake Richards, Guillaume Lajoie, Cole Hurwitz, Mehdi Azabou, Matthew R. Whiteway, Liam Paninski, Eva L. Dyer

Tissues and Organs · Cross-listed from cs.AI

Learning Cardiac Features: ECG Biometrics Across Time and~Exercise

Luca Thiebaud, Paul Chauchat, Mustapha Ouladsine et al. (+1 more)

Electrocardiograms (ECGs) carry subject-specific patterns enabling reliable individual discrimination, forming the basis of ECG biometrics. Beyond authentication, this paradigm holds significant potential to secure sensitive cardiac data and to serve as a pretext task in self-supervised learning. Yet, most studies remain confined to singlesession, resting data, leaving robustness to temporal and physiological variations largely untested. We address this gap by evaluating ECG biometrics under realistic conditions involving exercise-induced stress and cross-session variability. A Siamese ResNet with late multi-lead fusion strategy is trained on a large ECG dataset extracted from cardiopulmonary exercise tests and evaluated with a exercise-and time-aware protocol, as well as on public benchmarks. This first extensive assessment of ECG biometrics under combined physiological and temporal variability achieves an intra-session rest-to-peak EER of 1.7% and stateof-the-art 3.9% on the CYBHi dataset. Findings support the presence of an intrinsic cardiac signature resilient to physiological and temporal drift.

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All authors Luca Thiebaud, Paul Chauchat, Mustapha Ouladsine, Stéphane Delliaux

Populations and Evolution · Cross-listed from math.DS

Compartmental Epidemiological Models: Discussing Fundamental Elements via the SIR Model

Ligia Liani Barz, Jose Rafael Santos Furlanetto and Fernando Deeke Sasse

We present a didactic treatment of compartmental disease spread modeling centered on the SIR framework, aimed at mathematics education. Our focus is on how modeling choices encode transmission mechanisms and data-collection practices. We begin by stating the structural hypotheses of compartmental models, including population partitioning, homogeneous mixing, flow closure, and Markovian transitions. We then derive the basic SIR dynamic equations in both continuous-time (differential equations) and discrete-time (difference equations) settings. A key goal is to clarify the distinction between density-dependent and frequency-dependent transmission. We do this by constructing the force of infection from the contact rate, prevalence, and per-contact transmissibility while tracking units and interpretations. We go beyond the usual mass-balance SIR model by introducing the discrete Kermack-McKendrick perspective through the cumulative force of infection. This naturally leads to discrete SIR updates with an exponential escape probability term. We also derive Bernoulli-based exponential formulations for both density- and frequency-dependent transmission, interpret these probabilistically over observation intervals, and obtain the associated discrete infectivity kernel. This highlights how geometric infectious survival emerges from per-step removal. The paper includes reproducible, introductory-level Python code to implement the basic discrete SIR model, the continuous SIR model, and the exponential discrete variants. We provide instructors with an example of parameter estimation from prevalence data using least squares, along with guidance on interpretation and limitations. This framework is designed to help teachers integrate model assumptions, units, simulation, and inference in a coherent introduction to infectious disease modeling.

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Populations and Evolution

Exact Counts of Binary Phylogenetic Networks with Four Reticulations

Hao Yu and Louxin Zhang

Phylogenetic networks provide a flexible framework for representing reticulate evolutionary processes, such as hybridization, introgression, recombination, and horizontal gene transfer. However, their combinatorial complexity makes even basic enumeration problems difficult. Building on our previous work for networks with up to three reticulations, we derive an explicit closed-form formula for the number of unrestricted rooted binary phylogenetic networks with four reticulations on $n$ labeled taxa. Our approach is based on tree-component graphs. We classify the 79 possible component graphs corresponding to networks with four reticulations into ten groups. We then enumerate the networks associated with each group by combining known counts of one-component networks, forests, and networks with fewer reticulations. Summing these contributions yields the desired formula. This result extends the exact enumeration of unrestricted binary phylogenetic networks to four reticulations and further demonstrates the effectiveness of component graphs for systematically organizing and counting increasingly complex network classes.

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