Clay weekly context brief for the Quantitative Biology category (ISO week 2026-W42).

Clay tracks publications from the Quantitative Biology feed list. Below are recent items from this category, each with its source and a short description of what the publication covers when one is available in the source feed.

Recent publications:
1. La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2610.12236
   RNA function arises from the coupling of nucleotide sequence and three-dimensional structure, motivating their joint design.
2. Predictive Multiplicity in Cell-Fate Assignment: Label-Free Rashomon Sets and the Limits of Per-Cell Certification
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2610.11185
   Single-cell trajectory inference maps transcriptomic measurements onto developmental continua, yet configurations that fit the data equally well can assign conflicting cell fates.
3. Mean Field Theory Based on the Spike Time Response Curve for Synchronization Within and Between Two Alternating Populations of Neural Oscillators with Delays
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.10977
   In order to study oscillators coupled by pulses emitted at discrete times, it is generally assumed that the effect of each pulse is an instantaneous phase shift, with a possible conduction delay between emission and receipt.
4. A minimal model for rate-induced tipping to extinction
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2610.12319
   Rate-induced tipping ("R-tipping") in ecological modelling is characterised by too-rapid change of an environmental parameter causing collapse of a population without any bifurcation being crossed ("B-tipping").
5. Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2610.11694
   Existing reaction models primarily learn molecular transformations, whereas enzy- matic reactions depend jointly on molecular structure and catalytic function.
6. Generalizable single-cell perturbation response prediction using energy-guided flow matching
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2610.02232
   Predicting phenotypic and transcriptional responses to perturbations at single-cell resolution provides a powerful tool for probing biological systems.
7. Autocatalysis and Boundary Stability in Chemical Reaction Systems
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2610.01636
   The next-generation matrix method is a powerful tool for computing the basic reproduction number in compartmental mathematical models of infectious diseases.
8. Uncertainty-aware neural emulation reveals climate-resilient maize traits at scale
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2605.22848
   Global food security depends on predicting crop responses to climate variability, yet process based crop models remain too computationally expensive for large scale exploration of genotype and environment interactions.
9. Non-Markovain Quantum State Diffusion for the Tunneling in SARS-COVID-19 virus
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2502.17449
   In the context of biology, unlike the comprehensively established Standard Model in physics, many biological processes lack a complete theoretical framework and are often described phenomenologically.
10. CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2604.24201
   Motivation: Multi-omics integration can improve cancer subtyping, but modality informativeness and noise vary across cancer types and patients.
11. Neural Decoding as Cognitive Inference
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.11923
   The brain maintains stable cognition despite continuously changing neural activity.
12. The Site Frequency Spectrum in an Exponentially Growing Population with Selection
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2607.16479
   We consider a supercritical two-type continuous-time linear birth-death process with mutation and selection, in which wild-type individuals give rise to mutant offspring with a larger net growth rate $\lambda_1$.
13. PertMind: Eliciting Emergent Biological Reasoning in LLM via Reinforcement Learning on Cellular Perturbation Data
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2608.16419
   Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces.
14. Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2610.03160
   Discovering broadly neutralizing antibodies (bnAbs) from human natural immune repertoires remains a fundamental challenge in immunology, hindered by: the extreme rarity of bnAb, incomplete understanding of their cellular origins across pathogens, and the inability of existing computational tools to generalize across emerging viral threats.
15. Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2610.02175
   Protein function annotation needs to know which predictions to distrust, not only what a model predicts.
16. EPI-KAN: A Method For Estimating and Forecasting Time-Dependent COVID-19 Parameters
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2607.15302
   We introduce EPI-KAN, a novel method for estimating COVID-19 time-varying parameters.
17. UNAAGI: Atom-Level Diffusion for Generating Non-Canonical Amino Acid Substitutions
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2512.10515
   A central challenge in structural biology is identifying beneficial amino acid substitutions, a problem that underpins both mutational effect prediction and protein engineering.
18. Truecell reproduces R Seurat's single-cell analysis outputs natively in the Python ecosystem
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2610.10482
   Single-cell RNA-sequencing analyses predominantly use either Seurat (R) or Scanpy (Python), with the choice often driven by programming language preference.
19. Learning infinite context windows in recurrent architectures via spatial neural computing
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.10690
   Recurrent neural networks (RNNs) offer linear-time scaling with sequence length while requiring only constant memory, yet they struggle to capture long-range dependencies due to vanishing gradients and limited receptive fields.
20. Mathematical statistics of wild mammal biomass
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2610.09744
   Using the recently published global census of the biomass of wild terrestrial mammals, we perform a detailed mathematical statistical analysis of its distribution over $N_s=4795$ species, drawing on tools developed in economics to characterize wealth inequality.
21. Beyond Scaffold Splits: Structural-Frontier Evaluation Reveals Hidden Failures in ADMET Models
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2607.10729
   Molecular property models are commonly evaluated by holding out Bemis-Murcko scaffolds, yet a scaffold identifier is only one notion of chemical unfamiliarity.
22. Reduced Hodgkin-Huxley models based on the correlation between sodium and potassium gating variables
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2402.19185
   We analyse the interaction between the sodium (Na$^+$) and potassium (K$^+$) gating variables in the 4D Hodgkin-Huxley axonal electrophysiological model.
23. Triangularity of the Jacobian on siphon faces, the Metzler property of its transversal component and other results
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2603.06778
   We establish two structural properties of the Jacobian of a positive ODE on any invariant boundary face, i.e.
24. Aletheia: An Offline-First Clinical Decision Support System for Differential Diagnosis in Low-Resource Healthcare Settings
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2607.24814
   Access to specialist clinical expertise remains severely limited across sub-Saharan Africa, where physician-to-patient ratios can fall below 1:25,000 in rural settings.
25. Leveraging secondary-structure information for accurate nucleic acid structure prediction with OFoldNA
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2609.37414
   Recent advances in biomolecular structure prediction have enabled accurate modelling of increasingly complex molecular systems.
26. Taxonomic Classification with Complete Tag Arrays
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2610.10500
   Taxonomic classifiers such as Kraken assign each $k$-mer of a reference database to the lowest common ancestor (LCA) of the genomes containing it, but this works less well as databases grow, because more and more $k$-mers are shared across species.
27. Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.10850
   As AI models move toward clinical decision-making and personalized treatment, understanding \emph{what} a model learns is important beyond predictive accuracy alone.
28. Stochasticity and Environmental Switching Shape Quorum Sensing Evolution in Bacterial Populations
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2610.10492
   Quorum sensing (QS) is classically understood as a mechanism by which bacterial cells sense their own density, but environmental fluctuations make any single cell's estimate unreliable.
29. Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2604.26070
   Causal inference in continuous-time sequential decision problems is challenged by hidden confounding and partially observed states.
30. Speed and stability of segregated waves in a pressure-based model of heterogeneous cell populations
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2609.09043
   We consider a minimal pressure-based model of heterogeneous cell populations consisting of proliferative and non-proliferative cells with different mobilities.
31. A flux-based approach for analyzing the disguised toric locus of reaction networks
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2510.03621
   Dynamical systems with polynomial right-hand sides are very important in various applications, e.g., in biochemistry and population dynamics.
32. De novo design of monoclonal and bispecific antibodies with OFAntibody
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2610.09548
   Recent advances in generative protein design have enabled de novo antibody generation with explicit target and epitope conditioning.
33. TorchGWAS 1.0: GPU-accelerated GWAS at scale
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2604.21095
   Imaging, molecular, and machine-learning workflows can generate thousands of quantitative phenotypes in a single cohort, creating substantial computational and output bottlenecks when testing traits individually.

Sources in this brief: q-bio.BM (Biomolecules); q-bio.CB (Cell Behavior); q-bio.GN (Genomics); q-bio.MN (Molecular Networks); q-bio.NC (Neurons and Cognition); q-bio.OT (Other Quantitative Biology); q-bio.PE (Populations and Evolution); q-bio.QM (Quantitative Methods).
Selected 33 of 474 available items for this weekly brief.