Clay weekly context brief for the Quantitative Biology category (ISO week 2026-W45).

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. Kinetic Proofreading Activation Times Beyond the Weibull Limit
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2610.31788
   T cells can detect a few foreign peptide-major histocompatibility complexes (pMHCs) among a far larger number of self pMHCs.
2. How complex behavioural contagion can prevent infectious diseases from becoming endemic
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2604.10995
   Infectious disease transmission in human populations has a complex two-way interaction with changes in host behaviour.
3. FISH Omics Format: a standard enabling FAIR chromatin tracing data sharing across repositories
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2508.13255
   In recent years, multiplexed Fluorescence In Situ Hybridization (FISH) or FISH-omics methods have rapidly expanded, enabling the quantification of chromatin organization in single cells, often in conjunction with measurements of RNAs and proteins.
4. pyAvalanches: A Python Package for Analyzing Spatiotemporal Propagation in Neuronal Avalanches
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2609.11530
   The analysis of neuronal avalanches offers insights into brain dynamics utilizing the framework of criticality, but the reproducibility and comparability of studies are limited by the use of fragmented, lab-specific scripts.
5. pyTransRate: reference-free quality assessment of de novo transcriptome assemblies with current aligners and quantifiers
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2610.37031
   Summary: The original software TransRate scores a de novo transcriptome assembly by mapping the reads it was built from back to it, with no reference required.
6. A Co-Design Approach to Physically-Informed Molecular Docking for Quantum Annealers
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2604.09540
   Molecular docking is a crucial step in computer- aided drug design, involving the identification of ligand poses that are physically plausible within a protein pocket.
7. Bistability, Absolute Concentration Robustness, and Hysteresis in Dual-Site Futile Cycles with Bifunctional Enzymes
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2605.11389
   Bifunctional enzymes, which catalyze both the forward and reverse steps of a substrate modification reaction, arise naturally in bacterial two-component signaling systems and metabolic regulation.
8. Randomness-aware multiscale models of glioma invasion and treatment
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2602.08008
   In this work, we develop a stochastic multiscale model for glioma growth and invasion in the brain, incorporating the effects of therapeutic interventions.
9. Forecasting Without Full Identification: Reliability and Computational Cost in Clot-Growth Simulation
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2608.08165
   We forecast clot growth from sparse early measurements with incomplete biochemical information.
10. gpuGEM: GPU-based linear optimisation of genome-scale metabolic models
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2610.24106
   Summary: Genome-scale metabolic models increasingly challenge CPU linear-programming solvers in repeated optimisation.
11. Counting approximate breakpoint medians of random genomes
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2610.40027
   Unsigned linear unichromosomal genomes with genes labeled 1, .
12. A unified and composable framework for modeling packed-bed liquid chromatography
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2610.33269
   Packed-bed chromatography is a cornerstone of bioseparation, yet its modeling landscape remains fragmented by numerous model variants, inconsistent terminology, and limited structural clarity.
13. Prospective Coding and Path Integration Emerge as Equilibrium Solutions of Self-Organizing Neural Networks with Firing-Rate Adaptation
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2606.14649
   Continuous Attractor Neural Networks (CANNs) traditionally rely on pre-wired recurrent connectivity to model spatial representations, path integration, and anticipatory dynamics.
14. Reading Between the Reads: A Decade On, Revisiting Sequencing Protocol Annotation in the Sequence Read Archive
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2610.27331
   Background: Experimental metadata are essential for interpreting archived sequencing data.
15. Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2604.01169
   A fundamental challenge in science and engineering is the simulation-to-experiment gap.
16. Orchestra: Corroboration-Based Regulatory Candidate Discovery via Composed Bioinformatics MCP Agents
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2609.05496
   Orchestra composes two independently built bioinformatics MCP servers -- RegNetAgents, which infers regulatory network topology, and CASCADE, which provides independent functional evidence (LINCS knockdown, DepMap essentiality, super-enhancer status, DoRothEA transcription-factor confidence) -- into one multi-agent LangGraph workflow.
17. 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.
18. Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/1307.2965
   The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities.
19. Beyond $\chi^2$: Ensemble null tests for reliable scientific discovery
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2610.40167
   Unrecognized systematic errors can survive standard checks and undermine scientific discoveries.
20. A method for including additional socio-demographic factors in social contact matrices for compartment-based epidemic models
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2605.13870
   Socio-demographic factors influence social contact patterns and play a fundamental role in shaping the transmission dynamics of infectious diseases.
21. A Low-Rank Space Captures Linear Alignment Between LLM and Human Neural Activity During Naturalistic Language Listening
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.40511
   Large language models (LLMs) are increasingly used as computational models of human language processing, yet it remains unclear which components of their representations drive alignment with neural activity.
22. GenoBERT: A Language Model for Accurate Genotype Imputation
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2604.00058
   Genotype imputation enables dense variant coverage for genome-wide association and risk-prediction studies, yet conventional reference-panel methods remain limited by ancestry bias and reduced rare-variant accuracy.
23. Latent Style-based Quantum Wasserstein GAN for Drug Design
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2603.22399
   The development of new drugs is a time-consuming and expensive process, with average costs estimated to be up to around $2.5 billion.
24. Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks - the GATTACA Framework
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2505.02712
   Cellular reprogramming, the artificial transformation of one cell type into another, has been attracting increasing research attention due to its therapeutic potential for complex diseases.
25. 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.
26. An Octahedral Fibrous Constitutive Model for Heart Valve Mechanics and Function
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2606.04234
   Fibrous soft tissues derive their nonlinear mechanical response from networks of extracellular matrix fibers.
27. Confidence Filtering in Video-Based Gait Assessment: Measurement Error versus Selection Shift
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2610.39704
   Video-based gait assessment often keeps only high-confidence pose observations, which can lower measurement error but change the movement assessed.
28. Short-wave signal versus indirect prey-taxis
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2604.20469
   We address a short-wave asymptotic for one class of quasi-linear second-order PDE systems involving the cross-diffusion described by the so-called Patlak-Keller-Segel law.
29. Latent Prediction Yields Cross-Subject Alignable Representations in Sensory-Evoked fMRI
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.40280
   Decoding sensory-evoked functional magnetic resonance imaging (fMRI) data is a central analytical tool in cognitive neuroscience.
30. Adaptive Allele-Haplotype Incidence Indexing for Pangenome Queries: Representation, Reconstruction, and Benchmarking
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2512.21320
   Population-scale analysis of phased genetic variants requires both allele-carrier retrieval and ordered haplotype context.
31. Commutative Algebra Modeling of Mutation and Disease Association
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2509.26566
   Genetic mutations can lead to disease by disrupting molecular interactions, altering protein stability, inducing misfolding, and promoting protein aggregation.
32. On the Regularity of Reaction Systems
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2409.05340
   Reaction networks have been widely used as generic models in diverse areas of applied sciences, such as biology, chemistry, ecology, epidemiology, and computer science.
33. 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.

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); q-bio.TO (Tissues and Organs).
Selected 33 of 468 available items for this weekly brief.