Paper List
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PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell represen...
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Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
This paper addresses the core challenge of calibrating complex biological models (e.g., PDEs, agent-based models) with incomplete, noisy, or heterogen...
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Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
This paper addresses the core challenge of detecting viral variants in wastewater sequencing data without reference genomes or labeled annotations, ov...
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks
Department of Mathematical Analysis and Numerical Mathematics, Comenius University, Slovakia | University of Edinburgh, UK
The 30-Second View
IN SHORT: This paper addresses the core challenge of distinguishing genuine stochastic dynamics of gene regulatory networks from artifacts introduced by imperfect molecular detection in single-cell experiments.
Innovation (TL;DR)
- Methodology Extends the binomial capture model from simple gene expression to general gene regulatory networks (GRNs) with explicit regulation, enabling analysis of technical noise in complex systems.
- Theory Establishes precise mathematical conditions under which technical noise leads to a renormalization (rescaling) of kinetic rates versus when it introduces non-absorbable distortions.
- Methodology Derives results valid for networks of arbitrary connectivity and under time-dependent kinetic rates, significantly generalizing previous steady-state analyses.
Key conclusions
- Technical noise systematically reduces the apparent mean burst size of gene products by a factor of p (the capture probability), e.g., from b(t) to b(t)*p.
- Rate renormalization occurs when promoter-state transitions are on a distinct timescale (much slower/faster) than other reactions or under high transcription factor abundance.
- The framework shows that for the telegraph model, the observed mRNA dynamics are equivalent to the true system with a renormalized transcription rate: k₃(t) → p*k₃(t).
Abstract: Imperfect molecular detection in single-cell experiments introduces technical noise that obscures the true stochastic dynamics of gene regulatory networks. While binomial models of molecular capture provide a principled description of imperfect detection, they have so far been analyzed only for simple gene-expression models that do not explicitly account for regulation. Here, we extend binomial models of capture to general gene regulatory networks to understand how imperfect capture reshapes the observed time-dependent statistics of molecular counts. Our results reveal when capture effects correspond to a renormalization of a subset of the kinetic rates and when they cannot be absorbed into effective rates, providing a systematic basis for interpreting noisy single-cell measurements. In particular, we show that rate renormalization emerges either under significant transcription factor abundance or when promoter-state transitions occur on a distinct (much slower or faster) timescale than other reactions. In these cases, technical noise causes the apparent mean burst size of synthesized gene products to appear reduced while transcription factor binding reactions appear faster. These effects hold for gene regulatory networks of arbitrary connectivity and remain valid under time-dependent kinetic rates.