Paper List
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
This paper addresses the critical bottleneck in therapeutic peptide design: how to efficiently optimize lead peptides with geometric constraints while...
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.