Paper List
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
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Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Collective adsorption of pheromones at the water-air interface
This paper addresses the core challenge of understanding how amphiphilic pheromones, previously assumed to be transported in the gas phase, can be sta...
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pHapCompass: Probabilistic Assembly and Uncertainty Quantification of Polyploid Haplotype Phase
This paper addresses the core challenge of accurately assembling polyploid haplotypes from sequencing data, where read assignment ambiguity and an exp...
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Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
This paper addresses the core challenge of automating the discovery of biologically plausible recurrent neural network (RNN) dynamics that can replica...
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Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how obj...
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.