Bio papers — 2026-10-27

The Morning Preprint — this day as one edition: the day's papers, the five things worth knowing, and the episodes behind them.

Today's work focuses primarily on understanding relaxation dynamics within chemical reaction networks. This is vital because these systems often exhibit slow, plateau-like behaviors that are functionally important in biology. We obtained bounds on the Kullback--Leibler divergence to equilibrium for mass-action chemical reaction networks with equilibrium by characterizing trajectory-dependent bounds using singular values of the stoichiometric matrix and time-integrated activities via deformed exponential functions AKMPC.

This framework is particularly relevant because it provides an a posteriori characterization of slow relaxation induced by local convexity in catalytic reaction networks exhibiting plateaus. It offers a bound-based approach to quantifying these slow dynamics.

Beyond chemical kinetics, there is research into how the brain processes sensory information, specifically focusing on detecting absence rather than presence. We tested the hypothesis that the detection of absence relies on a dedicated prediction error mechanism for updating beliefs about stimulus occurrence in the auditory pathway AKMPC.

Our fMRI studies showed that while feature prediction error is encoded in the auditory midbrain, absence prediction error was not. This suggests that absence prediction error is processed by a different circuit and that these mechanisms might be disrupted in conditions like psychosis.

Finally, we are looking at how stochastic processes govern gene expression fluctuations and rare events. We applied the partitioning of Poisson arrivals framework to model post-transcriptional regulation. This allowed us to derive a general framework for obtaining large deviation rate functions for protein production rates AKMPC.

This work opens new avenues for analyzing rare events in general models of post-transcriptional regulation across various biological settings.

Today's papers

Convex Analysis of Relaxation Dynamics in Chemical Reaction Networks and Generalized Gradient Flows We obtain bounds on the Kullback--Leibler divergence to equilibrium for mass-action chemical reaction networks with equilibrium. [paper] [episode]
Detecting absence: A dedicated prediction-error signal emerging in the auditory thalamus How does the brain know what is out there and what is not? [paper] [episode]
Hodge-Decomposition of Brain Networks We analyze brain networks by decomposing them into three orthogonal components: gradient, curl, and harmonic flows. [paper] [episode]
Post-transcriptional Regulation of Stochastic Gene Expression Conditioned on Large Deviations We create a general framework to obtain objects of interest in large deviations for models of post-transcriptional regulation. [paper] [episode]

The papers

Convex Analysis of Relaxation Dynamics in Chemical Reaction Networks and Generalized Gradient Flows — The gist: This work establishes an analytical description of relaxation in equilibrium chemical reaction networks by systematically exploiting their underlying generalized gradient flow structure, providing rigorous bounds on divergences and characterizing slow relaxation behavio [episode]
Detecting absence: A dedicated prediction-error signal emerging in the auditory thalamus — The gist: The detection of sensory absence relies on a distinct form of prediction error, absence prediction error, dedicated to the update of beliefs on stimulus occurrence Hypothesis and Theoretical Framework The study proposes that the detection of absence relies on a distinct [episode]
Hodge-Decomposition of Brain Networks — The Hodge decomposition provides a novel framework for analyzing dynamically changing brain networks by partitioning edge-based signals into biologically interpretable components associated with node potentials, local triangular motifs, and global cycles. [episode]
Post-transcriptional Regulation of Stochastic Gene Expression Conditioned on Large Deviations — The gist The paper applies a general framework to obtain objects of interest in large deviations for models of post-transcriptional regulation directly from prior results for promoter-based models. [episode]

Important terms

Relaxation Dynamics: This refers to how chemical reaction networks settle down after a change, often showing slow, plateau-like behaviors that are important in biological systems.
Kullback--Leibler Divergence: This is a mathematical measure used to quantify the difference or distance between two probability distributions, here applied to chemical reaction network equilibrium.
Stoichiometric Matrix: This matrix describes the structure of a chemical reaction network, and its singular values are used to characterize how fast these systems relax.
Absence Prediction Error: This is a specific mechanism in the brain that deals with detecting something missing rather than something present, crucial for understanding sensory processing.