arXiv subjects

Xuewen Shen

Publications and source records attributed to Xuewen Shen.

3 recordsLinked to original sources

arXiv2026

ATP Level and Phosphorylation Free Energy Regulate Trigger-Wave Speed and Critical Nucleus Size in Cellular Biochemical Systems

Trigger waves are self-regenerating propagating fronts that emerge from the coupling of nonlinear reaction kinetics and diffusion. In cells, trigger waves coordinate large-scale processes such as mitotic entry and stress responses. Although the roles of circuit topology and feedback architecture in generating bistability are well established, how nonequilibrium energetic driving shapes wave propagation is less well understood. Here, we employ a thermodynamically consistent reaction--diffusion framework to investigate trigger-wave dynamics in ATP-dependent phosphorylation--dephosphorylation systems. We first recapitulate general expressions for trigger-wave speed in the bistable regime and analyze curvature-induced corrections that determine the minimum critical nucleus required for sustained propagation in higher dimensions. We then apply this framework to two representative systems, treating ATP concentration and the nonequilibrium parameter $\gamma = [ATP]/(K_{\mathrm{eq}}[ADP][P_i])$ as independent control variables to examine how energetic driving regulates wave propagation. Our results show that ATP and $\gamma$ not only modulate wave speed, but can also reverse the direction of propagation and reshape the parameter regime supporting trigger waves. The critical excitation radius also depends on both ATP concentration and phosphorylation free energy. These findings identify the intracellular energetic state as a regulator of trigger-wave behavior, linking metabolic conditions to the spatial dynamics of wave propagation. More broadly, this framework connects classical reaction--diffusion theory with ATP-driven biochemical regulation and provides a general perspective on related energy-dependent cellular decision-making processes.

q-bio.MN↗

arXiv2024

The Thermodynamic Model to Study the Slow Afterhyperpolarization in a Single Neuron at Different ATP Levels

The neuron consumes energy from ATP hydrolysis to maintain a far-from-equilibrium steady state inside the cell, thus all physiological functions inside the cell are modulated by thermodynamics. The neurons that manage information encoding, transferring, and processing with high energy consumption, displaying a phenomenon called slow afterhyperpolarization after burst firing, whose properties are affected by the energy conditions. Here we constructed a thermodynamical model to quantitatively describe the sAHP process generated by $Na^+-K^+$ ATPases(NKA) and the Calcium-activated potassium(K(Ca)) channels. The model simulates how the amplitude of sAHP is effected by the intracellular ATP concentration and ATP hydrolysis free energy $Δ$ G. The results show a trade-off between NKA and the K(Ca)'s modulation on the sAHP's energy dependence, and also predict an alteration of sAHP's behavior under insufficient ATP supply if the proportion of NKA and K(Ca)'s expression quantities is changed. The research provides insights in understanding the maintenance of neural homeostasis and support furthur researches on metabolism-related and neurodegenerative diseases.

q-bio.NC↗

arXiv2024

HARE: HumAn pRiors, a key to small language model Efficiency

Human priors play a crucial role in efficiently utilizing data in deep learning. However, with the development of large language models (LLMs), there is an increasing emphasis on scaling both model size and data volume, which often diminishes the importance of human priors in data construction. Influenced by these trends, existing Small Language Models (SLMs) mainly rely on web-scraped large-scale training data, neglecting the proper incorporation of human priors. This oversight limits the training efficiency of language models in resource-constrained settings. In this paper, we propose a principle to leverage human priors for data construction. This principle emphasizes achieving high-performance SLMs by training on a concise dataset that accommodates both semantic diversity and data quality consistency, while avoiding benchmark data leakage. Following this principle, we train an SLM named HARE-1.1B. Extensive experiments on large-scale benchmark datasets demonstrate that HARE-1.1B performs favorably against state-of-the-art SLMs, validating the effectiveness of the proposed principle. Additionally, this provides new insights into efficient language model training in resource-constrained environments from the view of human priors.

cs.CL↗