13 papers
This paper reviews deterministic and stochastic network-level models to explain how Arrhenius-like temperature dependencies in individual biochemical reactions transform into complex emergent system behaviors, such as non-Arrhenius scaling and thermal limits, thereby bridging empirical temperature response curves with the molecular organization of biological systems.
This paper presents a scalable Python-based algorithm that automates the identification and classification of homeostatic mechanisms in complex biological input-output networks by extending theoretical frameworks to handle multiple inputs and directly enumerating homeostatic subnetworks from connectivity structures, thereby overcoming the combinatorial and accessibility limitations of previous graph-theoretical approaches.
This paper argues that conserved quantities in mass-action networks are dual to internal cycles and proposes conjectured duality relationships between preclusters, maximal invariant polyhedral supports, and siphons.
This paper establishes new sufficient conditions for the non-explosivity and positive recurrence of stochastic reaction networks within compartments whose fragmentation rates depend on their internal species content, demonstrating that previous theoretical results for content-independent dynamics fail in this more general, biologically relevant setting.
This paper integrates Chemical Reaction Network theory and Mathematical Epidemiology to generalize the Next Generation Matrix theorem and apply a symbolic-numeric approach, utilizing the Epid-CRN Mathematica package, for analyzing the stability and bifurcation of positive ordinary differential equations.
This study employs a thermodynamically consistent reaction-diffusion framework to demonstrate that intracellular ATP levels and phosphorylation free energy act as critical regulators of trigger-wave speed, propagation direction, and the minimum critical nucleus size required for sustained spatial signaling in cellular biochemical systems.
The paper introduces HIDDENdb, a freely accessible web-based database that integrates large-scale perturbation screens, multi-omics data, and curated repositories to map and visualize genetic and protein co-dependency relationships, revealing functional modules and potential structural interactions across diverse biological contexts.
This paper proposes a nonlinear local mean-field approximation method that utilizes a first-order Taylor expansion of hazard rates to enable efficient and robust parameter estimation for quasi-reaction systems, particularly outperforming existing SDE and ODE-based approaches when dealing with large time gaps between observations and stiff biological dynamics.
This paper applies the Chemical Reaction Network theory (CRNT) and the symbolic package EpidCRN to analyze multi-strain rumor spreading models on online social networks, revealing that stability transitions occur via "relay" mechanisms governed by siphon-induced transcritical bifurcations and invasion inequalities.
This paper introduces a reduction method for Boolean networks based on dominant vertices, proving that the induced dynamics on these vertices are asymptotically equivalent to the original system, thereby simplifying the analysis of attractors, basins, and transient behaviors.
This paper departs from conventional dynamical systems analyses of genetic circuits by employing statistical mechanical models to examine how endogenous effector concentrations, rather than manually tuned parameters, regulate the stability of regulatory motifs, offering a more physiologically relevant perspective than traditional Hill function-based approaches.
This paper introduces a diagnostic framework demonstrating that rankings of gene regulatory network inference methods exhibit significant instability across evaluation protocol axes, driven primarily by shifts in relative discrimination ability rather than base rate effects, thereby challenging the assumption of ranking invariance in current benchmarking practices.
This paper introduces a one-dimensional Ising model characterized by calcium concentration and myosin-generated force to elucidate the cooperative mechanism of thin filament activation in muscle contraction, successfully validating its predictions against experimental data including the anti-cooperative effects of the drug Omecamtiv Mecarbil.