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Tyng, V.

Publications and source records attributed to Tyng, V..

2 recordsLinked to original sources

Kinetic Model With Feedback Cycle for Age-Dependent Beta Amyloid Accumulation in Mice

Amyloid beta (A{beta}) is believed to play a key role in Alzheimers disease (AD), whose causes, progression, diagnosis, and treatment nonetheless remain poorly understood despite decades of research. Recent studies suggest that A{beta} in its various forms participates in multiple mutual feedback loops ("vicious cycles") including tauopathy, oxidative stress, inflammation, calcium dysregulation, excitotoxicity, and probably many others, eventually leading to neurodegeneration and cognitive decline. Here, we explore a simple kinetic model of a coupled feedback vicious cycle for A{beta} buildup based on literature data for Tg2576 mice. The model is used to examine the efficacy of various hypothetical therapeutic approaches, either singly or in combination, to mitigate A{beta} buildup. While our computational results support the possible efficacy of combination interventions, they also suggest caution, inasmuch as clear synergy is not found. This kinetic approach highlights the essential importance of a vicious cycle of positive feedbacks in a quantitative model.

systems biology↗

Kinetic Model of Translational Autoregulation

We investigate dynamics of a kinetic model of inhibitory autoregulation as exemplified when a protein inhibits its own production by interfering with its messenger RNA, known in molecular biology as translational autoregulation. We first show how linear models without feedback set the stage with a nonequilibrium steady state that constitutes the target of the regulation. However, regulation in the simple linear model is far from optimal. The negative feedback mechanism whereby the protein \"jams\" the mRNA greatly enhances the effectiveness of the control, with response to perturbation that is targeted, rapid, and metabolically efficient. Understanding the full dynamics of the system phase space is essential to understanding the autoregulation process.

systems biology↗