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Maximum entropy prediction of non-equilibrium stationary distributions for stochastic reaction networks with oscillatory dynamics

机译:具有振荡动力学的随机反应网络的非平衡静止分布的最大熵预测

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摘要

Many chemical reaction networks in biological systems present complex oscillatory dynamics. In systems such as regulatory gene networks, cell cycle, and enzymatic processes, the number of molecules involved is often far from the thermodynamic limit. Although stochastic models based on the probabilistic approach of the Chemical Master Equation (CME) have been proposed, studies in the literature have been limited by the challenges of solving the CME and the lack of computational power to perform large-scale stochastic simulations.
机译:生物系统中的许多化学反应网络存在复杂的振荡动力学。 在诸如调控基因网络,细胞周期和酶法的系统中,所涉及的分子的数量往往远远远非热力学极限。 尽管已经提出了基于化学主硕士方程(CME)的概率方法的随机模型,但文献中的研究受到解决CME和缺乏计算能力来执行大规模随机模拟的挑战的限制。

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