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Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone

机译:从神经生长锥的膜电位记录估算分子系统的计算方法

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摘要

Biological cells express intracellular biomolecular information to the extracellular environment as various physical responses. We show a novel computational approach to estimate intracellular biomolecular pathways from growth cone electrophysiological responses. Previously, it was shown that cGMP signaling regulates membrane potential (MP) shifts that control the growth cone turning direction during neuronal development. We present here an integrated deterministic mathematical model and Bayesian reversed-engineering framework that enables estimation of the molecular signaling pathway from electrical recordings and considers both the system uncertainty and cell-to-cell variability. Our computational method selects the most plausible molecular pathway from multiple candidates while satisfying model simplicity and considering all possible parameter ranges. The model quantitatively reproduces MP shifts depending on cGMP levels and MP variability potential in different experimental conditions. Lastly, our model predicts that chloride channel inhibition by cGMP-dependent protein kinase (PKG) is essential in the core system for regulation of the MP shifts.
机译:生物细胞以各种物理反应向细胞外环境表达细胞内生物分子信息。我们显示了一种新的计算方法,可以根据生长锥电生理反应来估计细胞内生物分子途径。以前,已证明cGMP信号调节膜电位(MP)的变化,从而控制神经元发育过程中生长锥的转向。我们在这里提出了一个综合的确定性数学模型和贝叶斯逆向工程框架,该框架能够从电记录中估算分子信号传导途径,并考虑了系统不确定性和细胞间变异性。我们的计算方法在满足模型简单性并考虑所有可能的参数范围的同时,从多个候选对象中选择了最合理的分子途径。该模型根据cGMP水平和在不同实验条件下的MP变异潜力定量再现MP位移。最后,我们的模型预测,cGMP依赖性蛋白激酶(PKG)抑制氯离子通道在调节MP转变的核心系统中至关重要。

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