Positive Dynamical Networks in Neuronal Regulation: How Tunable Variability Coexists With Robustness
Positive Dynamical Networks in Neuronal Regulation: How Tunable Variability Coexists With Robustness
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DOI：
10.1109/lcsys.2020.2997214
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发表时间：
2020-10-01
影响因子：
3
通讯作者：
Golowasch, Jorge
中科院分区：
文献类型：
--
作者：
Franci, Alessio;O'Leary, Timothy;Golowasch, Jorge
神经系统表现出高度稳定和可调节的行为，尽管在分子组成水平上存在巨大的变异性，尽管存在持续的生理和病理扰动。这种强大的灵活性是如何实现的？动态平衡积分控制已被证明是协调变异性和稳定性的关键，但所使用的解释模型缺乏对扰动的基本稳健性。我们认为，正向分子调控网络可能在协调稳定性、变异性和稳健性方面发挥重要作用。我们提出的思想是积分控制沿着网络的主导方向发生。这个缓慢的方向产生了一个非常有吸引力的，因此也是强大的子空间，沿着这个子空间，可以实现几乎完美的自我平衡调节。相关分子变量沿着这个正显性子空间的波动解释了生物物理参数(如在实验中测量的)的大的正相关变化如何与强大的调节兼容，从而解释了灵活性。由于稳健性，正网络的特性可能会受到较慢的调节过程(如昼夜节律)的影响，这为可调变异性与稳健调节兼容提供了生物学上可信的基础。还讨论了所提出的神经疾病控制理论方法的调节模型的相关性。
Neuronal systems exhibit highly stable and tunable behaviors in spite of huge variability at the molecular component level and in spite of persistent physiological and pathological perturbations. How is this robust flexibility achieved? Homeostatic integral control has been shown to be key in reconciling variability with stability, but the explanatory model used lacks basic robustness properties to perturbations. We suggest that positive molecular regulatory networks may play a major role in reconciling stability, variability and robustness. The idea we propose is that integral control happens along the dominant direction of the network. This slow direction generates a strongly attractive, and thus robust, subspace along which almost perfect homeostatic regulation can be achieved. Fluctuations of relevant molecular variables along this positive dominant subspace explain how big, positively-correlated variations of biophysical parameters (as measured in experiments) are compatible with robust regulation, thus explaining flexibility. Because of robustness, the properties of the positive network can be subject to slower tuning processes (like the circadian rhythm), which provides a biologically plausible basis for tunable variability to be compatible with robust regulation. The relevance of the proposed regulation model for control-theoretical approaches to neurological diseases is also discussed.