Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
School of Chemical Engineering, National Technical University of Athens, Zografou, Athens 15780, Greece.
Chaos. 2023 Jan;33(1):013137. doi: 10.1063/5.0122744.
Circadian rhythmicity lies at the center of various important physiological and behavioral processes in mammals, such as sleep, metabolism, homeostasis, mood changes, and more. Misalignment of intrinsic neuronal oscillations with the external day-night cycle can disrupt such processes and lead to numerous disorders. In this work, we computationally determine the limits of circadian synchronization to external light signals of different frequency, duty cycle, and simulated amplitude. Instead of modeling circadian dynamics with generic oscillator models (e.g., Kuramoto-type), we use a detailed computational neuroscience model, which integrates biomolecular dynamics, neuronal electrophysiology, and network effects. This allows us to investigate the effect of small drug molecules, such as Longdaysin, and connect our results with experimental findings. To combat the high dimensionality of such a detailed model, we employ a matrix-free approach, while our entire algorithmic pipeline enables numerical continuation and construction of bifurcation diagrams using only direct simulation. We, thus, computationally explore the effect of heterogeneity in the circadian neuronal network, as well as the effect of the corrective therapeutic intervention of Longdaysin. Last, we employ unsupervised learning to construct a data-driven embedding space for representing neuronal heterogeneity.
昼夜节律性处于哺乳动物各种重要生理和行为过程的中心，例如睡眠、代谢、内稳态、情绪变化等。内在神经元振荡与外部日夜周期的失准会破坏这些过程，并导致许多疾病。在这项工作中，我们通过计算确定了外部光信号的不同频率、占空比和模拟幅度对昼夜同步的限制。我们没有使用通用振荡器模型（例如 Kuramoto 型）来建模昼夜节律动力学，而是使用了详细的计算神经科学模型，该模型集成了生物分子动力学、神经元电生理学和网络效应。这使我们能够研究小分子药物（如 Longdaysin）的作用，并将我们的结果与实验发现联系起来。为了克服如此详细模型的高维性，我们采用了无矩阵方法，而我们的整个算法管道仅使用直接模拟就能够进行数值延续和分岔图的构建。因此，我们通过计算探索了昼夜节律神经网络中的异质性的影响，以及 Longdaysin 的纠正治疗干预的效果。最后，我们采用无监督学习来构建用于表示神经元异质性的数据驱动嵌入空间。
