PyRhO: A Multiscale Optogenetics Simulation Platform.
PyRhO: A Multiscale Optogenetics Simulation Platform.
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DOI：
10.3389/fninf.2016.00008
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发表时间：
2016
影响因子：
3.5
通讯作者：
Nikolic K
中科院分区：
文献类型：
--
作者：
Evans BD;Jarvis S;Schultz SR;Nikolic K
光遗传学已成为理解神经回路功能和控制其行为的关键工具。一系列直接光驱动的视蛋白已经从几个生物家族中遗传分离出来，具有广泛的时间和光谱特性。为了表征，理解和应用这些视蛋白，我们提出了一套集成的开源，多尺度的计算工具，称为PyRhO。开发PyRhO的目的有三个方面：（i）通过将最小实验数据集自动拟合到三态、四态或六态动力学模型来表征新的（和现有的）视蛋白，（ii）在通道、神经元和网络水平上模拟这些模型，以及（iii）通过模型选择和虚拟实验提供功能性见解。该模块是用Python编写的，带有一个额外的基于IPython/IPython notebook的GUI，允许模型拟合，模拟运行，并通过简单的网页交互共享结果。模型拟合算法与这些虚拟视蛋白的模拟环境（包括NEURON和Brian 2）的无缝集成将使神经科学家能够全面了解它们的行为，并快速识别出最适合应用于特定生物系统的变体。因此，这一过程不仅可以指导实验设计和视蛋白的选择，还可以指导神经工程反馈回路中视蛋白遗传密码的改变。通过这种方式，我们预计PyRhO将有助于显着推进光遗传学作为改变生物科学的工具。
Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behavior. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these opsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO. The purpose of developing PyRhO is three-fold: (i) to characterize new (and existing) opsins by automatically fitting a minimal set of experimental data to three-, four-, or six-state kinetic models, (ii) to simulate these models at the channel, neuron and network levels, and (iii) provide functional insights through model selection and virtual experiments in silico. The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system. This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop. In this way, we expect PyRhO will help to significantly advance optogenetics as a tool for transforming biological sciences.