Suppr超能文献

甲壳类动物牵张感受器神经元的数学模型。感受器肌肉的生物力学、机械敏感离子通道及宏换能器特性。

A mathematical model of the crustacean stretch receptor neuron. Biomechanics of the receptor muscle, mechanosensitive ion channels, and macrotransducer properties.

作者信息

Swerup C, Rydqvist B

机构信息

Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.

出版信息

J Neurophysiol. 1996 Oct;76(4):2211-20. doi: 10.1152/jn.1996.76.4.2211.

Abstract
  1. A mathematical model of the primary transduction process in a mechanoreceptor, the slowly adapting stretch receptor organ of the crayfish, has been developed taking into account the viscoelastic properties of the accessory structures of the receptor, i.e., the receptor muscle, the biophysical properties of the mechanosensitive channels (MSCs) and the passive electrical properties of the neuronal membrane (leak conductance and capacitative properties). The work is part of an effort to identify and characterize the mechanical and ionic mechanisms in a complex mechanoreceptor. The parameters of the model are based mainly on results of our own experiments and to some extent on results from other studies. The performance of the model has been compared with the performance of the slowly adapting receptor. 2. The model resulted in nonlinear differential equations that were solved by an iterative, fourth order Range-Kutta method. For the calculations of potential, the cell was treated as an idealized spherical body. The extension of the receptor muscle was 0-30%, which is within the physiological limits for this receptor. 3. The mechanical properties of the receptor muscle were modeled by a simple Voigt element (a spring in parallel with a dashpot) in series with a nonlinear spring. This element can describe resonably well the tension development in the receptor muscle at least for large extensions (> 12%). However, for small extensions (< 12%), the muscle seems to be more stiff than for large extensions. 4. The receptor current at different extensions of the receptor was computed using typical viscoelastic parameters for a receptor muscle together with a transformation of muscle tension to tension in the neuronal dendrites and finally the properties of the mechanosensitive channels. The model fit was satisfactory in the high extension range whereas in the low extension range the deviation from the experimental results could be explained partly by insufficient modeling of the nonlinear viscoelastic properties. The voltage dependence of the receptor current was also well predicted by the model. 5. If the parameters of the viscoelastic model were adjusted for each extension so that each tension response closely resembled the experimental values, the fit of the current responses was improved but still deviated from the experimental currents. One factor that might explain the difference is the possibility that the MSCs in the stretch receptor neuron might have intrinsic adaptive properties. Introducing an exponential adaptive behavior of individual MSCs increased the ability of the model to predict the receptor current. 6. The receptor potential was calculated by modeling the neuronal membrane by a lumped leak conductance and capacitance The calculated receptor potential was higher than the experimental receptor potential. However, the fit of the receptor potential was improved substantially by introducing an adaptation of the MSCs as outlined in the preceding paragraph. the remaining discrepancy might be explained by insufficient blocking of K+ channels in the experiment. 7. The model can predict a wide range of experimental data from the slowly adapting stretch receptor neuron including the mechanical response of the receptor muscle, the receptor current and its voltage dependence, and the receptor potential. It also describes accurately the passive electrical properties of the neuronal membrane.
摘要
  1. 已构建了一个关于机械感受器(小龙虾的慢适应性牵张感受器器官)初级转导过程的数学模型,该模型考虑了感受器附属结构的粘弹性特性,即感受器肌肉的特性、机械敏感通道(MSC)的生物物理特性以及神经元膜的被动电学特性(漏电导和电容特性)。这项工作是识别和表征复杂机械感受器中机械和离子机制的努力的一部分。模型参数主要基于我们自己的实验结果,并在一定程度上基于其他研究的结果。已将该模型的性能与慢适应性感受器的性能进行了比较。2. 该模型产生了非线性微分方程,通过迭代的四阶龙格 - 库塔方法求解。为了计算电位,将细胞视为理想化的球体。感受器肌肉的伸长范围为0 - 30%,这在该感受器的生理极限范围内。3. 感受器肌肉的机械特性由一个简单的沃伊特元件(一个与阻尼器并联的弹簧)与一个非线性弹簧串联来建模。该元件至少对于较大伸长(> 12%)能较好地描述感受器肌肉中的张力发展。然而,对于较小伸长(< 12%),肌肉似乎比大伸长时更硬。4. 使用感受器肌肉的典型粘弹性参数,结合肌肉张力到神经元树突中张力的转换以及最终机械敏感通道的特性,计算了感受器在不同伸长时的电流。在高伸长范围内模型拟合令人满意,而在低伸长范围内,与实验结果的偏差部分可归因于非线性粘弹性特性建模不足。该模型对感受器电流的电压依赖性也有很好的预测。5. 如果针对每个伸长调整粘弹性模型的参数,以使每个张力响应紧密类似于实验值,则电流响应的拟合得到改善,但仍与实验电流存在偏差。一个可能解释这种差异的因素是,牵张感受器神经元中的MSC可能具有内在适应性特性。引入单个MSC的指数适应性行为提高了模型预测感受器电流的能力。6. 通过用集总漏电导和电容对神经元膜进行建模来计算感受器电位。计算得到的感受器电位高于实验感受器电位。然而,如前一段所述,通过引入MSC的适应性,感受器电位的拟合有了显著改善。剩余的差异可能是由于实验中钾通道阻断不足所致。7. 该模型可以预测来自慢适应性牵张感受器神经元的广泛实验数据,包括感受器肌肉的机械响应、感受器电流及其电压依赖性以及感受器电位。它还准确描述了神经元膜的被动电学特性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验