Computational Methods in Systems Biology - 17th International Conference, CMSB 2019, Trieste, Italy, September 18-20, 2019, Proceedings
Computational Methods in Systems Biology - 17th International Conference, CMSB 2019, Trieste, Italy, September 18-20, 2019, Proceedings
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系统生物学计算方法 - 第 17 届国际会议，CMSB 2019，意大利的里雅斯特，2019 年 9 月 18-20 日，会议记录
DOI：
10.1007/978-3-030-31304-3_7
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Piho P
中科院分区：
文献类型：
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作者：
Piho P
我们提出了一个果蝇幼虫兴奋性的模型。我们的模型最初是基于修改后的Hodgkin-Huxley方程，适用于代表变量，再生去极化（动作电位），我们偶尔观察到在细胞内记录，可以触发兴奋性接头电位在神经肌肉突触。我们修改了几个动力学方程描述电压敏感的离子电流，以前用于预测兴奋性的哺乳动物心脏房室结的肌肉细胞。由此产生的非线性微分方程有多个未知参数。因此，为了使模型适应可变兴奋性的实验观察，我们开发了一种新的粒子群优化实现。这种基于GPU的实现使我们能够采用集成模型的方法，其中每个实验观察被用来找到一个合理的参数化，导致在一组模型占细胞间的变化，肌肉兴奋性inDrosophilalartera，并与潜在的应用程序，以人口为基础的建模的其他可兴奋的细胞类型。
We present a model of excitability in larvalDrosophilamuscles. Our model was initially based on modified Hodgkin-Huxley equations, adapted to represent variable, regenerative depolarisations (action potentials) we have occasionally observed in intracellular recordings and that can be triggered by excitatory junction potentials at neuromuscular synapses. We modified several kinetic equations describing voltage sensitiveandionic currents, previously used to predict excitability in muscle cells of the mammalian cardiac atrioventricular node. The resulting nonlinear differential equations had multiple unknown parameters. Thus, to fit the model to experimental observations of variable excitability, we developed a new implementation of particle swarm optimisation. This GPU-based implementation allows us to adopt an ensemble model approach in which each experimental observation is used to find a plausible parameterisation, resulting in a set of models accounting for cell-to-cell variability of muscle excitability inDrosophilalarvae, and with potential applications to population-based modeling of other excitable cell types.