Manninen Tiina, Havela Riikka, Linne Marja-Leena
Computational Neuroscience Group, Faculty of Biomedical Sciences and Engineering and BioMediTech Institute, Tampere University of Technology Tampere, Finland.
Front Neuroinform. 2017 Feb 21;11:11. doi: 10.3389/fninf.2017.00011. eCollection 2017.
The scientific community across all disciplines faces the same challenges of ensuring accessibility, reproducibility, and efficient comparability of scientific results. Computational neuroscience is a rapidly developing field, where reproducibility and comparability of research results have gained increasing interest over the past years. As the number of computational models of brain functions is increasing, we chose to address reproducibility using four previously published computational models of astrocyte excitability as an example. Although not conventionally taken into account when modeling neuronal systems, astrocytes have been shown to take part in a variety of and phenomena including synaptic transmission. Two of the selected astrocyte models describe spontaneous calcium excitability, and the other two neurotransmitter-evoked calcium excitability. We specifically addressed how well the original simulation results can be reproduced with a reimplementation of the models. Additionally, we studied how well the selected models can be reused and whether they are comparable in other stimulation conditions and research settings. Unexpectedly, we found out that three of the model publications did not give all the necessary information required to reimplement the models. In addition, we were able to reproduce the original results of only one of the models completely based on the information given in the original publications and in the errata. We actually found errors in the equations provided by two of the model publications; after modifying the equations accordingly, the original results were reproduced more accurately. Even though the selected models were developed to describe the same biological event, namely astrocyte calcium excitability, the models behaved quite differently compared to one another. Our findings on a specific set of published astrocyte models stress the importance of proper validation of the models against experimental wet-lab data from astrocytes as well as the careful review process of models. A variety of aspects of model development could be improved, including the presentation of models in publications and databases. Specifically, all necessary mathematical equations, as well as parameter values, initial values of variables, and stimuli used should be given precisely for successful reproduction of scientific results.
所有学科的科学界都面临着确保科学成果的可获取性、可重复性和高效可比性的相同挑战。计算神经科学是一个快速发展的领域,在过去几年中,研究结果的可重复性和可比性越来越受到关注。随着脑功能计算模型数量的增加,我们选择以四个先前发表的星形胶质细胞兴奋性计算模型为例来探讨可重复性问题。尽管在对神经元系统进行建模时通常不考虑星形胶质细胞,但已表明它们参与了包括突触传递在内的各种生理和病理现象。所选的两个星形胶质细胞模型描述了自发钙兴奋性,另外两个描述了神经递质诱发的钙兴奋性。我们具体探讨了通过重新实现这些模型,原始模拟结果能够被重现的程度。此外,我们研究了所选模型的可重用性以及它们在其他刺激条件和研究环境中的可比性。出乎意料的是,我们发现其中三篇模型论文没有给出重新实现模型所需的所有必要信息。此外,根据原始论文及其勘误中给出的信息,我们只能完全重现其中一个模型的原始结果。实际上,我们在两篇模型论文提供的方程中发现了错误;相应地修改方程后,能更准确地重现原始结果。尽管所选模型是为描述同一生物学事件即星形胶质细胞钙兴奋性而开发的,但它们彼此之间的行为却有很大不同。我们对一组已发表的星形胶质细胞模型的研究结果强调了根据来自星形胶质细胞的实验湿实验室数据对模型进行适当验证以及对模型进行仔细审查过程的重要性。模型开发的各个方面都可以改进,包括在论文和数据库中对模型的呈现。具体而言,为了成功重现科学结果,应精确给出所有必要的数学方程以及参数值、变量的初始值和使用的刺激。