提高系统生物学机制建模再现性的实用资源
Practical Resources for Enhancing the Reproducibility of Mechanistic Modeling in Systems Biology
摘要：
虽然可重复性是科学方法的核心原则，但重复许多结果仍然具有挑战性。令人惊讶的是，这也适用于领域的计算结果，如系统生物学，其中有广泛的标准化工作。例如，Tiwari等人最近发现，他们只能重复系统生物学中50%的已发表模拟结果。为了提高计算系统研究的可重复性，我们确定了几种资源，研究人员可以利用这些资源使他们的研究更容易获得，可执行和理解。特别是，我们确定了几个领域标准和策展服务，以及软件工程行业开创的强大方法，我们相信许多研究人员可以采用。总之，我们相信这些方法可以大大提高系统生物学研究的可重复性。反过来，我们相信增强的可重复性将加速更复杂模型的开发，这些模型可以为精准医学和合成生物学提供信息。
英文摘要：
Although reproducibility is a core tenet of the scientific method, it remains challenging to reproduce many results. Surprisingly, this also holds true for computational results in domains such as systems biology where there have been extensive standardization efforts. For example, Tiwari et al. recently found that they could only repeat 50% of published simulation results in systems biology. Toward improving the reproducibility of computational systems research, we identified several resources that investigators can leverage to make their research more accessible, executable, and comprehensible by others. In particular, we identified several domain standards and curation services, as well as powerful approaches pioneered by the software engineering industry that we believe many investigators could adopt. Together, we believe these approaches could substantially enhance the reproducibility of systems biology research. In turn, we believe enhanced reproducibility would accelerate the development of more sophisticated models that could inform precision medicine and synthetic biology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
