Department of Mathematics, North Carolina State University, Raleigh, NC, USA.
Department of Mathematics, CUNY Queens College and Ph.D. Programs in Mathematics and Computer Science, CUNY Graduate Center, New York, NY, USA.
Bioinformatics. 2019 Aug 15;35(16):2873-2874. doi: 10.1093/bioinformatics/bty1069.
Biological processes are often modeled by ordinary differential equations with unknown parameters. The unknown parameters are usually estimated from experimental data. In some cases, due to the structure of the model, this estimation problem does not have a unique solution even in the case of continuous noise-free data. It is therefore desirable to check the uniqueness a priori before carrying out actual experiments. We present a new software SIAN (Structural Identifiability ANalyser) that does this. Our software can tackle problems that could not be tackled by previously developed packages.
SIAN is open-source software written in Maple and is available at https://github.com/pogudingleb/SIAN.
Supplementary data are available at Bioinformatics online.
生物过程通常通过具有未知参数的常微分方程进行建模。这些未知参数通常是根据实验数据估计的。在某些情况下，由于模型的结构，即使在没有连续噪声数据的情况下，这个估计问题也没有唯一的解。因此，在进行实际实验之前，期望能够事先检查其唯一性。我们提出了一个新的软件 SIAN（结构可识别性分析器）来做到这一点。我们的软件可以解决以前开发的软件包无法解决的问题。
SIAN 是一个用 Maple 编写的开源软件，可在 https://github.com/pogudingleb/SIAN 上获得。
补充数据可在Bioinformatics 在线获得。
