Department of Information Technology, Uppsala University, Uppsala SE-75105, Sweden.
Bioinformatics. 2019 Dec 15;35(24):5199-5206. doi: 10.1093/bioinformatics/btz420.
Discrete stochastic models of gene regulatory network models are indispensable tools for biological inquiry since they allow the modeler to predict how molecular interactions give rise to nonlinear system output. Model exploration with the objective of generating qualitative hypotheses about the workings of a pathway is usually the first step in the modeling process. It involves simulating the gene network model under a very large range of conditions, due to the large uncertainty in interactions and kinetic parameters. This makes model exploration highly computational demanding. Furthermore, with no prior information about the model behavior, labor-intensive manual inspection of very large amounts of simulation results becomes necessary. This limits systematic computational exploration to simplistic models.
We have developed an interactive, smart workflow for model exploration based on semi-supervised learning and human-in-the-loop labeling of data. The workflow lets a modeler rapidly discover ranges of interesting behaviors predicted by the model. Utilizing that similar simulation output is in proximity of each other in a feature space, the modeler can focus on informing the system about what behaviors are more interesting than others by labeling, rather than analyzing simulation results with custom scripts and workflows. This results in a large reduction in time-consuming manual work by the modeler early in a modeling project, which can substantially reduce the time needed to go from an initial model to testable predictions and downstream analysis.
A python-package is available at https://github.com/Wrede/mio.git.
Supplementary data are available at Bioinformatics online.
离散随机模型是基因调控网络模型不可或缺的工具，因为它们允许建模者预测分子相互作用如何产生非线性系统输出。以生成关于途径工作方式的定性假设为目标的模型探索通常是建模过程的第一步。由于相互作用和动力学参数的不确定性很大，需要在非常大的条件范围内模拟基因网络模型。这使得模型探索的计算量非常大。此外，由于对模型行为没有先验信息，因此需要对大量的模拟结果进行费力的手动检查。这限制了系统计算探索仅限于简单模型。
我们开发了一种基于半监督学习和人机交互标记数据的交互式智能模型探索工作流程。该工作流程可让建模者快速发现模型预测的有趣行为范围。利用相似的模拟输出在特征空间中彼此接近的特性，建模者可以通过标记来告知系统哪些行为比其他行为更有趣，而无需使用自定义脚本和工作流程来分析模拟结果。这使得建模者在建模项目早期减少了繁琐的手动工作，从而大大减少了从初始模型到可测试预测和下游分析所需的时间。
一个 Python 包可在 https://github.com/Wrede/mio.git 上获得。
补充数据可在生物信息学在线获得。
