MOTIVATION 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. RESULTS 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. AVAILABILITY AND IMPLEMENTATION A python-package is available at https://github.com/Wrede/mio.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

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使用人在环半监督学习对随机基因调控网络模型进行智能计算探索。

动机基因调控网络模型的离散随机模型是生物学研究必不可少的工具，因为它们使建模者可以预测分子相互作用如何引起非线性系统输出。以产生关于路径工作的定性假设为目标的模型探索通常是建模过程中的第一步。由于相互作用和动力学参数存在很大的不确定性，因此它涉及在很大范围的条件下模拟基因网络模型。这使得模型探索对计算有很高的要求。此外，由于没有关于模型行为的先验信息，因此需要大量劳动密集型的人工检查大量的模拟结果。这将系统的计算探索限制为简单的模型。结果我们开发了一个交互式的，智能的工作流，用于基于半监督学习和数据在环标记的模型探索。该工作流程使建模者可以快速发现模型预测的有趣行为的范围。利用相似的模拟输出在要素空间中彼此接近，建模者可以专注于通过标记告知系统哪些行为比其他行为更有趣，而不是使用自定义脚本和工作流来分析模拟结果。这大大减少了建模人员在建模项目初期的耗时的手动工作，从而可以大大减少从初始模型到可测试的预测和下游分析所需的时间。可用性和实现可以在https：// github上找到python-package。com / Wrede / mio.git。补充信息补充数据可从Bioinformatics在线获得。