ABI Innovation: Novel Methodology for Leveraging Metabolic Simulation to Improve Regulatory Reconstruction

该奖项授予罗切斯特理工学院，目的是开发新的计算方法，利用代谢模拟更好地重建基因调控网络。准确预测基因调控网络的行为对于推进生物技术、药物开发和全细胞建模等领域是必要的，而整合有关调控结构和/或行为的先前生物学知识对于成功预测至关重要。然而，使用先前的代谢知识是具有挑战性的，因为它是间接的，只能使用代谢模拟来评估。在这个项目中，该团队正在开发计算方法，将代谢预先整合到调节网络重建中。本研究的进展将提高基因调控网络重建的准确性，并有助于确定代谢模型的整合在多大程度上可以改善调控网络的重建。跨学科的研究团队结合了大规模计算密集型统计建模和分子生物学的专业知识，并有充分的装备来确保工作的有效性，可追溯性和统计合理性。一些学生将在这些活动中接受培训，来自代表性不足群体的代表将被纳入其中，结果将包括对研究界免费提供的软件工具。

This award to Rochester Institute of Technology is to develop novel computational methods to better reconstruct gene regulatory networks using metabolic simulation. Accurate prediction of the behavior of gene regulatory networks is necessary to advance areas such as biotechnology, drug development, and whole cell modeling, and the integration of prior biological knowledge about regulatory structure and/or behavior is critical for successful predictions. However, using prior metabolic knowledge is challenging, as it is indirect and can only be evaluated using metabolic simulation. In this project, the team is developing computational methods that will allow the metabolic prior to be integrated into the regulatory network reconstruction. The advances made in this study will improve the accuracy of gene regulatory network reconstruction, and will help to determine to what degree integration of metabolic models can improve regulatory network reconstruction. The interdisciplinary research team combines expertise in large-scale computationally intensive statistical modeling and molecular biology, and is well equipped to assure the validity, tractability and statistical soundness of the work. Several students will be trained in the activities, representatives from underrepresented groups will be included, and the outcomes will include software tools that are freely available to the research community.