Challenges in biomolecular network reconstruction

生物分子网络重建的挑战

• 批准号: RGPIN-2018-06703
• 负责人: Butler, Gregory
• 金额: $ 2.04万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750101
• 关键词: Challenges; biomolecular; network; reconstruction

Bioinformatics is an interdisciplinary area concerned with how best to apply information technology to explore the huge amounts of data in genomics, to support scientific discovery through data integration, information extraction, and knowledge discovery. Continual advances in biotechnology are providing new information about genomes and mechanisms within cells. The costs are rapidly decreasing such that many scientists can use these instruments to produce data on a wide variety of organisms. This volume and variety of data allows scientists unprecedented insight into the working of cells. While wet lab experiments are essential to validate hypotheses, the life sciences are benefiting from computational data analysis to provide insight that leads to hypotheses, to prioritise hypotheses, and to guide experimental design.rThe broad field of biological data analysis is following the trend of systems biology that places an emphasis on network models of the cell processes. This holistic view is a framework for integrating many different views provided by the many different datasets produced by today's instruments. The network model can combine coverage of the major cell processes: metabolism, transport, regulation, and signaling. Furthermore, the models may be structured to reflect the cell compartments and to explicitly capture transport of compounds across compartment membranes by the transmembrane transport proteins.Our own study of bioinformatics methods for the steps of network reconstruction has identified several challenges that are difficult yet seem solvable in our opinion with the data available today. The first challenge is improved coverage of transport across the membranes of the cell and its compartments, along with improved prediction of the compound(s) transported by each transmembrane transport protein. The second challenge is improved reconstruction of the transcriptional regulatory network. Furthermore, these challenges should become easier in the intermediate term as more data on more organisms is produced.Our aim is to integrate our algorithms into an existing open-source package for network reconstruction and to allow large scale distributed computation of the reconstruction process.This project provides benefits at several levels. For computer science, the project may contribute new algorithms for sequences, network construction, data integration, data mining, and knowledge discovery. For bioinformatics, the project will provide improved open-source software. For genomics, the software tools will assist experimentalists design experiments and better elucidate the working of cells. For industry, the tools could be applied to the discovery of enzymes for sustainable bio-based processes, or the bio-engineering of novel micro-organisms.

生物信息学是一个跨学科的领域，研究如何最好地应用信息技术来探索基因组学中的海量数据，通过数据整合、信息提取和知识发现来支持科学发现。生物技术的不断进步正在提供有关基因组和细胞内机制的新信息。成本正在迅速下降，以至于许多科学家可以使用这些仪器来产生关于各种生物体的数据。这些数量和种类繁多的数据让科学家对细胞的工作有了前所未有的洞察。虽然湿实验室实验对于验证假说是必不可少的，但生命科学正在受益于计算数据分析，以提供导致假说的洞察力，确定假说的优先顺序，并指导实验设计。r生物数据分析的广泛领域正在跟随系统生物学的趋势，强调细胞过程的网络模型。这一整体视图是一个框架，用于整合由当今仪器产生的许多不同数据集提供的许多不同视图。该网络模型可以结合主要细胞过程的覆盖：新陈代谢、运输、调节和信号。此外，这些模型可以被构造成反映细胞室，并明确地捕捉跨膜运输蛋白跨室膜的化合物运输。我们自己对网络重建步骤的生物信息学方法的研究发现了几个挑战，这些挑战在我们看来是困难的，但在我们看来，利用现有的数据似乎可以解决这些挑战。第一个挑战是提高跨细胞膜及其隔间转运的覆盖面，以及改进对每种跨膜转运蛋白转运的化合物(S)的预测。第二个挑战是改进转录调控网络的重建。此外，随着关于更多生物的更多数据的产生，这些挑战在中期内应该会变得更容易。我们的目标是将我们的算法集成到现有的开放源码包中用于网络重建，并允许重建过程的大规模分布式计算。该项目在几个层面上提供了好处。对于计算机科学来说，该项目可能会为序列、网络建设、数据集成、数据挖掘和知识发现贡献新的算法。在生物信息学方面，该项目将提供改进的开源软件。对于基因组学来说，这些软件工具将帮助实验者设计实验，并更好地阐明细胞的工作原理。对于工业来说，这些工具可以应用于可持续生物过程的酶的发现，或新型微生物的生物工程。