Challenges in biomolecular network reconstruction

生物信息学是一个跨学科的领域，关注如何最好地应用信息技术来探索基因组学中的大量数据，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提供了关于细胞内基因组和机制的新信息。成本正在迅速下降，许多科学家可以使用这些仪器来产生各种生物的数据。这些数量和种类的数据使科学家能够前所未有地深入了解细胞的工作。虽然湿实验室实验对于验证假设至关重要，但生命科学正在受益于计算数据分析，以提供导致假设的洞察力，优先考虑假设，并指导实验设计。这种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。网络模型可以联合收割机覆盖主要的细胞过程：代谢，运输，调节和信号。此外，模型可以被构造为反映细胞隔室，并明确捕获跨膜转运蛋白跨隔室膜的化合物转运。我们自己对网络重建步骤的生物信息学方法的研究已经确定了几个挑战，这些挑战很困难，但我们认为用今天可用的数据似乎是可以解决的。第一个挑战是提高跨细胞膜及其区室的转运覆盖率，沿着提高对由每种跨膜转运蛋白转运的化合物的预测。第二个挑战是改进转录调控网络的重建。此外，随着更多生物体的数据的产生，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源软件包中，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，该项目可能会为序列，网络构建，数据集成，数据挖掘和知识发现提供新的算法。对于生物信息学，该项目将提供改进的开源软件。对于基因组学，软件工具将帮助实验者设计实验，更好地阐明细胞的工作。对于工业来说，这些工具可以应用于发现可持续生物工艺的酶，或新微生物的生物工程。

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.r******The 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.