Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India.
Bioinformatics. 2019 Jan 1;35(1):88-94. doi: 10.1093/bioinformatics/bty530.
In Computational Cell Biology, whole-cell modeling and simulation is an absolute requirement to analyze and explore the cell of an organism. Despite few individual efforts on modeling, the prime obstacle hindering its development and progress is its compute-intensive nature. Towards this end, little knowledge is available on how to reduce the enormous computational overhead and which computational systems will be of use.
In this article, we present a network-based zoning approach that could potentially be utilized in the parallelization of whole-cell simulations. Firstly, we construct the protein-protein interaction graph of the whole-cell of an organism using experimental data from various sources. Based on protein interaction information, we predict protein locality and allocate confidence score to the interactions accordingly. We then identify the modules of strictly localized interacting proteins by performing interaction graph clustering based on the confidence score of the interactions. By applying this method to Escherichia coli K12, we identified 188 spatially localized clusters. After a thorough Gene Ontology-based analysis, we proved that the clusters are also in functional proximity. We then conducted Principal Coordinates Analysis to predict the spatial distribution of the clusters in the simulation space. Our automated computational techniques can partition the entire simulation space (cell) into simulation sub-cells. Each of these sub-cells can be simulated on separate computing units of the High-Performance Computing (HPC) systems. We benchmarked our method using proteins. However, our method can be extended easily to add other cellular components like DNA, RNA and metabolites.
Supplementary data are available at Bioinformatics online.
在计算细胞生物学中，对整个细胞进行建模和模拟是分析和探索生物体细胞的绝对要求。尽管在建模方面有一些个别努力，但阻碍其发展和进步的主要障碍是其计算密集型的性质。为此，关于如何减少巨大的计算开销以及哪些计算系统将有用的知识很少。
在本文中，我们提出了一种基于网络的分区方法，该方法可能可用于整个细胞模拟的并行化。首先，我们使用来自各种来源的实验数据构建生物体整个细胞的蛋白质-蛋白质相互作用图。基于蛋白质相互作用信息，我们预测蛋白质的局部性，并相应地为相互作用分配置信度得分。然后，我们通过基于相互作用置信度对相互作用图进行聚类，识别严格局部相互作用蛋白质的模块。通过将这种方法应用于大肠杆菌 K12，我们鉴定了 188 个空间本地化的簇。经过彻底的基于基因本体论的分析，我们证明了这些簇在功能上也很接近。然后，我们进行了主坐标分析，以预测这些簇在模拟空间中的空间分布。我们的自动计算技术可以将整个模拟空间（细胞）划分为模拟子细胞。这些子细胞中的每一个都可以在高性能计算 (HPC) 系统的单独计算单元上进行模拟。我们使用蛋白质对我们的方法进行了基准测试。但是，我们的方法可以很容易地扩展到添加其他细胞成分，如 DNA、RNA 和代谢物。
补充数据可在“Bioinformatics”在线获取。
