CAVE: a cloud-based platform for analysis and visualization of metabolic pathways
Zhitao Mao, Qianqian Yuan, Haoran Li, Yue Zhang, Yuanyuan Huang, Chunhe Yang, Ruoyu Wang, Yongfu Yang, Yalun Wu, Shihui Yang, Xiaoping Liao, Hongwu Ma
Abstract
Flux balance analysis (FBA) is an important method for calculating optimal pathways to produce industrially important chemicals in genome-scale metabolic models (GEMs). However, for biologists, the requirement of coding skills poses a significant obstacle to using FBA for pathway analysis and engineering target identification. Additionally, a time-consuming manual drawing process is often needed to illustrate the mass flow in an FBA-calculated pathway, making it challenging to detect errors or discover interesting metabolic features. To solve this problem, we developed CAVE, a cloud-based platform for the integrated calculation, visualization, examination and correction of metabolic pathways. CAVE can analyze and visualize pathways for over 100 published GEMs or user-uploaded GEMs, allowing for quicker examination and identification of special metabolic features in a particular GEM. Additionally, CAVE offers model modification functions, such as gene/reaction removal or addition, making it easy for users to correct errors found in pathway analysis and obtain more reliable pathways. With a focus on the design and analysis of optimal pathways for biochemicals, CAVE complements existing visualization tools based on manually drawn global maps and can be applied to a broader range of organisms for rational metabolic engineering. CAVE is available at https://cave.biodesign.ac.cn/.