Various omics technologies are changing Biology into a data-driven science subject. Development of data-driven digital cell models is key for understanding system level organization and evolution principles of life, as well as for predicting cellular function under various environmental/genetic perturbations and subsequently for the design of artificial life. Consequently, the construction, analysis and design of digital cell models have become one of the core supporting technologies in synthetic biology. This paper summarized the research progress on digital cell models in the last ten years after the foundation of Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, with a focus on the development and quality control of genome-scale metabolic network for reliable metabolic pathway design and their application in guiding strain metabolic engineering. We also introduced the latest progress on developing cellular models with multiple constraints to improve prediction accuracy. At last, we briefly discussed the current challenges and future directions in digital cell model development. We believe that digital cell technology, along with genome sequencing, genome synthesis and genome editing, will greatly improve our ability in reading, writing, modifying and creating life.
Keywords: digital cell; genome-scale metabolic network; metabolic engineering; multiple constraint model; pathway design.