Digital Biomedicine is an emerging interdisciplinary field that bridges computer science, life sciences, and basic medicine. It primarily uses digital technologies and computer simulations to study life systems and processes, thereby understanding the essence of life phenomena from a systemic perspective. Digital Biomedicine leverages computer simulation, big data analysis, and artificial intelligence to model dynamic life processes at various levels of biological systems, enabling the simulation and prediction of physiological and pathological phenomena. This approach provides essential tools for biomedical and life science research, supports new drug development, and advances disease diagnosis, driving innovation in biotechnology and healthcare.

Digital Biomedicine is divided into subfields based on the levels of life systems it models, such as protein structure, gene transcription, cell fate, and organ system homeostasis. In addition to constructing reductionist digital models at each level, it also aims to simulate dynamic changes across multiple levels of life systems. This is the core scientific objective of our center.

Given the intense global competition in biotechnology and healthcare, our country has prioritized the development of autonomous digital biotechnology. Data autonomy, algorithm autonomy, and application autonomy are key components of this goal. Leveraging the institute's advanced background in biological and medical research and the construction of the national human cell lineage facility, our center is poised for continuous high-quality data support. Our focus will be on dynamic life simulations based on cells as the fundamental unit of life, addressing information integration issues in multi-modal cell data, and providing intelligent models that combine "black box" and "white box" approaches to simulate dynamic life changes at various levels. By constructing causal models and applying large models, we aim to integrate and transfer prior knowledge to achieve high-quality simulations under perturbations, supporting drug and disease research.