期刊
NATIONAL SCIENCE REVIEW
卷 9, 期 8, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/nsr/nwac116
关键词
energy landscape; cell differentiation; stochastic systems
资金
- Japan Science and Technology Agency Moonshot RD [JPMJMS2021]
- Japan Agency for Medical Research and Development [JP21dm0307009]
- Japan Society for the Promotion of Science KAKENHI [JP20H05921]
- Institute of AI and Beyond, The University of Tokyo
- National Key R&D Program of China [2017YFA0505500]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB38040400]
- National Natural Science Foundation of China [11825102, 12131020, 31930022, 12026608]
- Beijing Academy of Artificial Intelligence (BAAI)
- Special Fund for Science and Technology Innovation Strategy of Guangdong Province [2021B0909050004, 2021B0909060002]
- Major Key Project of Peng Cheng Laboratory [PCL2021A12]
This study establishes a landscape theory for cell differentiation with a proliferation effect, and identifies two energy landscapes U and V that collectively contribute to the establishment of non-equilibrium steady differentiation.
Complex interactions between genes determine the development and differentiation of cells. We establish a landscape theory for cell differentiation with proliferation effect, in which the developmental process is modeled as a stochastic dynamical system with a birth-death term. We find that two different energy landscapes, denoted U and V, collectively contribute to the establishment of non-equilibrium steady differentiation. The potential U is known as the energy landscape leading to the steady distribution, whose metastable states stand for cell types, while V indicates the differentiation direction from pluripotent to differentiated cells. This interpretation of cell differentiation is different from the previous landscape theory without the proliferation effect. We propose feasible numerical methods and a mean-field approximation for constructing landscapes U and V. Successful applications to typical biological models demonstrate the energy landscape decomposition's validity and reveal biological insights into the considered processes.
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