Energy landscape decomposition for cell differentiation with proliferation effect

Author:

Shi Jifan12ORCID,Aihara Kazuyuki2,Li Tiejun3,Chen Luonan4567ORCID

Affiliation:

1. Research Institute of Intelligent Complex Systems, Fudan University , Shanghai 200433, China

2. International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo , Tokyo 113-0033, Japan

3. Key Laboratory of Mathematics and Its Applications (LMAM) and School of Mathematical Sciences, Peking University , Beijing 100871, China

4. Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences , Shanghai 200031, China

5. Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences , Chinese Academy of Sciences, Hangzhou 310024, China

6. School of Life Science and Technology, ShanghaiTech University , Shanghai 201210, China

7. Guangdong Institute of Intelligence Science and Technology , Zhuhai 519031, China

Abstract

Abstract 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.

Funder

Japan Science and Technology Agency

Japan Agency for Medical Research and Development

Japan Society for the Promotion of Science London

National Key Research and Development of China

Chinese Academy of Sciences

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Multidisciplinary

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