Landscape control for cell fate transitions

Abstract Cell fate decision making is a core issue in systems biology with profound implications for cellular development and disease. Although dynamical system approaches using gene network models have advanced our knowledge of cell fate transitions, accurately and stably controlling these transiti...

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Main Authors: Jinchao Lv, Song Zhang, Wenjia Zhou, Chunhe Li
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Communications Physics
Online Access:https://doi.org/10.1038/s42005-025-02160-8
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author Jinchao Lv
Song Zhang
Wenjia Zhou
Chunhe Li
author_facet Jinchao Lv
Song Zhang
Wenjia Zhou
Chunhe Li
author_sort Jinchao Lv
collection DOAJ
description Abstract Cell fate decision making is a core issue in systems biology with profound implications for cellular development and disease. Although dynamical system approaches using gene network models have advanced our knowledge of cell fate transitions, accurately and stably controlling these transitions remains a great challenge. Here, we present a landscape control (LC) approach based on energy landscape theory, which manipulates specific gene targets to direct cell fate. Through testing on a two-gene mutual inhibition and self-activation (MISA) model, an epithelial-mesenchymal transition (EMT) network, and a human embryonic stem cell (HESC) network, we demonstrate that LC significantly outperforms the previous optimal least action control (OLAC) approach in both effectiveness and computational efficiency. Moreover, LC can identify key transcription factors and integrate sparse control strategies to induce specific transitions. Overall, the LC framework provides a valuable tool for studying and engineering cell fate, with potential applications in therapeutic innovation and regenerative medicine.
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spelling doaj-art-816f213ecedf46bca9b86c6dfa894f612025-08-20T02:30:42ZengNature PortfolioCommunications Physics2399-36502025-06-018111110.1038/s42005-025-02160-8Landscape control for cell fate transitionsJinchao Lv0Song Zhang1Wenjia Zhou2Chunhe Li3Institute of Science and Technology for Brain-Inspired Intelligence, Fudan UniversityInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan UniversityAcademy of Mathematics and Systems Science, Chinese Academy of SciencesInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan UniversityAbstract Cell fate decision making is a core issue in systems biology with profound implications for cellular development and disease. Although dynamical system approaches using gene network models have advanced our knowledge of cell fate transitions, accurately and stably controlling these transitions remains a great challenge. Here, we present a landscape control (LC) approach based on energy landscape theory, which manipulates specific gene targets to direct cell fate. Through testing on a two-gene mutual inhibition and self-activation (MISA) model, an epithelial-mesenchymal transition (EMT) network, and a human embryonic stem cell (HESC) network, we demonstrate that LC significantly outperforms the previous optimal least action control (OLAC) approach in both effectiveness and computational efficiency. Moreover, LC can identify key transcription factors and integrate sparse control strategies to induce specific transitions. Overall, the LC framework provides a valuable tool for studying and engineering cell fate, with potential applications in therapeutic innovation and regenerative medicine.https://doi.org/10.1038/s42005-025-02160-8
spellingShingle Jinchao Lv
Song Zhang
Wenjia Zhou
Chunhe Li
Landscape control for cell fate transitions
Communications Physics
title Landscape control for cell fate transitions
title_full Landscape control for cell fate transitions
title_fullStr Landscape control for cell fate transitions
title_full_unstemmed Landscape control for cell fate transitions
title_short Landscape control for cell fate transitions
title_sort landscape control for cell fate transitions
url https://doi.org/10.1038/s42005-025-02160-8
work_keys_str_mv AT jinchaolv landscapecontrolforcellfatetransitions
AT songzhang landscapecontrolforcellfatetransitions
AT wenjiazhou landscapecontrolforcellfatetransitions
AT chunheli landscapecontrolforcellfatetransitions