Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology.
Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology.
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DOI:
10.1088/1478-3975/ac8c16
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发表时间:
2022-09-09
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影响因子:
2
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中科院分区:
文献类型:
--
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Cells with the same genome can exist in different phenotypes. and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis development. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.
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影响因子:
48
作者:
Buggenthin F;Buettner F;Hoppe PS;Endele M;Kroiss M;Strasser M;Schwarzfischer M;Loeffler D;Kokkaliaris KD;Hilsenbeck O;Schroeder T;Theis FJ;Marr C
通讯作者:
Marr C
影响因子:
56.9
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Battich, Nico;Beumer, Joep;van Oudenaarden, Alexander
通讯作者:
van Oudenaarden, Alexander
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4.4
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Gillespie, DT
通讯作者:
Gillespie, DT
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56.9
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FRAUENFELDER, H;SLIGAR, SG;WOLYNES, PG
通讯作者:
WOLYNES, PG
影响因子:
7.7
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Alizadeh, Elaheh;Castle, Jordan;Prasad, Ashok
通讯作者:
Prasad, Ashok