How computational models contribute to our understanding of the germ line.

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How computational models contribute to our understanding of the germ line.

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2016 nî lūn-bûn

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2016年の論文

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2016年論文

@yue

2016年論文

@zh-hant

2016年論文

@zh-hk

2016年論文

@zh-mo

2016年論文

@zh-tw

2016年论文

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2016年论文

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2016年论文

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How computational models contribute to our understanding of the germ line.

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How computational models contribute to our understanding of the germ line.

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How computational models contribute to our understanding of the germ line.

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P1476

How computational models contribute to our understanding of the germ line.

@en

P2093

Hillel Kugler

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James M Osborne

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Kathryn Atwell

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Sara-Jane Dunn

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P2860

Computational models reveal a passive mechanism for cell migration in the crypt

Chaste: an open source C++ library for computational physiology and biology.

Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation

Live imaging of the Drosophila spermatogonial stem cell niche reveals novel mechanisms regulating germline stem cell output.

Predictive computation of genomic logic processing functions in embryonic development.

Qualitative networks: a symbolic approach to analyze biological signaling networks.

The molecular anatomy of spontaneous germline mutations in human testes.

Regulation of the eukaryotic cell cycle: molecular antagonism, hysteresis, and irreversible transitions.

Progression from a stem cell-like state to early differentiation in the C. elegans germ line

Evidence for selective advantage of pathogenic FGFR2 mutations in the male germ line.

P304

944-957

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P31

scholarly article

P356

10.1002/MRD.22735

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P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

E. Jane Albert Hubbard

P577

2016-09-13T00:00:00Z

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P698

27627621

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P932

5568140

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