R
Ruth E. Baker
Researcher at University of Oxford
Publications - 184
Citations - 6361
Ruth E. Baker is an academic researcher from University of Oxford. The author has contributed to research in topics: Population & Stochastic modelling. The author has an hindex of 37, co-authored 170 publications receiving 5309 citations. Previous affiliations of Ruth E. Baker include University of Minnesota.
Papers
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Journal ArticleDOI
Cyclic dermal BMP signalling regulates stem cell activation during hair regeneration
Maksim V. Plikus,Julie Ann Mayer,Damon de la Cruz,Ruth E. Baker,Philip K. Maini,Robert E. Maxson,Cheng-Ming Chuong +6 more
TL;DR: Results show that BMPs may be the long-sought ‘chalone’ inhibitors of hair growth postulated by classical experiments, and provide an example of hierarchical regulation of local organ stem cell homeostasis by the inter-organ macroenvironment.
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Vertex models of epithelial morphogenesis.
TL;DR: This review summarizes how vertex models have been used to provide insight into developmental processes and highlights current challenges in this area, including progressing these models from two to three dimensions and developing new tools for model validation.
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A random cell motility gradient downstream of FGF controls elongation of an amniote embryo
Bertrand Bénazéraf,Paul François,Ruth E. Baker,Nicolas Denans,Nicolas Denans,Charles D. Little,Olivier Pourquié +6 more
TL;DR: Tissue ablation in the chicken embryo is used to demonstrate that the caudal presomitic mesoderm (PSM) has a key role in axis elongation, and proposes that the gradient of random cell motility downstream of FGF signalling in the PSM controls posterior elongation in the amniote embryo.
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Turing's model for biological pattern formation and the robustness problem.
TL;DR: The basic properties of Turing's theory are reviewed, the successes and pitfalls of using it as a model for biological systems are highlighted, and emerging developments in the area are discussed.
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Mechanistic models versus machine learning, a fight worth fighting for the biological community?
TL;DR: There has been a vast increase in the use of machine learning models in the biomedical and clinical sciences to try and keep pace with the rate of data generation, and recent successes now beg the question of whether mechanistic models are still relevant in this area.