Beyond Turing: mechanochemical pattern formation in biological tissues

被引:27
作者
Mercker, Moritz [1 ]
Brinkmann, Felix [1 ,2 ]
Marciniak-Czochra, Anna [1 ]
Richter, Thomas [2 ]
机构
[1] Heidelberg Univ, Inst Appl Math, BioQuant & Interdisciplinary Ctr Sci Comp IWR, Heidelberg, Germany
[2] FAUL Erlangen Nurnberg, Dept Math, Erlangen, Germany
来源
BIOLOGY DIRECT | 2016年 / 11卷
关键词
Morphogens; Tissue morphogenesis; Development; Pattern formation; Mechanochemistry; Tissue mechanics; Mechanotransduction; Reaction-diffusion; Long-range inhibition; APICAL CONSTRICTION; MECHANICAL CONTROL; SYMMETRY-BREAKING; CELL-SHAPE; MECHANOTRANSDUCTION; DEFORMATION; GROWTH; FORCE; HYDRA; DIFFERENTIATION;
D O I
10.1186/s13062-016-0124-7
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: During embryogenesis, chemical (morphogen) and mechanical patterns develop within tissues in a self-organized way. More than 60 years ago, Turing proposed his famous reaction-diffusion model for such processes, assuming chemical interactions as the main driving force in tissue patterning. However, experimental identification of corresponding molecular candidates is still incomplete. Recent results suggest that beside morphogens, also tissue mechanics play a significant role in these patterning processes. Results: Combining continuous finite strain with discrete cellular tissue models, we present and numerically investigate mechanochemical processes, in which morphogen dynamics and tissue mechanics are coupled by feedback loops. We consider three different mechanical cues involved in such feedbacks: strain, stress, and compression. Based on experimental results, for each case, we present a feedback loop spontaneously creating robust mechanochemical patterns. In contrast to Turing-type models, simple mechanochemical interaction terms are sufficient to create de novo patterns. Conclusions: Our results emphasize mechanochemical processes as possible candidates controlling different steps of embryogenesis. To motivate further experimental research discovering related mechanisms in living tissues, we also present predictive in silicio experiments.
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页数:15
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