Agricultural and Biological Engineering, Weldon School of Biomedical Engineering, and Bindley Bioscience Center, 225 South University Street, Purdue University, West Lafayette, IN 47907, USA.
Dev Cell. 2010 Feb 16;18(2):260-74. doi: 10.1016/j.devcel.2010.01.006.
Advances in image acquisition and informatics technology have led to organism-scale spatiotemporal atlases of gene expression and protein distributions. To maximize the utility of this information for the study of developmental processes, a new generation of mathematical models is needed for discovery and hypothesis testing. Here, we develop a data-driven, geometrically accurate model of early Drosophila embryonic bone morphogenetic protein (BMP)-mediated patterning. We tested nine different mechanisms for signal transduction with feedback, eight combinations of geometry and gene expression prepatterns, and two scale-invariance mechanisms for their ability to reproduce proper BMP signaling output in wild-type and mutant embryos. We found that a model based on positive feedback of a secreted BMP-binding protein, coupled with the experimentally measured embryo geometry, provides the best agreement with population mean image data. Our results demonstrate that using bioimages to build and optimize a three-dimensional model provides significant insights into mechanisms that guide tissue patterning.
图像采集和信息学技术的进步已经导致了生物体规模的基因表达和蛋白质分布的时空图谱。为了最大限度地利用这些信息来研究发育过程，需要新一代的数学模型来进行发现和假设检验。在这里，我们开发了一种数据驱动的、几何上精确的早期果蝇胚胎骨形态发生蛋白（BMP）介导的模式形成模型。我们测试了九种具有反馈的信号转导机制、八种几何形状和基因表达预图案的组合以及两种尺度不变性机制，以验证它们在野生型和突变型胚胎中产生正确的 BMP 信号输出的能力。我们发现，基于分泌的 BMP 结合蛋白的正反馈的模型，与实验测量的胚胎几何形状相结合，与群体平均图像数据的吻合度最好。我们的结果表明，使用生物图像来构建和优化三维模型可以为指导组织模式形成的机制提供重要的见解。
