Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, United States; Department of Physics, University of California at Berkeley, Berkeley, CA, United States; Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, United States; Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, CA, United States.
Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, United States.
Curr Top Dev Biol. 2020;137:1-35. doi: 10.1016/bs.ctdb.2019.10.010. Epub 2019 Nov 22.
Although the last 30years have witnessed the mapping of the wiring diagrams of the gene regulatory networks that dictate cell fate and animal body plans, specific understanding building on such network diagrams that shows how DNA regulatory regions control gene expression lags far behind. These networks have yet to yield the predictive power necessary to, for example, calculate how the concentration dynamics of input transcription factors and DNA regulatory sequence prescribes output patterns of gene expression that, in turn, determine body plans themselves. Here, we argue that reaching a predictive understanding of developmental decision-making calls for an interplay between theory and experiment aimed at revealing how the regulation of the processes of the central dogma dictate network connections and how network topology guides cells toward their ultimate developmental fate. To make this possible, it is crucial to break free from the snapshot-based understanding of embryonic development facilitated by fixed-tissue approaches and embrace new technologies that capture the dynamics of developmental decision-making at the single cell level, in living embryos.
尽管过去 30 年来已经绘制出了决定细胞命运和动物体型的基因调控网络的布线图,但在此基础上进一步深入了解 DNA 调控区域如何控制基因表达的工作还远远落后。这些网络还没有产生必要的预测能力,例如,无法计算输入转录因子和 DNA 调控序列的浓度动态如何规定基因表达的输出模式,而这些输出模式反过来又决定了体型本身。在这里,我们认为,要想对发育决策做出可预测的理解,就需要理论和实验相互作用,旨在揭示中心法则过程的调控如何决定网络连接,以及网络拓扑结构如何引导细胞走向最终的发育命运。为了实现这一目标,至关重要的是要摆脱固定组织方法所带来的基于静态快照的胚胎发育理解方式,而采用新的技术,在活体胚胎中以单细胞水平捕获发育决策的动态。