Department of Bionanoscience, TU Delft, 2629 HZ Delft, The Netherlands.
Cells. 2020 Nov 24;9(12):2534. doi: 10.3390/cells9122534.
A bottom-up route towards predicting evolution relies on a deep understanding of the complex network that proteins form inside cells. In a rapidly expanding panorama of experimental possibilities, the most difficult question is how to conceptually approach the disentangling of such complex networks. These can exhibit varying degrees of hierarchy and modularity, which obfuscate certain protein functions that may prove pivotal for adaptation. Using the well-established polarity network in budding yeast as a case study, we first organize current literature to highlight protein entrenchments inside polarity. Following three examples, we see how alternating between experimental novelties and subsequent emerging design strategies can construct a layered understanding, potent enough to reveal evolutionary targets. We show that if you want to understand a cell's evolutionary capacity, such as possible future evolutionary paths, seemingly unimportant proteins need to be mapped and studied. Finally, we generalize this research structure to be applicable to other systems of interest.
从底层预测进化的方法依赖于对细胞内蛋白质形成的复杂网络的深入理解。在实验可能性迅速扩大的背景下,最困难的问题是如何从概念上着手解开这些复杂的网络。这些网络可能表现出不同程度的层次结构和模块性,从而掩盖了某些可能对适应至关重要的蛋白质功能。我们以已确立的出芽酵母极性网络为例,首先组织现有文献以突出极性内蛋白质的固定。通过三个例子,我们可以看到在实验创新和后续新兴设计策略之间交替如何构建一个足以揭示进化目标的分层理解。我们表明,如果要了解细胞的进化能力,例如可能的未来进化路径,就需要映射和研究看似不重要的蛋白质。最后,我们将这种研究结构推广到其他感兴趣的系统。