Heckmann David
Institute for Computer Science, Heinrich Heine University, 40225 Düsseldorf, Germany
Biochem Soc Trans. 2015 Dec;43(6):1172-6. doi: 10.1042/BST20150148.
How did the complex metabolic systems we observe today evolve through adaptive evolution? The fitness landscape is the theoretical framework to answer this question. Since experimental data on natural fitness landscapes is scarce, computational models are a valuable tool to predict landscape topologies and evolutionary trajectories. Careful assumptions about the genetic and phenotypic features of the system under study can simplify the design of such models significantly. The analysis of C4 photosynthesis evolution provides an example for accurate predictions based on the phenotypic fitness landscape of a complex metabolic trait. The C4 pathway evolved multiple times from the ancestral C3 pathway and models predict a smooth 'Mount Fuji' landscape accordingly. The modelled phenotypic landscape implies evolutionary trajectories that agree with data on modern intermediate species, indicating that evolution can be predicted based on the phenotypic fitness landscape. Future directions will have to include structural changes of metabolic fitness landscape structure with changing environments. This will not only answer important evolutionary questions about reversibility of metabolic traits, but also suggest strategies to increase crop yields by engineering the C4 pathway into C3 plants.
我们今天所观察到的复杂代谢系统是如何通过适应性进化演变而来的？适应度景观是回答这个问题的理论框架。由于关于自然适应度景观的实验数据稀缺，计算模型是预测景观拓扑结构和进化轨迹的宝贵工具。对所研究系统的遗传和表型特征进行仔细假设可以显著简化此类模型的设计。对C4光合作用进化的分析为基于复杂代谢性状的表型适应度景观进行准确预测提供了一个例子。C4途径从祖先的C3途径多次进化而来，模型据此预测出一个平滑的“富士山”景观。模拟的表型景观意味着进化轨迹与现代中间物种的数据相符，表明可以基于表型适应度景观来预测进化。未来的方向将不得不包括随着环境变化代谢适应度景观结构的结构变化。这不仅将回答有关代谢性状可逆性的重要进化问题，还将提出通过将C4途径工程化到C3植物中来提高作物产量的策略。
