Institute for Molecules and Materials, Radboud University Nijmegen, 6525 AJ, Nijmegen, The Netherlands.
Anal Chem. 2022 May 24;94(20):7311-7318. doi: 10.1021/acs.analchem.2c00659. Epub 2022 May 12.
In order to create artificial enzymatic networks capable of increasingly complex behavior, an improved methodology in understanding and controlling the kinetics of these networks is needed. Here, we introduce a Bayesian analysis method allowing for the accurate inference of enzyme kinetic parameters and determination of most likely reaction mechanisms, by combining data from different experiments and network topologies in a single probabilistic analysis framework. This Bayesian approach explicitly allows us to continuously improve our parameter estimates and behavior predictions by iteratively adding new data to our models, while automatically taking into account uncertainties introduced by the experimental setups or the chemical processes in general. We demonstrate the potential of this approach by characterizing systems of enzymes compartmentalized in beads inside flow reactors. The methods we introduce here provide a new approach to the design of increasingly complex artificial enzymatic networks, making the design of such networks more efficient, and robust against the accumulation of experimental errors.
为了创建能够表现出越来越复杂行为的人工酶网络，我们需要一种改进的方法来理解和控制这些网络的动力学。在这里，我们引入了一种贝叶斯分析方法，通过将来自不同实验和网络拓扑的数据结合到一个单一的概率分析框架中，可以准确推断酶动力学参数并确定最可能的反应机制。这种贝叶斯方法通过迭代地向模型中添加新数据，同时自动考虑实验设置或一般化学过程中引入的不确定性，使我们能够不断改进参数估计和行为预测。我们通过对在流动反应器中的珠粒内分隔的酶系统进行特征描述来证明该方法的潜力。我们在这里介绍的方法为设计越来越复杂的人工酶网络提供了一种新方法，使这些网络的设计更加高效，并且对实验误差的积累具有更强的鲁棒性。
