dynamical network
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2022 ◽  
Author(s):  
Sebastian Gergel ◽  
Jordi Soler ◽  
Alina Klein ◽  
Kai Schülke ◽  
Bernhard Hauer ◽  
...  

The direct regioselective oxidation of internal alkenes to ketones could simplify synthetic routes and solve a longstanding challenge in synthesis. This reaction is of particular importance because ketones are predominant moieties in valuable products as well as crucial intermediates in synthesis. Here we report the directed evolution of a ketone synthase that oxidizes internal alkenes directly to ketones with several thousand turnovers. The evolved ketone synthase benefits from more than a dozen crucial mutations, most of them distal to the active site. Computational analysis reveals that all these mutations collaborate to facilitate the formation of a highly reactive carbocation intermediate by generating a confined, rigid and preorganized active site through an enhanced dynamical network. The evolved ketone synthase fully exploits a catalytic cycle that has largely eluded small molecule catalysis and consequently enables various challenging functionalization reactions of internal alkenes. This includes the first catalytic, enantioselective oxidation of internal alkenes to ketones, as well as the formal asymmetric hydration and hydroamination of unactivated internal alkenes in combination with other biocatalysts.


Author(s):  
Wudai Liao ◽  
Haoran Chen ◽  
Jinhuan Chen ◽  
Chaochuan Zhang ◽  
Xiufen Xin ◽  
...  
Keyword(s):  

Gene ◽  
2021 ◽  
pp. 145997
Author(s):  
Kazuyuki Aihara ◽  
Rui Liu ◽  
Keiichi Koizumi ◽  
Xiaoping Liu ◽  
Luonan Chen

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2096
Author(s):  
Mingcong Zhou ◽  
Zhaoyan Wu

Topology structure and system parameters have a great influence on the dynamical behavior of dynamical networks. However, they are sometimes unknown or uncertain in advance. How to effectively identify them has been investigated in various network models, from integer-order networks to fractional-order networks with the same order. In the real world, many systems consist of subsystems with different fractional orders. Therefore, the structure identification of a dynamical network with different fractional orders is investigated in this paper. Through designing proper adaptive controllers and parameter updating laws, two network estimators are well constructed. One is for identifying only the unknown topology structure. The other is for identifying both the unknown topology structure and system parameters. Based on the Lyapunov function method and the stability theory of fractional-order dynamical systems, the theoretical results are analytically proved. The effectiveness is verified by three numerical examples as well. In addition, the designed estimators have a good performance in monitoring switching topology. From the practical viewpoint, the designed estimators can be used to monitor the change of current and voltage in the fractional-order circuit systems.


2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yu Xiong ◽  
Zhaoyan Wu

AbstractThe impulsive synchronization of a fractional-order complex-variable network is investigated. Firstly, static impulsive controllers are designed and the corresponding synchronization criteria are derived. From the criteria, the impulsive gains can be calculated. Secondly, adaptive impulsive controllers are designed. Noticeably, the impulsive gains can be adjusted to the needed values adaptively. Finally, numerical examples are provided to verify the results.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Adam Gosztolai ◽  
Alexis Arnaudon

AbstractDescribing networks geometrically through low-dimensional latent metric spaces has helped design efficient learning algorithms, unveil network symmetries and study dynamical network processes. However, latent space embeddings are limited to specific classes of networks because incompatible metric spaces generally result in information loss. Here, we study arbitrary networks geometrically by defining a dynamic edge curvature measuring the similarity between pairs of dynamical network processes seeded at nearby nodes. We show that the evolution of the curvature distribution exhibits gaps at characteristic timescales indicating bottleneck-edges that limit information spreading. Importantly, curvature gaps are robust to large fluctuations in node degrees, encoding communities until the phase transition of detectability, where spectral and node-clustering methods fail. Using this insight, we derive geometric modularity to find multiscale communities based on deviations from constant network curvature in generative and real-world networks, significantly outperforming most previous methods. Our work suggests using network geometry for studying and controlling the structure of and information spreading on networks.


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