scholarly journals Autocatalytic Networks: An Intimate Relation between Network Topology and Dynamics

2021 ◽  
Vol 81 (4) ◽  
pp. 1623-1644
Author(s):  
Badal Joshi ◽  
Gheorghe Craciun
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sandeep Ameta ◽  
Simon Arsène ◽  
Sophie Foulon ◽  
Baptiste Saudemont ◽  
Bryce E. Clifton ◽  
...  

AbstractDiscovering autocatalytic chemistries that can evolve is a major goal in systems chemistry and a critical step towards understanding the origin of life. Autocatalytic networks have been discovered in various chemistries, but we lack a general understanding of how network topology controls the Darwinian properties of variation, differential reproduction, and heredity, which are mediated by the chemical composition. Using barcoded sequencing and droplet microfluidics, we establish a landscape of thousands of networks of RNAs that catalyze their own formation from fragments, and derive relationships between network topology and chemical composition. We find that strong variations arise from catalytic innovations perturbing weakly connected networks, and that growth increases with global connectivity. These rules imply trade-offs between reproduction and variation, and between compositional persistence and variation along trajectories of network complexification. Overall, connectivity in reaction networks provides a lever to balance variation (to explore chemical states) with reproduction and heredity (persistence being necessary for selection to act), as required for chemical evolution.


2019 ◽  
Author(s):  
Sandeep Ameta ◽  
Simon Arsène ◽  
Sophie Foulon ◽  
Baptiste Saudemont ◽  
Bryce E. Clifton ◽  
...  

Discovering autocatalytic chemistries that can evolve is a major goal in systems chemistry and a critical step towards understanding the origin of life. Autocatalytic networks have been discovered in various chemistries, but we lack a general understanding of how network topology controls the Darwinian properties of variation, differential reproduction, and heredity, which are mediated by the chemical composition. Using barcoded sequencing and droplet microfluidics, we establish a landscape of thousands of networks of RNAs that catalyze their own formation from fragments, and derive relationships between network topology and chemical composition. We find that strong variations arise from catalytic innovations perturbing weakly connected networks, and that reproduction increases with global connectivity. These rules imply trade-offs between reproduction and variation, and between compositional persistence and variation along trajectories of network complexification. Overall, connectivity in reaction networks provides a lever to balance variation (to explore chemical states) with reproduction and heredity (persistence being necessary for selection to act), as required for chemical evolution.


Author(s):  
Lisheng Huang ◽  
Mingyong Yin ◽  
Changchun Li ◽  
Xin Wang

Author(s):  
K. Maystrenko ◽  
A. Budilov ◽  
D. Afanasev

Goal. Identify trends and prospects for the development of radar in terms of the use of convolutional neural networks for target detection. Materials and methods. Analysis of relevant printed materials related to the subject areas of radar and convolutional neural networks. Results. The transition to convolutional neural networks in the field of radar is considered. A review of papers on the use of convolutional neural networks in pattern recognition problems, in particular, in the radar problem, is carried out. Hardware costs for the implementation of convolutional neural networks are analyzed. Conclusion. The conclusion is made about the need to create a methodology for selecting a network topology depending on the parameters of the radar task.


2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga

<div>We have thoroughly studied the paper of Perazzo et al., which presents a routing attack named DIO suppression attack with its impact analysis. However, the considered simulation grid of size 20mx20m does not correspond to the results presented in their paper. We believe that the incorrect simulation detail needs to be rectified further for the scientific correctness of the results. In this comment, it is shown that the suppression attack on such small sized network topology does not have any major impact on routing performance, and specific reason is discussed for such behavior.</div>


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