In silicon emergence of an autonomous artificial metabolic system
Chen, S.
Show abstract
In this work, we establish and evolve an artificial metabolic system in silicon to shed light on how the metabolic mechanism emerged. This system is composed of two subsystems: the artificial genome subsystem (AGS) and the artificial metabolite subsystem (AMS). The whole system is designed to be capable of being autonomous: the dynamics of AGS is capable of situating itself to the dynamics of AMS to provide it with enzymes in the right time and quantity; the dynamics of AMS is capable of implementing the metabolic function and harvest energy so as to pay back the energy consumption of AGS. This kind of autonomous state requires an intricate structure of the AGS. So it is almost impossible to be predetermined manually. With the help of an evolutionary computational method that has a hierarchical mutational structure, the artificial metabolic system with this kind of autonomous state eventually emerged in silicon. We find that ATP and ADP molecules have an important role in making the state of the system autonomous. We also find that the emerged structure of AGS ensemble existing biological structures in the natural cells.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Evaluating the Number of Different Genomes in a Metagenome by Means of the Compositional Spectra Approach 96%
- A Perturbation Approach for Refining Boolean Models of Cell Cycle Regulation 96%
- Analytical approach of synchronous and asynchronous update schemes applied to solving biological Boolean networks 96%
Similar papers in this journal
- scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data 96%
- Friendly-rivalry solution to the iterated n-person public-goods game 95%
- A new paradigm considering multicellular adhesion, repulsion and attraction represent diverse cellular tile patterns 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.