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益生元化学反应空间的自动化探索:进展与展望

Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives.

作者信息

Sharma Siddhant, Arya Aayush, Cruz Romulo, Cleaves Ii Henderson James

机构信息

Blue Marble Space Institute of Science, Seattle, WA 98154, USA.

Department of Biochemistry, Deshbandhu College, University of Delhi, New Delhi 110019, India.

出版信息

Life (Basel). 2021 Oct 26;11(11):1140. doi: 10.3390/life11111140.

Abstract

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.

摘要

益生元化学通常涉及对化学反应复杂系统的研究,这些反应形成了具有大量不同物种的大型网络。这样的复杂系统可能产生了一些涌现现象,最终导致了地球上生命的起源。这种涌现所涉及的环境条件和过程可能无法完全重现,这使得实验人员难以在实验室模拟中研究益生元系统。计算化学提供了研究此类化学系统并识别最有可能展现与生命相关复杂特性的系统的有效方法。在此,我们回顾了用于模拟益生元化学反应网络的工具和技术,并概述了识别对许多生命起源模型至关重要的自我复制特征的可能方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bebc/8624352/5cc420ad61b6/life-11-01140-g001.jpg

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