Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives

被引:7
|
作者
Sharma, Siddhant [1 ,2 ,3 ]
Arya, Aayush [1 ,4 ]
Cruz, Romulo [1 ,5 ]
Cleaves, Henderson, II [1 ,6 ]
机构
[1] Blue Marble Space Inst Sci, Seattle, WA 98154 USA
[2] Univ Delhi, Deshbandhu Coll, Dept Biochem, New Delhi 110019, India
[3] Chalmers Univ Technol, Dept Chem & Chem Engn, SE-41296 Gothenburg, Sweden
[4] Lovely Profess Univ, Dept Phys, Jalandhar Delhi GT Rd, Phagwara 144001, India
[5] Natl Univ Engn, Informat & Commun Technol Ctr CTIC, Big Data Lab, Amaru 210, Lima 15333, Peru
[6] Tokyo Inst Technol, Earth Life Sci Inst, Tokyo 1528550, Japan
来源
LIFE-BASEL | 2021年 / 11卷 / 11期
关键词
prebiotic chemistry; automated chemical space searches; chemical reaction networks; computational modelling; network autocatalysis; self-replicating structures; SYSTEMS CHEMISTRY; GRAPH; PREDICTION; GENERATOR; VISUALIZATION; TAUTOMER; NETWORKS; ENERGY; ORIGIN; HCN;
D O I
10.3390/life11111140
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:19
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