The hierarchical organization of autocatalytic reaction networks and its relevance to the origin of life.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
09 2022
Historique:
received: 01 05 2022
accepted: 18 08 2022
revised: 21 09 2022
pubmed: 10 9 2022
medline: 24 9 2022
entrez: 9 9 2022
Statut: epublish

Résumé

Prior work on abiogenesis, the emergence of life from non-life, suggests that it requires chemical reaction networks that contain self-amplifying motifs, namely, autocatalytic cores. However, little is known about how the presence of multiple autocatalytic cores might allow for the gradual accretion of complexity on the path to life. To explore this problem, we develop the concept of a seed-dependent autocatalytic system (SDAS), which is a subnetwork that can autocatalytically self-maintain given a flux of food, but cannot be initiated by food alone. Rather, initiation of SDASs requires the transient introduction of chemical "seeds." We show that, depending on the topological relationship of SDASs in a chemical reaction network, a food-driven system can accrete complexity in a historically contingent manner, governed by rare seeding events. We develop new algorithms for detecting and analyzing SDASs in chemical reaction databases and describe parallels between multi-SDAS networks and biological ecosystems. Applying our algorithms to both an abiotic reaction network and a biochemical one, each driven by a set of simple food chemicals, we detect SDASs that are organized as trophic tiers, of which the higher tier can be seeded by relatively simple chemicals if the lower tier is already activated. This indicates that sequential activation of trophically organized SDASs by seed chemicals that are not much more complex than what already exist could be a mechanism of gradual complexification from relatively simple abiotic reactions to more complex life-like systems. Interestingly, in both reaction networks, higher-tier SDASs include chemicals that might alter emergent features of chemical systems and could serve as early targets of selection. Our analysis provides computational tools for analyzing very large chemical/biochemical reaction networks and suggests new approaches to studying abiogenesis in the lab.

Identifiants

pubmed: 36084149
doi: 10.1371/journal.pcbi.1010498
pii: PCOMPBIOL-D-22-00678
pmc: PMC9491600
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1010498

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

Proc Natl Acad Sci U S A. 2020 Oct 13;117(41):25230-25236
pubmed: 32989134
Cold Spring Harb Perspect Biol. 2012 May 01;4(5):
pubmed: 20739415
J R Soc Interface. 2012 Dec 12;10(79):20120869
pubmed: 23235265
Life (Basel). 2021 Oct 26;11(11):
pubmed: 34833016
Artif Life. 2011 Summer;17(3):203-17
pubmed: 21554111
Curr Mod Biol. 1974 May;5(4):187-96
pubmed: 4407425
Nucleic Acids Res. 2021 Jan 8;49(D1):D545-D551
pubmed: 33125081
J Theor Biol. 2013 Sep 7;332:96-107
pubmed: 23648185
Biol Direct. 2012 Jan 05;7:1; discussion 1
pubmed: 22221860
IEEE/ACM Trans Comput Biol Bioinform. 2019 Mar-Apr;16(2):510-523
pubmed: 29990045
Orig Life Evol Biosph. 2014 Apr;44(2):111-24
pubmed: 25476991
Genome Biol. 2008;9(3):R51
pubmed: 18331628
J Biol Chem. 2018 Dec 7;293(49):18854-18863
pubmed: 30282809
Proc Natl Acad Sci U S A. 2019 Mar 19;116(12):5387-5392
pubmed: 30842280
Microbiol Rev. 1988 Dec;52(4):452-84
pubmed: 3070320
J Theor Biol. 2004 Apr 21;227(4):451-61
pubmed: 15038982
J R Soc Interface. 2019 Feb 28;16(151):20180808
pubmed: 30958202
Artif Life. 2016 Fall;22(4):483-498
pubmed: 27824499
Life (Basel). 2019 Oct 23;9(4):
pubmed: 31652727
Life (Basel). 2021 Sep 14;11(9):
pubmed: 34575115
Philos Trans A Math Phys Eng Sci. 2017 Dec 28;375(2109):
pubmed: 29133442
Chemistry. 2016 Aug 26;22(36):12785-99
pubmed: 27464613
Zh Evol Biokhim Fiziol. 2015 Jan-Feb;51(1):64-74
pubmed: 25859609
Faraday Discuss. 2010;147:495-508; discussion 527-52
pubmed: 21302562
Life (Basel). 2018 Dec 08;8(4):
pubmed: 30544834
Nature. 1979 Aug 9;280(5722):445-6
pubmed: 460422
Orig Life Evol Biosph. 2016 Jun;46(2-3):149-69
pubmed: 26508401
J Phys Chem B. 2010 Mar 4;114(8):2807-13
pubmed: 20136092
J Chem Inf Comput Sci. 2000 Jul;40(4):920-6
pubmed: 10955519
Artif Life. 2012 Summer;18(3):243-66
pubmed: 22662913
Proc Biol Sci. 2020 Mar 11;287(1922):20192377
pubmed: 32156207
J Mol Evol. 2014 Dec;79(5-6):213-27
pubmed: 25428684
J R Soc Interface. 2020 Oct;17(171):20200488
pubmed: 33023395
Orig Life Evol Biosph. 2017 Dec;47(4):381-403
pubmed: 27896547
Nat Rev Microbiol. 2008 Nov;6(11):805-14
pubmed: 18820700
Science. 2020 Sep 25;369(6511):
pubmed: 32973002
Q Rev Biol. 2006 Jun;81(2):105-25
pubmed: 16776061
Sci Rep. 2021 Jan 18;11(1):1743
pubmed: 33462313
Astrobiology. 2016 Feb;16(2):181-97
pubmed: 26841066
Protein Sci. 2019 Nov;28(11):1947-1951
pubmed: 31441146
J Theor Biol. 2020 Dec 21;507:110451
pubmed: 32800733
J Theor Biol. 2008 Jun 7;252(3):411-8
pubmed: 17889904
Astrobiology. 2018 Mar;18(3):259-293
pubmed: 29489386
Nucleic Acids Res. 2000 Jan 1;28(1):27-30
pubmed: 10592173
Proc Natl Acad Sci U S A. 2010 Feb 16;107(7):2763-8
pubmed: 20160129
Science. 2013 Nov 29;342(6162):1098-100
pubmed: 24288333
Naturwissenschaften. 1971 Oct;58(10):465-523
pubmed: 4942363
Bull Math Biol. 2007 May;69(4):1199-231
pubmed: 17415616
J Mol Evol. 2018 Jan;86(1):1-10
pubmed: 29260254
Life (Basel). 2018 Aug 18;8(3):
pubmed: 30126201
Bioessays. 2021 Oct;43(10):e2100103
pubmed: 34426986
Biosystems. 2020 Feb;188:104063
pubmed: 31715221

Auteurs

Zhen Peng (Z)

Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

Jeff Linderoth (J)

Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison Wisconsin, United States of America.

David A Baum (DA)

Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
Department of Botany, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

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