Stochastic yield catastrophes and robustness in self-assembly.
mathematical modeling
none
physics of living systems
self-assembly
stochastic effects
yield optimization
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
05 02 2020
05 02 2020
Historique:
received:
12
08
2019
accepted:
04
02
2020
pubmed:
6
2
2020
medline:
4
5
2021
entrez:
6
2
2020
Statut:
epublish
Résumé
A guiding principle in self-assembly is that, for high production yield, nucleation of structures must be significantly slower than their growth. However, details of the mechanism that impedes nucleation are broadly considered irrelevant. Here, we analyze self-assembly into finite-sized target structures employing mathematical modeling. We investigate two key scenarios to delay nucleation: (i) by introducing a slow activation step for the assembling constituents and, (ii) by decreasing the dimerization rate. These scenarios have widely different characteristics. While the dimerization scenario exhibits robust behavior, the activation scenario is highly sensitive to demographic fluctuations. These demographic fluctuations ultimately disfavor growth compared to nucleation and can suppress yield completely. The occurrence of this stochastic yield catastrophe does not depend on model details but is generic as soon as number fluctuations between constituents are taken into account. On a broader perspective, our results reveal that stochasticity is an important limiting factor for self-assembly and that the specific implementation of the nucleation process plays a significant role in determining the yield. The self-assembly of a large biological molecule from small building blocks is like finishing a puzzle of magnetic pieces by shaking the box. Even though each piece of the puzzle is attracted to its correct neighbours, the limited control makes it very hard to finish the puzzle in a short amount of time. The problem becomes even more difficult if several copies of the same puzzle are assembled in one box. If several puzzles start at the same time, the different parts might steal pieces from each other, making it impossible to successfully complete any of the puzzles. This is called a depletion trap. If the box is only shaken and there is no real control over individual pieces, these traps occur at random. Overcoming these random depletion traps is an important challenge when assembling nanostructures and other artificial molecules designed by humans without wasting many, potentially expensive, components. Previous studies have shown that when multiple copies of the same structure are assembled simultaneously, slowing the rate of initiation increases the yield of correctly-made structures. This prevents new structures from stealing pieces from existing structures before they are fully completed. Now, Gartner, Graf, Wilke et al. have used a mathematical model to show that changing the way initiation is delayed leads to different yields. This was especially true for small systems where fluctuations in the availability of the different pieces strongly enhanced the initiation of new structures. In these cases, the self-assembly process terminated undesirably with many incomplete structures. Nanostructures have various applications ranging from drug delivery to robotics. These findings suggest that in order to efficiently assemble biological molecules, the concentrations of the different building blocks need to be tightly controlled. A question for further research is to investigate strategies that reduce fluctuations in the availability of the building blocks to develop more efficient assembly protocols.
Autres résumés
Type: plain-language-summary
(eng)
The self-assembly of a large biological molecule from small building blocks is like finishing a puzzle of magnetic pieces by shaking the box. Even though each piece of the puzzle is attracted to its correct neighbours, the limited control makes it very hard to finish the puzzle in a short amount of time. The problem becomes even more difficult if several copies of the same puzzle are assembled in one box. If several puzzles start at the same time, the different parts might steal pieces from each other, making it impossible to successfully complete any of the puzzles. This is called a depletion trap. If the box is only shaken and there is no real control over individual pieces, these traps occur at random. Overcoming these random depletion traps is an important challenge when assembling nanostructures and other artificial molecules designed by humans without wasting many, potentially expensive, components. Previous studies have shown that when multiple copies of the same structure are assembled simultaneously, slowing the rate of initiation increases the yield of correctly-made structures. This prevents new structures from stealing pieces from existing structures before they are fully completed. Now, Gartner, Graf, Wilke et al. have used a mathematical model to show that changing the way initiation is delayed leads to different yields. This was especially true for small systems where fluctuations in the availability of the different pieces strongly enhanced the initiation of new structures. In these cases, the self-assembly process terminated undesirably with many incomplete structures. Nanostructures have various applications ranging from drug delivery to robotics. These findings suggest that in order to efficiently assemble biological molecules, the concentrations of the different building blocks need to be tightly controlled. A question for further research is to investigate strategies that reduce fluctuations in the availability of the building blocks to develop more efficient assembly protocols.
Identifiants
pubmed: 32022683
doi: 10.7554/eLife.51020
pii: 51020
pmc: PMC7089767
doi:
pii:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : GRK2062
Organisme : Deutsche Forschungsgemeinschaft
ID : QBM
Organisme : Aspen Center for Physics
ID : PHY-160761
Organisme : Deutsche Forschungsgemeinschaft
ID : EXC-2094 - 390783311
Informations de copyright
© 2020, Gartner et al.
Déclaration de conflit d'intérêts
FG, IG, PW, PG, EF No competing interests declared
Références
Virol J. 2010 Dec 03;7:355
pubmed: 21129200
Biophys J. 2010 Mar 17;98(6):1065-74
pubmed: 20303864
Nat Rev Genet. 2009 Oct;10(10):715-24
pubmed: 19763154
J Chem Phys. 2012 Dec 28;137(24):244107
pubmed: 23277928
Proc Natl Acad Sci U S A. 2015 May 19;112(20):6313-8
pubmed: 25941388
J Chem Phys. 2009 Oct 21;131(15):155101
pubmed: 20568884
Nat Biotechnol. 2003 Oct;21(10):1171-8
pubmed: 14520402
Nat Rev Microbiol. 2008 Jun;6(6):455-65
pubmed: 18483484
Virology. 2003 Oct 25;315(2):269-74
pubmed: 14585329
Biophys J. 2002 Aug;83(2):1217-30
pubmed: 12124301
Sci Rep. 2017 Sep 25;7(1):12295
pubmed: 28947758
Nature. 2010 Mar 25;464(7288):575-8
pubmed: 20336142
Adv Chem Phys. 2014;155:1-68
pubmed: 25663722
Nat Commun. 2015 Feb 11;6:6203
pubmed: 25669898
Science. 2015 Mar 27;347(6229):1446-52
pubmed: 25814577
Phys Rev Lett. 2014 Jun 13;112(23):238103
pubmed: 24972230
Annu Rev Phys Chem. 2007;58:35-55
pubmed: 17037977
Science. 2002 Mar 29;295(5564):2418-21
pubmed: 11923529
Soft Matter. 2014 Sep 14;10(34):6404-16
pubmed: 25005537
J Chem Phys. 2013 Sep 28;139(12):121918
pubmed: 24089730
Nature. 2013 Sep 5;501(7465):45-51
pubmed: 24005412
Biochemistry. 1999 Nov 2;38(44):14644-52
pubmed: 10545189
Nature. 2017 Dec 6;552(7683):78-83
pubmed: 29219966
J Chem Phys. 2011 Dec 7;135(21):214505
pubmed: 22149800
Nat Struct Mol Biol. 2017 Sep 7;24(9):689-699
pubmed: 28880863
J Chem Phys. 2011 Sep 14;135(10):104115
pubmed: 21932884
Science. 2017 Mar 24;355(6331):
pubmed: 28336611
J Chem Phys. 2012 Feb 28;136(8):084110
pubmed: 22380035
J Phys Chem B. 2016 Jul 7;120(26):6306-18
pubmed: 27117092
Soft Matter. 2015 Nov 14;11(42):8225-35
pubmed: 26350267
Science. 2012 Nov 30;338(6111):1177-83
pubmed: 23197527
J Chem Phys. 2015 Jul 7;143(1):014112
pubmed: 26156470
J Phys Chem A. 2008 Oct 2;112(39):9405-12
pubmed: 18754598
Phys Rev Lett. 2016 Jun 24;116(25):258103
pubmed: 27391756
Soft Matter. 2015 Dec 14;11(46):8930-8
pubmed: 26404794
Science. 1991 Nov 29;254(5036):1312-9
pubmed: 1962191
Nature. 2012 May 30;485(7400):623-6
pubmed: 22660323