Literature DB >> 33171073

Modelling photosystem I as a complex interacting network.

D Montepietra1,2, M Bellingeri3,4, A M Ross4, F Scotognella4,5, D Cassi3.   

Abstract

In this paper, we model the excitation energy transfer (EET) of photosystem I (PSI) of the common pea plant Pisum sativum as a complex interacting network. The magnitude of the link energy transfer between nodes/chromophores is computed by Forster resonant energy transfer (FRET) using the pairwise physical distances between chromophores from the PDB 5L8R (Protein Data Bank). We measure the global PSI network EET efficiency adopting well-known network theory indicators: the network efficiency (Eff) and the largest connected component (LCC). We also account the number of connected nodes/chromophores to P700 (CN), a new ad hoc measure we introduce here to indicate how many nodes in the network can actually transfer energy to the P700 reaction centre. We find that when progressively removing the weak links of lower EET, the Eff decreases, while the EET paths integrity (LCC and CN) is still preserved. This finding would show that the PSI is a resilient system owning a large window of functioning feasibility and it is completely impaired only when removing most of the network links. From the study of different types of chromophore, we propose different primary functions within the PSI system: chlorophyll a (CLA) molecules are the central nodes in the EET process, while other chromophore types have different primary functions. Furthermore, we perform nodes removal simulations to understand how the nodes/chromophores malfunctioning may affect PSI functioning. We discover that the removal of the CLA triggers the fastest decrease in the Eff, confirming that CAL is the main contributors to the high EET efficiency. Our outcomes open new perspectives of research, such comparing the PSI energy transfer efficiency of different natural and agricultural plant species and investigating the light-harvesting mechanisms of artificial photosynthesis both in plant agriculture and in the field of solar energy applications.

Entities:  

Keywords:  biological network; complex network; network attack; network robustness; photosynthetic network; photosystem I

Mesh:

Substances:

Year:  2020        PMID: 33171073      PMCID: PMC7729049          DOI: 10.1098/rsif.2020.0813

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  42 in total

Review 1.  The architecture of complex weighted networks.

Authors:  A Barrat; M Barthélemy; R Pastor-Satorras; A Vespignani
Journal:  Proc Natl Acad Sci U S A       Date:  2004-03-08       Impact factor: 11.205

Review 2.  Lessons from nature about solar light harvesting.

Authors:  Gregory D Scholes; Graham R Fleming; Alexandra Olaya-Castro; Rienk van Grondelle
Journal:  Nat Chem       Date:  2011-09-23       Impact factor: 24.427

3.  The absolute quantum efficiency of bacteriochlorophyll photooxidation in reaction centres of Rhodopseudomonas spheroides.

Authors:  C A Wraight; R K Clayton
Journal:  Biochim Biophys Acta       Date:  1974-02-22

4.  Solar fuels via artificial photosynthesis.

Authors:  Devens Gust; Thomas A Moore; Ana L Moore
Journal:  Acc Chem Res       Date:  2009-12-21       Impact factor: 22.384

5.  Structure of the plant photosystem I supercomplex at 2.6 Å resolution.

Authors:  Yuval Mazor; Anna Borovikova; Ido Caspy; Nathan Nelson
Journal:  Nat Plants       Date:  2017-03-01       Impact factor: 15.793

Review 6.  A comparison between plant photosystem I and photosystem II architecture and functioning.

Authors:  Stefano Caffarri; Tania Tibiletti; Robert C Jennings; Stefano Santabarbara
Journal:  Curr Protein Pept Sci       Date:  2014       Impact factor: 3.272

Review 7.  Complex brain networks: graph theoretical analysis of structural and functional systems.

Authors:  Ed Bullmore; Olaf Sporns
Journal:  Nat Rev Neurosci       Date:  2009-02-04       Impact factor: 34.870

Review 8.  The small world of the cerebral cortex.

Authors:  Olaf Sporns; Jonathan D Zwi
Journal:  Neuroinformatics       Date:  2004

9.  Complexity, centralization, and fragility in economic networks.

Authors:  Carlo Piccardi; Lucia Tajoli
Journal:  PLoS One       Date:  2018-11-29       Impact factor: 3.240

10.  The heterogeneity in link weights may decrease the robustness of real-world complex weighted networks.

Authors:  M Bellingeri; D Bevacqua; F Scotognella; D Cassi
Journal:  Sci Rep       Date:  2019-07-23       Impact factor: 4.379

View more
  1 in total

1.  Glycinebetaine mitigates tomato chilling stress by maintaining high-cyclic electron flow rate of photosystem I and stability of photosystem II.

Authors:  Dandan Wei; Tianpeng Zhang; Bingquan Wang; Huiling Zhang; Mingyang Ma; Shufen Li; Tony H H Chen; Marian Brestic; Yang Liu; Xinghong Yang
Journal:  Plant Cell Rep       Date:  2022-02-12       Impact factor: 4.570

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.