Quantitative Aspect of Bacillus subtilis σB Regulatory Network-A Computational Simulation
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
LM2018131
Ministry of Education Youth and Sports
61388971
RVO
CEP - Centrální evidence projektů
PubMed
36552239
PubMed Central
PMC9775250
DOI
10.3390/biology11121729
PII: biology11121729
Knihovny.cz E-zdroje
- Klíčová slova
- Bacillus subtilis sigma B, computer simulation, regulatory network,
- Publikační typ
- časopisecké články MeSH
Bacillus subtilis is a model organism used to study molecular processes in prokaryotic cells. Sigma factor B, which associates with RNA polymerase, is one of the transcriptional regulators involved in the cell's response to environmental stress. This study addresses the key question of how the levels of free SigB, which acts as the actual regulator of gene expression, are controlled. A set of chemical equations describing the network controlling the levels of free SigB was designed, leading to a set of differential equations quantifying the dynamics of the network. Utilizing a microarray-measured gene expression time series then allowed the simulation of the kinetic behavior of the network in real conditions and investigation of the role of phosphatases RsbU/RsbP transmitting the environmental signal and controlling the amounts of free SigB. Moreover, the role of kinetic constants controlling the formation of the molecular complexes, which consequently influence the amount of free SigB, was investigated. The simulation showed that although the total amount of sigma B is relatively high in the unstressed population, the amount of free SigB, which actually controls its regulon, is quite low. The simulation also allowed determination of the proportion of all the network members that were free or bound in complexes. While previously the qualitative features of B. subtilis SigB have been studied in detail, the kinetics of the network have mostly been ignored. In summary, the computational results based on experimental data provide a quantitative insight into the functioning of the SigB-dependent circuit and provide a roadmap for its further exploration in this industrially important bacterium.
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