Effects of Population Dynamics on Establishment of a Restriction-Modification System in a Bacterial Host

被引:1
|
作者
Graovac, Stefan [1 ,2 ]
Rodic, Andjela [1 ,2 ]
Djordjevic, Magdalena [3 ]
Severinov, Konstantin [4 ,5 ]
Djordjevic, Marko [1 ]
机构
[1] Univ Belgrade, Fac Biol, Belgrade 11000, Serbia
[2] Univ Belgrade, Multidisciplinary PhD Program Biophys, Belgrade 11000, Serbia
[3] Univ Belgrade, Inst Phys Belgrade, Belgrade 11080, Serbia
[4] Rutgers State Univ, Waksman Inst Microbiol, Piscataway, NJ 08854 USA
[5] Skolkovo Inst Sci & Technol, Ctr Life Sci, Skolkovo 143026, Russia
基金
美国国家卫生研究院; 俄罗斯科学基金会;
关键词
restriction-modification systems; bacterial population dynamics; gene expression control; statistical thermodynamics; transcription regulation; GENE-EXPRESSION; TRANSCRIPTION REGULATION; NETWORK;
D O I
10.3390/molecules24010198
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
In vivo dynamics of protein levels in bacterial cells depend on both intracellular regulation and relevant population dynamics. Such population dynamics effects, e.g., interplay between cell and plasmid division rates, are, however, often neglected in modeling gene expression regulation. Including them in a model introduces additional parameters shared by the dynamical equations, which can significantly increase dimensionality of the parameter inference. We here analyse the importance of these effects, on a case of bacterial restriction-modification (R-M) system. We redevelop our earlier minimal model of this system gene expression regulation, based on a thermodynamic and dynamic system modeling framework, to include the population dynamics effects. To resolve the problem of effective coupling of the dynamical equations, we propose a mean-field-like procedure, which allows determining only part of the parameters at a time, by separately fitting them to expression dynamics data of individual molecular species. We show that including the interplay between kinetics of cell division and plasmid replication is necessary to explain the experimental measurements. Moreover, neglecting population dynamics effects can lead to falsely identifying non-existent regulatory mechanisms. Our results call for advanced methods to reverse-engineer intracellular regulation from dynamical data, which would also take into account the population dynamics effects.
引用
收藏
页数:17
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