Abstract
BackgroundIncP-1 plasmids are broad host range plasmids that have been found in clinical and environmental bacteria. They often carry genes for antibiotic resistance or catabolic pathways. The archetypal IncP-1 plasmid RK2 is a well-characterized biological system, with a fully sequenced and annotated genome and wide range of experimental measurements. Its central control operon, encoding two global regulators KorA and KorB, is a natural example of a negatively self-regulated operon. To increase our understanding of the regulation of this operon, we have constructed a dynamical mathematical model using Ordinary Differential Equations, and employed a Bayesian inference scheme, Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm, as a way of integrating experimental measurements and a priori knowledge. We also compared MCMC and Metabolic Control Analysis (MCA) as approaches for determining the sensitivity of model parameters.ResultsWe identified two distinct sets of parameter values, with different biological interpretations, that fit and explain the experimental data. This allowed us to highlight the proportion of repressor protein as dimers as a key experimental measurement defining the dynamics of the system. Analysis of joint posterior distributions led to the identification of correlations between parameters for protein synthesis and partial repression by KorA or KorB dimers, indicating the necessary use of joint posteriors for correct parameter estimation. Using MCA, we demonstrated that the system is highly sensitive to the growth rate but insensitive to repressor monomerization rates in their selected value regions; the latter outcome was also confirmed by MCMC. Finally, by examining a series of different model refinements for partial repression by KorA or KorB dimers alone, we showed that a model including partial repression by KorA and KorB was most compatible with existing experimental data.ConclusionsWe have demonstrated that the combination of dynamical mathematical models with Bayesian inference is valuable in integrating diverse experimental data and identifying key determinants and parameters for the IncP-1 central control operon. Moreover, we have shown that Bayesian inference and MCA are complementary methods for identification of sensitive parameters. We propose that this demonstrates generic value in applying this combination of approaches to systems biology dynamical modelling.
Highlights
incompatibility group P (IncP)-1 plasmids are broad host range plasmids that have been found in clinical and environmental bacteria
We have devised a mathematical model for the transcription regulation of the central control operon of RK2 plasmids by the global regulators KorA and KorB
The monomerization rate is not relevant to the model formulation and can be neglected; and estimation of partial repression is dependent on the estimation of the protein synthesis rates and these parameters cannot be estimated independently
Summary
IncP-1 plasmids are broad host range plasmids that have been found in clinical and environmental bacteria They often carry genes for antibiotic resistance or catabolic pathways. IncP-1 Plasmids Plasmids are autonomous, extra-chromosomal, selfreplicating DNA elements typically associated with bacteria [1] They are important as they can maintain and transfer genes for antibiotic resistance [2] and other important phenotypes, often as part of transposable elements [3]. The low-copy number RK2 plasmid belongs to the plasmid incompatibility group P (IncP) of Escherichia coli (IncP-1 of Pseudomonas species) It can persist in most Gram-negative bacteria [8,9], and is referred to as having a broad host range. Their complete sequence was first compiled in 1994 [13], and improved genome sequence published in 2007 [14]
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