New Publication: Modelling Plasmid Regulatory Systems

Springer have brought out on on-line encyclopedia on Molecular Life Sciences and my contribution has just been published:

Stekel D.: Modelling Plasmid Regulatory Systems. In: Bell E., Bond J., Klinman J., Masters B., Wells R. (Ed.) Molecular Life Sciences: An Encyclopedic Reference: SpringerReference ( Springer-Verlag Berlin Heidelberg, 2013.


The success of plasmids as stably inherited, autonomously replicating units depends on control circuits that ensure that positive events such as replication occur efficiently at a set average frequency and that the genetic load carried by the plasmid is at minimal metabolic cost to the host.  While selective pressure has ensured that natural plasmids do achieve this, the wish to exploit plasmids or interfere with their survival mechanisms for biotechnological applications means that we need to understand the critical features that are needed for success.  Mathematical modelling of the intracellular control circuits can help to explore different systems and to distinguish between key parameters and those whose variation will have little effect on the system.  The relatively low complexity of plasmids makes them ideal systems to model and they also provide suitable systems to test prediction from the models.  In the past, plasmid modelling has particularly focussed on the ColE1 and R1 plasmids, using both deterministic and stochastic approaches; more recent work has started to address plasmids with more complex regulatory architectures, such as RK2. This has developed our understanding of the contrasting regulatory mechanisms found in high and low copy number plasmids. The combination of mathematical modelling with robust statistical methods for parameter estimation can integrate experimental data into the model, leading to more realistically parameterized mathematical models. These have greater predictive power and are likely to play a crucial future role in the rational design of plasmids for use in biotechnology and bioprocessing.


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