Stochastic kinetic model of two component system signalling reveals all-or-none, graded and mixed mode stochastic switching responses.
Two-component systems (TCSs) are prevalent signal transduction systems in bacteria that control innumerable adaptive responses to environmental cues and host-pathogen interactions. We constructed a detailed stochastic kinetic model of two component signalling based on published data. Our model has been validated with flow cytometry data and used to examine reporter gene expression in response to extracellular signal strength. The model shows that, depending on the actual kinetic parameters, TCSs exhibit all-or-none, graded or mixed mode responses. In accordance with other studies, positively autoregulated TCSs exhibit all-or-none responses. Unexpectedly, our model revealed that TCSs lacking a positive feedback loop exhibit not only graded but also mixed mode responses, in which variation of the signal strength alters the level of gene expression in induced cells while the regulated gene continues to be expressed at the basal level in a substantial fraction of cells. The graded response of the TCS changes to mixed mode response by an increase of the translation initiation rate of the histidine kinase. Thus, a TCS is an evolvable design pattern capable of implementing deterministic regulation and stochastic switches associated with both graded and threshold responses. This has implications for understanding the emergence of population diversity in pathogenic bacteria and the design of genetic circuits in synthetic biology applications. The model is available in systems biology markup language (SBML) and systems biology graphical notation (SBGN) formats and can be used as a component of large-scale biochemical reaction network models.
Kierzek
Andrzej M
AM
Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK GU2 7XH. a.kierzek@surrey.ac.uk
Zhou
Lu
L
Wanner
Barry L
BL
D018407:Bacterial Physiological Phenomena; D001426:Bacterial Proteins / Q000378:metabolism; D003198:Computer Simulation; D005434:Flow Cytometry; D000071677:Histidine Kinase; D007700:Kinetics; D008954:Models, Biological; D011494:Protein Kinases / Q000378:metabolism; D015203:Reproducibility of Results; D015398:Signal Transduction / Q000502:physiology*; D018709:Statistics, Nonparametric; D013269:Stochastic Processes; D049490:Systems Biology / Q000379:methods*
D001426:Bacterial Proteins; D011494:Protein Kinases; D000071677:Histidine Kinase