Regulatory dynamics of standard two-component systems in bacteria

Complex cellular networks regulate metabolism, environmental adaptation, and phenotypic changes in biological systems. Among the elements forming regulatory networks in bacteria are regulatory proteins such as transcription factors, which respond to exogenous and endogenous conditions. To perceive their surroundings, bacteria have evolved sensory regulatory systems of two-components. The archetype of these systems is made up of two proteins--a signal sensor and a response regulator-whose genes are usually located together in a single transcription unit. These units switch transcriptional programs in response to environmental conditions. Here, we study 14 two-component systems in Escherichia coli, which have been experimentally characterized with respect to their transcriptional regulation and their perceived signal. Given that the activity of these sensory units is connected to the rest of the transcriptional network, we first classify them as autonomous, semiautonomous or dependent, according to whether or not they use additional regulators to be transcribed. Next, we use discrete-time models to simulate their qualitative regulatory dynamics in response to their transcriptional regulation and to the activation of these systems by their cognate signals. Compared to more traditional ordinary differential equations method, ours has the advantage of being computationally simple and mathematically tractable, while keeping the ability to reproduce the phenomenology described by non-linear models. The aim of the present work is not the study of all possible behaviors of these two-component systems, but to exemplify those behaviors reported in the literature. On the other hand, most of these systems are auto-activating switches, a property that distinguishes them from the other transcription factors in the regulatory network, which are mostly auto-repressing. Based on the data, our models show dynamic behaviors that explain how most of these sensory systems convey abilities for multistationarity, and these dynamic properties could explain the phenotypic heterogeneity observed in bacterial populations. Our results are likely to have an impact in the design of synthetic signaling modules.