Added value of autoregulation and multi-step kinetics of transcription initiation

Abstract

Bacterial gene expression regulation occurs mostly during transcription, which has two main rate-limiting steps: the close complex formation, when the RNA polymerase binds to an active promoter, and the subsequent open complex formation, after which it follows elongation. Tuning these steps' kinetics by the action of e.g. transcription factors, allows for a wide diversity of dynamics. For example, adding autoregulation generates single-gene circuits able to perform more complex tasks. Using stochastic models of transcription kinetics with empirically validated parameter values, we investigate how autoregulation and the multi-step transcription initiation kinetics of single-gene autoregulated circuits can be combined to fine-tune steady state mean and cell-to-cell variability in protein expression levels, as well as response times. Next, we investigate how they can be jointly tuned to control complex behaviours, namely, time counting, switching dynamics and memory storage. Overall, our finding suggests that, in bacteria, jointly regulating a single-gene circuit's topology and the transcription initiation multi-step dynamics allows enhancing complex task performance.

A Novel Computational Method for Two-State Transcription Model with Non-Exponential ON and OFF Durations

“…The assertion was then validated in experiments [11,37], and was generalized to different models with multiple sub-OFF or sub-ON states [28,42,47], and multiple signaling pathways [21,35,36]. Also, some complex models and methods were established to estimate system parameters or calculate the probability distribution functions of transcripts [3,5,14,15,28,31,45].…”

Stationary distributions and metastable behaviour for self-regulating proteins with general lifetime distributions

“…Biochemical processes at the single-cell level involve molecules such as transcription factors that are present at low copy numbers [6,46]. The dynamics of these processes is therefore well described by stochastic Markov processes in continuous time with discrete state space [15,22,42]. While few-component or linear-kinetics systems [16] allow for exact analysis, in more complex system one often uses approximative methods [12], such as moment closure [4], linear-noise approximation [3,9], hybrid formulations [25,26,33], and multi-scale techniques [38,39].…”