Abstract
The behavior of genetic motifs is determined not only by the gene-gene interactions, but also by the expression patterns of the constituent genes. Live single-molecule measurements have provided evidence that transcription initiation is a sequential process, whose kinetics plays a key role in the dynamics of mRNA and protein numbers. The extent to which it affects the behavior of cellular motifs is unknown. Here, we examine how the kinetics of transcription initiation affects the behavior of motifs performing filtering in amplitude and frequency domain. We find that the performance of each filter is degraded as transcript levels are lowered. This effect can be reduced by having a transcription process with more steps. In addition, we show that the kinetics of the stepwise transcription initiation process affects features such as filter cutoffs. These results constitute an assessment of the range of behaviors of genetic motifs as a function of the kinetics of transcription initiation, and thus will aid in tuning of synthetic motifs to attain specific characteristics without affecting their protein products.
Highlights
Genes function in networks, whose building blocks are motifs of few genes
Motivated by recent findings of the relevance of the kinetics of the process of transcription initiation on the dynamics of RNA production in bacteria [6,16], we investigated the functioning of genetic filter motifs as a function of the kinetics of transcription initiation of the constituent genes
One major concern regarding their performance is that most genes in bacteria exhibit very low expression levels
Summary
Genes function in networks, whose building blocks are motifs of few genes. Several motifs have been identified, which perform a specific function in networks [1]. In addition to the gene-gene interactions, the behavior of a motif depends on the expression pattern of each constituent gene Investigating this dependency is of relevance given recent evidence that both mean level and the cell to cell diversity in RNA and protein numbers vary between genes by several orders of magnitude [2]. We need to use models that account for the nature of gene expression, since genes with low expression levels are abundant in bacteria [2,3]. Such low numbers cause the dynamics of motifs to be poised with correlations and low copy number fluctuations
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