Abstract
From the viewpoint of thermodynamics, gene transcription necessarily consumes free energy due to nonequilibrium processes. On the other hand, regulatory molecules present on the core promoter of a gene interact often in a dynamic, highly combinatorial, and possibly energy-dependent manner, leading to a complex promoter structure. This raises the question of how gene transcription with general promoter topology consumes free energy. We propose a biophysically intuitive approach to calculate energy consumption (quantified by the production rate of entropy) of a gene transcription process. Then, we show that the numbers of the ON and OFF states of a promoter can reduce energy consumption of the gene system and the Fano factor of mRNA, and in contrast to other regulatory ways, the cooperative binding of transcription factors to DNA sites always reduces energy consumption but amplifies the mRNA noise. While our proposed approach is general, our obtained qualitative results can in turn be used to the inference of complex promoter structure.
Highlights
Apart from the genetic information flow described by central dogma in biology, gene expression would involve other dynamical subprocesses such as switching between transcriptional active and inactive states [1, 2], recruitment of transcription factors (TFs) [3, 4], and feedback regulation [5, 6]
Revealing gene expression mechanisms using stochastic models is a significant step toward understanding intracellular processes but is a challenging task. Many gene models, such as stochastic telegraph models [7,8,9], three-stage model [10], and gene models with feedback of various forms [11,12,13,14], have been proposed to study the stochastic mechanisms of gene expression from different viewpoints. These models have successfully interpreted some biological phenomena observed in experiments [12,13,14], they assume that the gene promoters have only one transcriptionally active (ON) state and one transcriptionally inactive (OFF) state and there are transitions between these states. is assumption is not reasonable in many situations
We introduce an extra set of energy values
Summary
Apart from the genetic information flow described by central dogma in biology, gene expression would involve other dynamical subprocesses such as switching between transcriptional active and inactive states [1, 2], recruitment of transcription factors (TFs) [3, 4], and feedback regulation [5, 6]. All these processes are biochemical, giving rise to stochastic fluctuations in the mRNA abundance. We will calculate the free energy in each of these four cases and analyze the relationship between gene expression and energy dissipation
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