Ghosh Anandamohan
Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur, 741246, India,
J Biol Phys. 2015 Jan;41(1):49-58. doi: 10.1007/s10867-014-9365-9. Epub 2014 Oct 8.
The process of gene regulation is comprised of intrinsically random events resulting in large cell-to-cell variability in mRNA and protein numbers. With gene expression being the central dogma of molecular biology, it is essential to understand the origin and role of these fluctuations. An intriguing observation is that the number of mRNA present in a cell are not only random and small but also that they are produced in bursts. The gene switches between an active and an inactive state, and the active gene transcribes mRNA in bursts. Transcriptional noise being bursty, so are the number of proteins and the subsequent gene expression levels. It is natural to ask the question: what is the reason for the bursty mRNA dynamics? And can the bursty dynamics be shown to be entropically favorable by studying the reaction kinetics underlying the gene regulation mechanism? The dynamics being an out-of-equilibrium process, the fluctuation theorem for entropy production in the reversible reaction channel is discussed. We compute the entropy production rate for varying degrees of burstiness. We find that the reaction parameters that maximize the burstiness simultaneously maximize the entropy production rate.
基因调控过程由内在随机事件组成,导致细胞间mRNA和蛋白质数量存在很大差异。由于基因表达是分子生物学的中心法则,理解这些波动的起源和作用至关重要。一个有趣的观察结果是,细胞中存在的mRNA数量不仅随机且少,而且是以脉冲形式产生的。基因在活跃和不活跃状态之间切换,活跃基因以脉冲形式转录mRNA。转录噪声是脉冲式的,蛋白质数量和随后的基因表达水平也是如此。自然而然会提出这样一个问题:mRNA脉冲动力学的原因是什么?通过研究基因调控机制背后的反应动力学,能否证明脉冲动力学在熵方面是有利的?由于该动力学是一个非平衡过程,因此讨论了可逆反应通道中熵产生的涨落定理。我们计算了不同脉冲程度下的熵产生率。我们发现,使脉冲程度最大化的反应参数同时也使熵产生率最大化。