Using Mathematical Modelling to Deconstruct Transcription

转录是复杂的基因表达过程的第一步，通过将基因的DNA编码转录成RNA副本，转录本，随后将被翻译成蛋白质，细胞的主力，从而形成细胞的特征。因此，转录是一个高度调控的过程，一旦被破坏就会导致疾病。因此，我们确实需要了解转录调控的各个方面，以及它究竟在哪里以及如何出错。最著名的转录调控因子是DNA结合转录因子，它被认为是RNA聚合酶转录的开关。然而，最近的数据表明，许多转录因子并不作为简单地打开或关闭基因的决定点，而是在转录开始后的各个阶段，在转录延伸期间。事实上，我们最近已经表明，与RNA聚合酶相关的辅助因子，即转录延伸因子的数量是由dna结合的转录因子决定的，这在个体基因和环境条件上是不同的。为了探索转录的各个阶段，以及转录因子对这些事件的影响，我们开发了一个数学模型，该模型经过实验数据的训练，可以确定哪些转录阶段会随着突变或环境变化而变化。我们已经开始了扩展模型的过程，包括转录过程的更多细节，并在人类和酵母的实验数据上进行训练，以便它可以普遍应用。这项工作的目的是开发模型并利用其预测能力，以便我们能够描述基因转录被调节的关键步骤，以及当条件改变时，这可能如何变化。我们的建模结果将使我们能够更好地理解当环境条件改变时，当生物体受到压力时，以及在疾病中，转录是如何被破坏的，这可能很好地确定潜在的治疗靶点。我们希望这种模式在学术界得到广泛传播。

Transcription is the first step in the complex process of gene expression that brings the characteristics of a cell into being, by transcribing the DNA code of genes into RNA copies, transcripts, which will subsequently be translated into proteins, the workhorses of a cell. As such, transcription is a highly regulated process that when disrupted can cause disease. Thus, there is a real need to understand all aspects of transcriptional regulation and exactly where and how it can go wrong.The best-known regulators of transcription are the DNA binding transcription factors which were thought to act as on/off switches for transcription by RNA polymerase. However, recent data suggests that many transcription factors do not act as the decision point for simply switching a gene on or off but at various stages following the start of transcription, during transcription elongation. Indeed, we have recently shown that the amount of the accessory factors associated with RNA polymerase, known as transcription elongation factors, is determined by the DNA-bound transcription factors, and that this differs on individual genes and with environmental conditions.In order to explore the various stages of transcription, and the impact of transcription factors on these events, we have developed a mathematical model that is trained on experimental data and can determine which of the many stages of transcription changes with mutation or environmental variation. We have begun the process of extending the model by including further details of the transcriptional process and training it on experimental data from humans and yeast, so that it can be applied generally. The purpose of this work to develop the model and exploit its predictive power so that we can describe the key steps at which the transcription of a gene is regulated and how this is likely to change when conditions change. The results of our modelling will enable us to move to a better understanding of how transcription is disrupted when environmental conditions change, when organisms are stressed and in disease where it may well identify potential therapeutic targets. We intend that the model becomes widely dispersed in the academic community.