Using Mathematical Modelling to Deconstruct Transcription

点击翻译按钮获取中文摘要

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.