Mathieu Lavigne - Molecular/Mathematical modeling: Role of nascent RNAs in chromatin and transcription dynamics

In our group, we attempt to model and understand the complex molecular interactions that drive the extraordinarily robust biological networks regulating RNA transcription levels. In particular we have assessed the impact of co-transcriptional regulatory mechanisms and we focus on nascent RNAs (nRNAs) role in defining chromatin state and genome function. First, I will focus on our discovery of a crosstalk between RNAs and Histone H2B ubiquitinylation and how we rely on interdisciplinary approaches using genome engineering, biochemistry, microscopy and chromatin and computational biology methods to achieve our goals. Second, I will share unpublished results on our exciting attempt to develop a flexible and comprehensive in-silico transcription simulation tool - iTranscribeRNA - relying on mathematics models (MM) and machine learning (ML). It is designed to help biologists to test and infer accurate principles of transcription dynamics in physiological or perturbed cellular environments. It relies on a Markov chain mathematical model to predict with high accuracy Pol2 initiation and release rates and can provide theoretical estimation of the transcription system that help biologists to interpret key biological network properties and understand mechanisms regulating the dynamics of gene expression (e.g. rates of Pol 2 binding to promoter, initiation, proximal-promoter pausing or release), splicing or repair. By testing virtually thousands of combinations of parameters in the machine (simulated data), we can find best values by fitting the output to real biological data. As such, and when combining our experiments to perturbation schemes (e.g. genetic, pharmacological, environmental), we prioritize disease-enabling steps/components of gene expression or repair mechanisms.