Transcription in mammalian cells is a strongly fluctuating process and produces mRNAs in 'bursts', which gives rise to broad distributions of mRNAs in individual cells. Several factors contributing to the irregular dynamics of transcription have been identified and include the probabilistic nature of reactions due to low molecule numbers, the cell cycle, cell size fluctuations, and others.
However, most of these factors can only partially account for the observed 'transcriptional noise', and some possible contributors have not been explored yet. We are using various techniques such as single-molecule RNA-FISH, next generation sequencing, genomics, and bioinformatics to investigate this subject from different angles.
The availability of many genome sequences, together with the development of next generation sequencing based assays, has produced a wealth of data that permits genome-wide analyses; comparing thousands of genes or other features can reveal trends that suggest mechanisms.
Using techniques such as RNA-seq, ChIP-seq, and PRO-seq, we generate genome-wide data that helps identifying mechanisms involved in stochastic transcription.
Cells are complex machines functioning on a molecular scale. They are often compared to electronic devices: microscopic units that process information.
Electronics might be regarded as fixed infrastructures that provide switchable channels for electric current. The devices are designed robustly enough to permit two vastly different levels of current, allowing the familiar approach of binary logics to design circuits and make them modular.
Things are very different in biological cells: nothing is ‘fixed’, everything is in flux. Individual components of cells undergo state transitions but also strongly vary in numbers.
Individual cells will have vastly different numbers of a specific mRNA, even if the cells are genetically identical and are kept under identical conditions, for instance.
Instead of a fixed infrastructure, cells are thus better regarded as highly complex joint probability distributions over their components; there is a large degree of stochastic variation.
A better understanding of how cells work and how they can be manipulated is therefore a formidable challenge. We want to advance the field by employing an interdisciplinary approach based on precise measurements, data at single-cell, single-molecule and genome-wide resolution, and theoretical analysis.