Grant: Discovery Grants Program - Individual

In a typical gene expression cycle, molecular signals enter the nucleus, they interact with the transcription factors and with the DNA, one or more genes are transcribed to RNA, the transcript is edited, and then it is exported to the cytoplasm where it is translated to protein. The time required for each of these steps is a random variable due to the intrinsic randomness of diffusive search and of reactive events. My research program seeks to understand these random events mechanistically, as well as their consequences for cellular function and for bioengineering. In very rough terms, we seek to reduce these complex stochastic (random) processes to their constituent parts, to understand the parts, and then to reintegrate them in order to say something about the statistics of the whole chain of events.I propose to work on several related problems relating to the synthesis and export of messenger RNA: My students and I will elaborate a mathematical model of eukaryotic (higher-organism) transcription developed in my laboratory. To this model, we will add a model of splicing (editing to cut out non-coding intervening sequences, introns). In the meantime, we will develop some theory to treat diffusion and kinetics in highly crowded media, the prevailing conditions in the eukaryotic nucleus, as well as some statistical theory of interacting molecular machines, which is required to treat, among other things, the interaction between the spliceosome and RNA polymerase. These projects taken together will allow us to simulate in detail the gene expression processes occurring in the nucleus.In parallel, we are developing some theoretical tools for understanding the control of gene expression. These tools will provide a framework for analyzing both experimental and modeling data, and for understanding how cells control noise (random fluctuations). A control theory of gene expression which takes into account the unavoidable fluctuations in cellular function in time and across cells will also be a valuable tool for bioengineers whose designs must be robust to noise.