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A short introduction to some of our current research directions

Predictive design of bacterial transcription

transcription The process of cellular decision making comes down to which genes are expressed, when they are expressed and where they are expressed. At the heart of this subject is how cells transfer information from the genome to usable proteins that steer the cell towards specific functions. Encoded on the genome, alongside the code specifying the amino acid sequence of these proteins, is the regulatory code responsible for recruiting regulatory proteins to alter the expression level of the gene of interest. At a qualitative level, the "which, when and where" of gene expression and its regulation is addressed by large-scale studies that identify which specific regulatory proteins effect the expression of which specific genes. What remains elusive in these studies is the answer to "how much"; a predictive understanding of how the level of expression depends on the base pair identity of the regulatory DNA, the copy numbers of regulatory proteins and the specific cellular environment is still missing. Much of our work is dedicated to developing quantitative models of gene expression and then testing those models with systematic genetic tuning one parameter at a time.

See for instance:

  • Robert C. Brewster*, Daniel L. Jones* and Rob Phillips (2012) Tuning Promoter Strength through RNA Polymerase Binding Site Design in Escherichia coli, PLoS Computational Biology 8(12), e1002811 [manuscript]
  • Daniel L. Jones*, Robert C. Brewster* and Rob Phillips (2014) Cell-to-Cell Variability in Gene Expression is Governed by Promoter Architecture, Science, 346 (6216), 1533-1536 [manuscript, SI]

The consequence of "overworked" transcription factors

GA9It has been said that the cell is an efficent machine where evolutionary forces quickly eliminate waste. As a result, one motif that is prevalent in bacteria is that resources are shared. This sharing forces an interconnectivity between otherwise "isolated" cellular functions which must compete for the same required resource. This is especially true in transcription where the different enzymes and transcription factor proteins that perform and control gene expression are required by different, unrelated genes across the genome. This situation is further complicated by DNA substrates that exist in high copy number; Plasmids, viral vectors, and repeated gene segments are all capable of exerting a strong demand on a given transcription factor or transcription associated enzyme and "exhausting" the available supply. The response to this exhaustion is sharp and non-linear and thus can have drastic consequences for the cell. The goal in this project is to gain the ability to quantitative predict these effects as a function of the gene architecture and molecular parameters of that architecture (such as number of binding sites, location of binding sites, copy number of proteins, etc).



See for instance:

  • Robert C. Brewster*, Franz M. Weinert*, Hernan G. Garcia, Linda Song, Mattias Rydenfelt and Rob Phillips (2014) The Transcription Factor Titration Effect Dictates Level of Gene Expression, Cell 156(6) [manuscript, SI]
  • Franz M. Weinert*, Robert C. Brewster*, Mattias Rydenfelt, Rob Phillips and Willem K. Kegel (2014) Scaling of Gene Expression with Transcription Factor Fugacity, Physical Review Letters 113, 258101 [manuscript]

The dynamics of mobile DNA elements

It is increasingly clear that the classic picture of the genome as a mostly static, inherited substrate is incomplete. The horizontal transfer of genes provides an important avenue for cells to explore DNA substrates available in the environment. We are interested in the biophysical rules that dictate the survival or extinction of distinct DNA substrates in a competitive environment. Initially, the focus of this project is to study the mutual exclusion that similar plasmids (small, autonomous circular DNA segments) experience and to elucidate the rules in the competition for survival of two similar alleles.

plasmid segregation         plasmid segregation