Transcriptional Regulation by the Numbers

Recent decades have seen dramatic advances in our ability to make quantitative measurements of the level of gene expression in organisms of all types. The data resulting from these experiments has raised the need for quantitative models that go beyond the verbal and cartoon-level descriptions that have been so useful in developing a qualitative picture of the nature of gene expression. The improvement in our quantitative description of regulatory networks and our corresponding ability to rewire these networks at will has led many to argue for an analogy between biological regulatory networks and their electronic counterparts. In the electronic setting, we can predict the output current given knowledge of the input voltage and the parameters characterizing the circuit. However, this has so far been nothing more than a hopeful analogy since the input-output functions of most quantitative models of transcriptional regulation are based on phenomenological fits with little-to-no connection to the microscopic parameters of the system. This thesis sharpens this analogy by presenting an integrated approach to understanding transcriptional regulation in bacteria in terms of the microscopic parameters involved in the decision-making processes. This is achieved by a three-pronged approach consisting of theoretical models, in vivo measurements and single-molecule experiments in vitro. The theoretical analysis is based upon two different families of models aimed at describing the output of several regulatory architectures as a function of their input parameters. Thermodynamic models of transcriptional regulation are used to predict the mean level of gene expression of several bacterial promoter architectures as a function of the concentration of the intervening regulatory proteins and their binding energies to DNA and to the associated transcriptional machinery. In recent years, however, an increasing body of work has been performed where levels of gene expression are quantified in single cells and sometimes even at the single molecule level. These measurements have revealed that "noise" in gene expression can play a significant role in decision-making processes in systems ranging from bacteria to mammalian cells. Stochastic models of transcriptional regulation predict this variability in gene expression as a function of the microscopic parameter of the system. Unlike thermodynamic models, however, the predictions from stochastic models are dependent on the rate constants describing the regulatory circuit of interest. A complete set of models that predict input-output functions of regulatory systems in bacteria as a function of not only equilibrium parameters, but also probabilities of transition between different regulatory states is presented. The second half of the thesis complements the theoretical analyses by presenting several experiments aimed at testing the various predictions generated by these models. One of the experiments is carried out in vivo and aims to test the theoretical predictions for the input-output function of simple repression in terms of its microscopic parameters such as the concentration of repressor inside the cell and its binding energy to DNA. By quantifying the output level of gene expression as a function of the intracellular absolute concentration of repressor it is shown that our models can account for the level of gene expression as a function of the input parameters over several orders of magnitude. The simple repression motif is also explored experimentally using a second method based upon evaluating fluctuations in the partitioning of regulatory proteins during the cell division process. A third set of experiments performed at the single-molecule level in vitro show how a particular repressor protein binds to DNA at two different sites and loops the intervening DNA.