In vivo calibration of kinetic rate constants

Vanderbilt University Medical Center, Nashville TN

Investigators

Abstract

Many of the most important types of interactions that occur in cells, such as the binding of transcription factors to chromatin, involve the diffusion of proteins to their binding site and subsequent capture, with the lifetime of the resultant complex defined by its characteristic binding constants. In recent years, the advent of GFP has made it possible to study such binding interactions in the context of their native environments within the cell. Two fluorescence microscopy-based techniques, fluorescence recovery after photobleaching (FRAP) and photoactivation, have proved among the most promising ways for biologists to study the dynamics of intracellular proteins and their interactions with one another and cellular components. Importantly, these techniques are widely accessible to an exceptionally large number of experimentalists, and when analyzed using appropriate models, hold the potential to provide quantitative measurements of the on and off rates of complex formation, as well as directly measure the diffusion rate of the bound and unbound species. Despite this, in vivo analysis of such reaction-diffusion type processes is still in its relative infancy due to the lack of consensus about how to extract accurate diffusion coefficients (D) and binding constants from data obtained by confocal FRAP and related techniques. Even for the case of proteins that are presumed to act as inert reporters, such as EGFP, reported measurements of D in the cytoplasm and nucleoplasm of cells vary considerably in the literature. For the more complicated case of reaction-diffusion behavior, it is even less clear how to best extract reliable kinetic constants. In addition, the influence of so-called anomalous diffusion on reaction-diffusion processes in cells has been largely unexplored. The goal of the studies proposed in this application is to address this fundamental gap in our current knowledge by developing new methods to calibrate, measure and quantify reaction-diffusion kinetics in living cells. This will be accomplished through two aims: (1) to cross-calibrate measurements of freely diffusing proteins across techniques and assess the degree of anomalous diffusion in the cellular environment, and (2) to develop and test methods to robustly quantify reaction-diffusion type behavior from FRAP and photoactivation experiments using binding of transcription factors to DNA as a model. The proper functioning of cells relies on the ability of macromolecules such as proteins and DNA to undergo reversible binding events. For many years, such transient interactions have been investigated using purified interacting partners in test tubes. However, this simplified environment does not fully reflect the complex milieu found within cells. As such, scientists still lack a fundamental understanding of how strong many binding interactions actually are in cells, where in the cell such interactions occur, what controls their specificity, and how they are regulated. Recently, it has become possible to address many of these questions using a form of fluorescence microscopy that can measure the motion of proteins in living cells. In principle, this technique could be widely used by many researchers, as the microscopes required and tools to make the proteins visible are already widely available. However, analysis of such data is complex, and requires careful mathematical modeling. Our proposed research seeks to develop and test mathematical methods that will enable the accurate measurement of intracellular binding events using these microscopy-based techniques. Successful completion of this proposed work should help make these experimental approaches available to a large number of researchers that in turn can rapidly and easily apply them to a wide range of interacting molecules. In addition, related mathematical models are used to describe types of binding interactions are relevant over all scales of biology, including for example models of synchronized insect emergence and dispersal, and the geographic spread of epidemics.