Systems-level measurements of biophysical parameters in the Dorsal/NF-kappaB pathway
Gregory T Reeves
Project runs from 07/01/2019 to 06/30/2022
In the past decade and a half, our understanding of developmental biology has been revolutionized by real-time, live experimental approaches, which have acquired vast quantitative data sets and challenged established views of tissue patterning. It is now understood that gene expression does not simply rely on a steady state level of morphogen signaling, and thus, simple, intuitive descriptions of tissue patterning are no longer sufficient. To continue at the forefront of research, there must be a synergism of quantitative, real-time experiments, together with predictive computational models that synthesize the wealth of data into a coherent mathematical framework. However, it has been shown that such systems biology models have “sloppy parameters,” meaning there is a large ensemble of diverse parameter sets that each fit the noisy biological data sufficiently. This greatly reduces the predictive power of the models.
Therefore, the overall objective of this proposal is to perform detailed measurements of local biophysical parameters and global morphogen gradient properties to build and constrain a predictive, computational model of the Dorsal/NF-κB gradient in the early Drosophila embryo. During this stage, NF-kappaB signaling directs the formation of muscle, skin and neurons. Furthermore, the NF-kappaB pathway is highly conserved (i.e., the same) in all animals from flies to humans, making the lessons learned about NF-kappaB signaling in fruit flies directly relevant to human cancer research.
The central hypothesis is that such measurements, acting as model constraints, will greatly increase the model’s predictive power. Our hypothesis is based on our preliminary data and previous modeling experience of this pathway. Given that we also have experience in detailed measurements of this pathway, our lab has the capacity to perform this work. Only a few labs worldwide have combined both quantitative, real-time measurements with mechanistic models in the same system, which makes our lab nearly unique.
The expected outcomes of the work will be a more detailed understanding of the NF-κB module at the local and global level, as well as a model that can generate testable predictions. We expect these outcomes to have an important positive impact, because they will advance not only our understanding of the dynamics of Dorsal gradient formation, but the general field of biological modeling. Testing our central hypothesis will show whether models necessarily contain “sloppy parameters,” or may lead to discovering additional aspects that can improve the model. The proposed research will also our general knowledge of the NF-κB signaling module that can be found in animals from Cnidarians to humans.