Dendrite structure: Data-Driven Models to Bridge from Molecules to Morphology The highly branched structures of dendritic arbors enable the extraordinary connectivity and information-processing power of the nervous system. Altered dendritic morphologies are often associated with neurological conditions and diseases. While we know many molecular components underlying dendritic growth and structure through genetic and cell biological studies, we still do not understand how molecular interactions generate dendritic arbors, which are thousands to millions of times larger than the constituent molecules. The overall goal of this application is to develop data-driven models that predict, quantitatively, dendritic growth in Drosophila Class IV da neurons. These cells are chosen because their dendrites can be imaged with outstanding spatial and temporal resolution, and the genetic tools in flies facilitate molecular manipulations. Our central hypothesis is that the growing and shrinking tips of dendrites constitute an intermediate level of organization between molecules and morphology. This allows us to divide the large gap between genotype and phenotype into two parts: the first is from molecules to dendrite tips, and the second is from dendrite tips to morphology. The second part will be bridged using models. To attain our overall objective, we will pursue the following three specific aims: (i) We will formulate kinetic rules underlying the dynamics of dendritic tips using high-resolution, live-cell imaging to measure the birth and death of tips through branching and retraction, and the transition rates between different velocity states. (ii) We will develop multi-scale mathematical models that take as input the data such as obtained in Aim 1 and predict morphologies, which will be compared to real dendritic arbors. (iii) We will genetically perturb cytoskeletal proteins and use the models to test whether the effects on tip dynamics account for the altered dendrite structures. The expected outcome is mechanistic understanding of how morphological phenotypes emerge from molecular processes occurring at the level of dendrite tips. These results will positively impact the field by bridging genotype to phenotype and by providing insight into the pathophysiology of genetic disorders that affect neuronal structures.
The highly branched structures of dendrites enable the extraordinary connectivity and information- processing power of the nervous system. The overall goal of this application is to develop multi- scale data-driven models that predict quantitatively how the growth of dendritic tips during development gives rise to the morphology of mature dendrites. Bridging genotype to phenotype is expected to provide insight into the pathophysiology of genetic disorders that affect neuronal structures.