Our ability to store and retain new information rests upon the brain's immense plastic capabilities. Experimental evidence suggests that morpho-chemical modifications at the level of single dendritic spines may contribute to learning and memory but we lack both a quantitative and a mechanistic understanding of how spines function. To address this deficit, the proposed project aims to explore how the ultra-structural three-dimensional architecture of dendritic spines shapes their electro-chemical signal transduction and how structural changes alter the transduction and thus affect synaptic efficacy. State-of-the-art 3D electron microscope (EM) reconstructions endowed with precise ionotropic receptor kinetics and accurate biophysical models will be used to construct a nanometer-resolution model for dendritic spines. To simulate the electro-chemical dynamics within such a complex multi-scale environment, advanced numeric schemes such as finite-element discretization and fast multi-level solvers will be employed. By performing detailed experiments in silico, the primary factors that influence ionic current conduction in spines will be identified and then used to systematically derive a low-dimensional spine model amenable to exact mathematical analysis. Both, the full and the reduced model will allow the consortium to study the sub-cellular information-processing capabilities of single spines, and to compare the results with in vivo and in vitro data. The models will enable researchers to develop and test spine-related experimental hypotheses and to interpret data recorded in healthy and disease-modified tissue within a unified framework. Objective 1: Reconstruct Dendritic Spines in 3D at Nanometer Resolution;Objective 2: Establish a Biophysically Realistic Nanometer-Resolution Spine Model;Objective 3: Develop High-Performance Numerical Methods to Simulate the Spine Model;Objective 4: Use Simulations and Theory to Study the Computations of Dendritic Spines. The project aims to relate the multi-scale biological organization of dendritic spines to possible functional consequences at the macroscopic and systems level. In the era of ever-increasing super-computing capabilities, any mechanistic model of synaptic transmission and postsynaptic integration should start with a clear insight into precisely how biophysics orchestrates the signal transduction at the smallest scales. Deciphering the impact of micro-structural features on spine dynamics will be a stepping-stone towards understanding neural signal propagation and synaptic plasticity, and likely reveal novel sub-cellular computational principles. The findings wil deepen our understanding of neural information processing in healthy and disease-modified brains and may lead to new designs for neuromorphic devices. Alterations in spine morphology are seen in various brain diseases [1] including Down?s syndrome and fragile X syndrome [2]. Similarly, changes of the intra-spine calcium dynamics and homeostasis have been documented for Alzheimer?s disease [3]. Developing new cures and therapies for these diseases will profit from a better understanding of the relation between the dynamical structure and the function of dendritic spines. To reach this goal, in continuation of past NSF-supported projects, we will recruit and train young scientists to meet the interdisciplinary challenges of modern multi-scale and multi-modal data-driven biology, where progess is driven not only by neuroscience, but also engineering, mathematics and physical sciences, computational science and neuroinformatics The planned collaboration between the three laboratories in the US and Germany will generate international training opportunities for graduate students and postdoctoral researchers, and the participation of researchers in programs that encourage underrepresented minorities to pursue career paths in STEM disciplines.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
1R01DA038896-01
Application #
8838316
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Radman, Thomas C
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California San Diego
Department
Neurosciences
Type
Schools of Medicine
DUNS #
City
La Jolla
State
CA
Country
United States
Zip Code
92093