IV. TR&D2 - Abstract The overall goal of this project is to develop the next generation computer simulation platform for spatially realistic simulation and analysis of cellular and subcellular biochemistry. Cellular systems, especially in neurons, are profoundly difficult to understand because of the interplay between spatial, biochemical and molecular complexity that occurs on multiple levels of organization, from macromolecular assemblies to synapse architecture to neural circuits. Biological complexity is daunting and scientific investigators must persevere to finds ways to overcome it. This is important because Scientific Discovery is driven by testable hypotheses which derive from our intuition and questions surrounding our current understanding of reality. But when daunting complexity confounds our intuition we struggle to conceive new hypotheses and the cycle of discovery grinds to a halt. Computational models allow investigators to probe the complex relationships between biological components, obtain new insights and intuition -- the genesis of new hypotheses. The MCell/CellBlender platform for cell modeling we are developing is expressly designed to fulfill this need, providing insight and understanding of complex cellular systems. The cell modeling tools we develop here are designed to mesh with the molecular, network, and image-derived modeling tools of TR&Ds 1, 3 and 4. The tools will be used by our Driving Biomedical Project research partners to study neuronal and synaptic structure and function and the intricate biochemical pathways involved in learning and memory in the brain. The detailed level of understanding of these systems afforded by computational modeling of these systems will provide new insights that may be applicable to many types of cell signaling pathways, and in particular should help to elucidate how dysfunctions in cell signaling may contribute to neurological and psychiatric pathology.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Biotechnology Resource Grants (P41)
Project #
5P41GM103712-09
Application #
9990798
Study Section
Special Emphasis Panel (ZRG1)
Project Start
2012-09-24
Project End
2022-07-31
Budget Start
2020-08-01
Budget End
2021-07-31
Support Year
9
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Pittsburgh
Department
Type
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15260
Lee, Ji Young; Krieger, James; Herguedas, Beatriz et al. (2018) Druggability Simulations and X-Ray Crystallography Reveal a Ligand-Binding Site in the GluA3 AMPA Receptor N-Terminal Domain. Structure :
Han, Ligong; Murphy, Robert F; Ramanan, Deva (2018) Learning Generative Models of Tissue Organization with Supervised GANs. IEEE Winter Conf Appl Comput Vis 2018:682-690
Ernst, Oliver K; Bartol, Thomas; Sejnowski, Terrence et al. (2018) Learning dynamic Boltzmann distributions as reduced models of spatial chemical kinetics. J Chem Phys 149:034107
Kaya, Cihan; Cheng, Mary H; Block, Ethan R et al. (2018) Heterogeneities in Axonal Structure and Transporter Distribution Lower Dopamine Reuptake Efficiency. eNeuro 5:
Zhao, Yixiu; Zeng, Xiangrui; Guo, Qiang et al. (2018) An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification. Bioinformatics 34:i227-i236
Antunes, G; Simoes-de-Souza, F M (2018) AMPA receptor trafficking and its role in heterosynaptic plasticity. Sci Rep 8:10349
Sparks, Samuel; Temel, Deniz B; Rout, Michael P et al. (2018) Deciphering the ""Fuzzy"" Interaction of FG Nucleoporins and Transport Factors Using Small-Angle Neutron Scattering. Structure 26:477-484.e4
Donovan-Maiye, Rory M; Langmead, Christopher J; Zuckerman, Daniel M (2018) Systematic Testing of Belief-Propagation Estimates for Absolute Free Energies in Atomistic Peptides and Proteins. J Chem Theory Comput 14:426-443
Li, Jing; Ostmeyer, Jared; Cuello, Luis G et al. (2018) Rapid constriction of the selectivity filter underlies C-type inactivation in the KcsA potassium channel. J Gen Physiol 150:1408-1420
Gupta, Sanjana; Hainsworth, Liam; Hogg, Justin S et al. (2018) Evaluation of Parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology. Proc Euromicro Int Conf Parallel Distrib Netw Based Process 2018:690-697

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