Multiscale modeling for computational systems biology is growing in importance for neuroscience as well as for other areas of biomedical research. Integration of recent findings in computational cell biology and synapse function, in the context of neurons and neuronal networks, will require simulations that can incorporate reaction-diffusion modeling down to the level of genomic and proteomic expression. This will allow simulation of the complex biochemical interactions occurring in cell bodies, dendrites and spines. To do this, the NEURON simulator, designed to simulate electrophysiology, will require major extensions. The primary extension will involve addition of 3-dimensional intracellular spatial integration with registration to the existing multicompartment voltage integration. Additional extensions at front and back- ends will also be needed to make the new tools fully useable. This work will be done in collaboration with the VCell team at University of Connecticut Health Center, who will provide us with code from their integration procedures and will consult on development of additional viewers that will be needed for these complex simulations. This procedure will also ensure interoperability with the VCell simulator, other existing cell biology simulators that read and write SBML (Systems Biology markup-language), and NeuroML. The extensions to NEURON will be effected through the following Specific Aims. 1. Kinetic translation from other kinetic markup languages. 2. Implementation of 3-D intracellular reaction diffusion modeling. 3. Development of two primary driving projects: a. model of ALS-related mitochondrial transport dysfunction;b. model of chemical fluxes between spine and dendrite. 4. Dissemination of the new tools. Dissemination is of critical importance to ensure that the new tool is used by some of the more than 650 active NEURON users. We will also make efforts to encourage cell neurobiologists to incorporate both NEURON and VCell into their technique repertoire. The ready translation of models between these two major platforms, using the several hundred peer-review published simulations available, will encourage simulation reuse and speed development of new applications.
The enormous complexity of brain interactions makes understanding brain diseases such as Alzheimer disease, Parkinson disease and epilepsy more difficult than understanding diseases in other organs. Part of this complexity lies in the need to understand how the chemicals that underlie learning and memory and brain energy interact with the electrical signals that travel along nerve fibers. We will extend NEURON, one of the most widely used nerve and brain simulators, so that it simulates chemical reactions as well as electric signals and allow these two types of information to be combined to understand disease.
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| Mulugeta, Lealem; Drach, Andrew; Erdemir, Ahmet et al. (2018) Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience. Front Neuroinform 12:18 |
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| Lin, Zhongwei; Tropper, Carl; Mcdougal, Robert A et al. (2017) Multithreaded Stochastic PDES for Reactions and Diffusions in Neurons. ACM Trans Model Comput Simul 27: |
| Lytton, William W; Arle, Jeff; Bobashev, Georgiy et al. (2017) Multiscale modeling in the clinic: diseases of the brain and nervous system. Brain Inform 4:219-230 |
| Ishlam Patoary, Mohammad Nazrul; Tropper, Carl; McDougal, Robert A et al. (2017) Parallel Stochastic discrete event simulation of calcium dynamics in neuron. IEEE/ACM Trans Comput Biol Bioinform : |
| Lin, Zhongwei; Tropper, Carl; Yao, Yiping et al. (2017) Load balancing for multi-threaded PDES of stochastic reaction-diffusion in neurons. J Simul 11:267-284 |
| Neymotin, Samuel A; Dura-Bernal, Salvador; Moreno, Herman et al. (2016) Computer modeling for pharmacological treatments for dystonia. Drug Discov Today Dis Models 19:51-57 |
| Neymotin, S A; McDougal, R A; Bulanova, A S et al. (2016) Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex. Neuroscience 316:344-66 |
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