Kim "Avrama" Blackwell
Will talk about: Dynamical spatial models of signaling pathways underlying synaptic plasticity
Kim Blackwell received a PhD in Bioengineering and a V.M.D. in veterinary medicine from the University of Pennsylvania. Her research interests have revolved around memory storage in neurons and neuronal networks. Her professional career began at the not-for-profit Environmental Research Institute of Michigan, where she began developing artificial neural networks for pattern recognition. Unsatisfied with the non-biophysical learning rules used in ANN, she began investigating the biophysical and biochemical mechanisms of long term memory storage in neurons using computational and electrophysiological techniques. In 1996 Blackwell moved to the Krasnow Institute at George Mason University, where she is now a professor in the Molecular Neuroscience Department. Her research presently focuses on calcium dynamics and signaling pathways underlying memory storage, synaptic plasticity, and the ability of hippocampal and striatal neurons to discriminate spatio-temporal patterns of in
puts. Because of the importance of dynamics, she has developed the software tools Chemesis and NeuroRD for large scale dynamical modeling of the signaling pathways in neurons underlying memory storage. She continues to integrate computational modeling with experiments, both in her own lab and through collaborations.
The ability of neurons to respond differentially to specific temporal and spatial patterns of stimulation underlies the storage of memory and information in neural circuits. Plasticity of synaptic and ionic channels is thought to underlie learning and memory, but the complex interactions among the large numbers of molecules preclude the simple conceptual models traditionally used to explain the molecular processes underlying neuronal plasticity. Similarly, though network analysis of the data produced by genomics and proteomics studies has revealed many principles of network function, subcellular spatial location of molecules and the kinetics of molecular interactions can change network activity. Therefore, quantitative kinetic models are required for in-depth understanding of the spatio-temporal dynamics of signaling pathways underlying plasticity. Intracellular signaling pathways are activated by spatially specific synaptic activation, and also through activation of metabotropic receptors, by neuromodulators such as dopamine or norepinephrine. It is particularly challenging to understand how biochemical processes mediated by spatially diffuse neuromodulators are activated in a temporally specific and spatially restricted fashion.
One brain region where the importance of neuromodulation has been long acknowledged is the striatum. Dopamine in the striatum is responsible for normal habit learning as well as pathologies such as Parkinson’s disease, and addiction. Both neuromodulators and calcium influx through synaptic and ion channels activate intracellular signal transduction pathways which are critical for information storage. I will describe a modeling approach to understand the spatial and temporal properties of signaling pathways underlying synaptic plasticity in the striatum. Novel software was created for computationally efficient modeling of signaling pathways in neurons. Models created with this software are being used to investigate spatial and temporal aspects of dopamine signaling in the striatum. Ultimately, interfacing these signaling pathway models with models of electrical activity will be useful for deriving more plausible and generalizable synaptic plasticity rules to enhance neuronal network models used to understand brain function.