Learning and memory are cornerstones of human cognition, and cognitive defects are associated with many brain disorders. It has recently become possible to relate cognitive function to specific molecules. These molecules include the CREB family of transcription factors, which are essential for long-term (LT) synaptic plasticity and memory. Alterations in CREB signaling pathways are associated with diseases that impair cognition, such as Rubinstein-Taybi syndrome, neurofibromatosis, and Coffin-Lowry syndrome. Many of the molecular details of these signaling pathways are known. However, the ways in which these elements quantitatively account for normal and pathological cellular behavior are not well understood because the signaling cascades are embedded in a biochemical and genetic network that includes extensive cross talk and negative and positive feedback loops. To address this issue, the present proposal outlines computational studies that model and simulate CREB signaling pathways and their role in memory. Two well characterized neuronal correlates of memory will be modeled: long-term facilitation (LTF) and long-term potentiation (LTP). The proposed models will use differential equations to simulate molecular processes and will be constrained by empirical data.
Aim 1 will test the hypothesis that the dynamics for the induction and consolidation of LTF are governed by the dynamics of the PKA and ERK kinase cascades and by feedback loops within CREB regulated transcription. Simulations will examine the efficacy of training protocols and predict protocols that optimize learning.
Aim 2 will test the hypothesis that LTF and LTP share molecular mechanisms and dynamics. Simulations will identify control parameters, which may correspond to pharmacological control points for enhancing learning and cognition. Simulations also will explore LT plasticity impairment due to mutations that affect CREB activity, such as Rubinstein-Taybi syndrome. Finally, the models will be used to predict treatments for ameliorating CREB-related memory deficits and thereby help restore normal plasticity, learning and memory.
Learning and memory are essential to human cognition, and their disruptions contribute to several brain diseases including neurofibromatosis, Rubinstein-Taybi syndrome, and Coffin-Lowry syndrome. This project will advance the understanding of basic memory mechanisms, which will lead to better learning paradigms and help identify molecular targets for pharmacological treatments of brain disorders.
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|Smolen, Paul; Zhang, Yili; Byrne, John H (2016) The right time to learn: mechanisms and optimization of spaced learning. Nat Rev Neurosci 17:77-88|
|Zhang, Yili; Smolen, Paul; Alberini, Cristina M et al. (2016) Computational model of a positive BDNF feedback loop in hippocampal neurons following inhibitory avoidance training. Learn Mem 23:714-722|
|Zhou, Lian; Zhang, Yili; Liu, Rong-Yu et al. (2015) Rescue of impaired long-term facilitation at sensorimotor synapses of Aplysia following siRNA knockdown of CREB1. J Neurosci 35:1617-26|
|Smolen, Paul (2015) Modeling maintenance of long-term potentiation in clustered synapses: long-term memory without bistability. Neural Plast 2015:185410|
|Smolen, Paul; Baxter, Douglas A; Byrne, John H (2014) Simulations suggest pharmacological methods for rescuing long-term potentiation. J Theor Biol 360:243-250|
|Zhang, Yili; Smolen, Paul; Baxter, Douglas A et al. (2014) Computational analyses of synergism in small molecular network motifs. PLoS Comput Biol 10:e1003524|
|Zhou, Lian; Baxter, Douglas A; Byrne, John H (2014) Contribution of PKC to the maintenance of 5-HT-induced short-term facilitation at sensorimotor synapses of Aplysia. J Neurophysiol 112:1936-49|
|Liu, Rong-Yu; Zhang, Yili; Baxter, Douglas A et al. (2013) Deficit in long-term synaptic plasticity is rescued by a computationally predicted stimulus protocol. J Neurosci 33:6944-9|
|Zhang, Yili; Liu, Rong-Yu; Heberton, George A et al. (2012) Computational design of enhanced learning protocols. Nat Neurosci 15:294-7|
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