The scientific objective of this project is to develop a comprehensive quantitative framework of ALS from which initiation/progression can be examined and combination treatments be quantitatively generated, predicted, and prioritized. We hypothesize that there exists a pathophysiological spectrum of interactions that can only be viewed when physiology and pathology are examined together, and it is this set of interactions that holds the key to unlocking both our understanding of ALS and especially its treatment with synergistic, interaction-exploiting combination treatments. Our approach is to integrate the wealth of information derived from the ALS and related physiological literature, in conjunction with traditional experimental and clinical observations, to develop a comprehensive framework of ALS-relevant motoneuron pathophysiology using our innovative """"""""dynamic meta-analysis"""""""" technique, referred to as relational modeling. The significance of this proposal is that it will generate new and exciting ALS insight and treatment strategies in a high-throughput manner not possible with traditional experimentation alone.
The research aims consist of developing a quantitative framework of ALS using both the literature and clinical data analysis (aim 1);assess the effects of riluzole on in vivo electrophysiology (aim 2);and generating and quantitatively assessing combination treatment strategies within the developed framework (aim 3). The primary scientific outcomes of this proposal include a comprehensive model of ALS motoneuron pathophysiology, mechanistic insight into the actions of riluzole and a prioritized list of potential therapeutic strategies. The career development outcomes of this proposal include hands-on training in in vivo neuroelectrophysiology, clinical data analysis, and their integration with theory to make clinically relevant predictions.
In this project, we use an innovative method, our form of """"""""dynamic meta-analysis"""""""" referred to as relational modeling that integrates theory, experimental, and clinical data analysis to create a quantitative framework of ALS from which clinically relevant predictions can be made regarding pathology progression. Using this developed framework, we will generate and quantitatively assess novel ALS treatments, both singly and in combination. The scientific outcome of this proposal is a prioritized list of ALS therapeutic strategies, with the career development outcomes being hands-on training in traditional neuroelectrophysiology and clinical neurodegenerative assessment.
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