Person:
Mitchell, Cassie S.

Permanent Link
https://hdl.handle.net/1853/71527
Associated Organization(s)
ORCID
ArchiveSpace Name Record

Filters

2
1
2009 - 2009 1 2010 - 2017 1
1
1 1
1 1
2
2
Reset filters

Settings

Publication Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    After the Ice Bucket: Thawing Amyotrophic Lateral Sclerosis with Predictive Medicine
    ( 2017-10-16) Mitchell, Cassie S.
    The prolific 2014 ALS Association’s Ice Bucket Challenge commenced the world-wide dumping of ice water on the heads of courageous supporters to bring awareness and research funding to a lesser-known yet fatal neurodegenerative disease, Amyotrophic Lateral Sclerosis (ALS). The Laboratory for Pathology Dynamics at Georgia Tech, which proudly dunked GT President Peterson during the GT ALS Ice Bucket Challenge, has been actively developing data analytics, informatics, and complex systems-based techniques to expedite preclinical and clinical ALS research. In short, we are vigorously stitching together a comprehensive quilt of ALS using thousands of data sets collected from cells, transgenic animal models, and patients. We will discuss how predictive medicine is revealing ALS etiological underpinnings and diagnostic markers; identifying epidemiological ALS patient commonalities; precisely forecasting survival in highly heterogeneous ALS populations; identifying future disease dynamics-based combination therapies in preclinical ALS animal models; and optimizing current ALS life-prolonging interventions. Finally, we will also discuss the application of the lab’s techniques to other research topics.
  • Thumbnail Image
    Item
    Viewpoint aggregation via relational modeling and analysis: a new approach to systems physiology
    (Georgia Institute of Technology, 2009-04-09) Mitchell, Cassie S.
    The key to understanding any system, including physiologic and pathologic systems, is to obtain a truly comprehensive view of the system. The purpose of this dissertation was to develop foundational analytical and modeling tools, which would enable such a comprehensive view to be obtained of any physiological or pathological system by combining experimental, clinical, and theoretical viewpoints. Specifically, we focus on the development of analytical and modeling techniques capable of predicting and prioritizing the mechanisms, emergent dynamics, and underlying principles necessary in order to obtain a comprehensive system understanding. Since physiologic systems are inherently complex systems, our approach was to translate the philosophy of complex systems into a set of applied and quantitative methods, which focused on the relationships within the system that result in the system's emergent properties and behavior. The result was a set of developed techniques, referred to as relational modeling and analysis that utilize relationships as either a placeholder or bridging structure from which unknown aspects of the system can be effectively explored. These techniques were subsequently tested via the construction and analysis of models of five very different systems: synaptic neurotransmitter spillover, secondary spinal cord injury, physiological and pathological axonal transport, and amyotrophic lateral sclerosis and to analyze neurophysiological data of in vivo cat spinal motoneurons. Our relationship-based methodologies provide an equivalent means by which the different perspectives can be compared, contrasted, and aggregated into a truly comprehensive viewpoint that can drive research forward.