Pathology Dynamics

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Research

What are we at the Laboratory of Pathology Dynamics doing?

We are working vigorously on methods that allow the identification and examination of pathology dynamics, including their predicted responses to potential treatments. There is no one specific tool that constitutes solving for pathology dynamics. We utilize a combination of developed and traditional tools to accomplish this task. These techniques have goals which include examining relationships, interactions, feedback, and overall system stability. Additionally, a wide variety of tools are used to gather, extract, organize and quantify the data before the analysis even begins. Click the links to read our results and on-going work with the pathologies of ALS, Alzheimer’s Disease, and Chronic Myeloid Leukemia. If you have questions, have a new tool you’d like for us to try or are looking to access our SOD1 G93A and Tg2576 databases, please contact us by following the link below.

Get In Touch!

 

Developed Tools

  • Dynamic meta-analysis (DMA):   DMA is similar to traditional meta-analysis except that is explicitly includes time and implicitly includes interactions.  In short, the algebraic meta-regression equation is converted into a set of time-varying differential equation(s).  For more information, see Mitchell and Lee, 2012, Intech).
  • Relational modeling:  form of computational modeling that solely utilizes relationships and correlations to aggregate and simulate detailed, unknown or complicated mechanisms at a system-level. See Mitchell and Lee, 2009, J Neurotrauma.
  • Relational analysis:  an aggregation of correlative and complex systems techniques that uses multiple inter-relationships of system components to make predictions of system-level behavior.  In particular, “landscapes” of component relationships are compared over varying conditions or at different time points. See Mitchell and Lee, 2007, N Neural Engineering for process details and Mitchell and Lee, 2009, J Neurotrauma for pathology example.
  • Conceptual modeling: form of computational modeling that uses concepts and theories to build model mechanisms. For example of a conceptual model, see Shapiro and Lee, 2007, J Neurophysiology.
  • Viewpoint aggregation: an analytical technique that combines different views of a pathological system, such as conceptual insights, experimental observations, refined/detailed mechanisms, and clinical treatment outcomes to produce a comprehensive, system-level view. For details see Mitchell, 2009, Georgia Institute of Technology.

Traditional Tools

  • Multi-variate statistics: use of cross-correlation analysis, cluster analysis, hierarchial analysis, factor analysis, etc. to identify key trends and quanitfy system dimensionality.
  • Mathematical and engineering dynamics: used to determine stability of the system, identify instability type, determine system gains and feedback control.
  • Mechanistic modeling: used to examine mechanistic dynamics at the system component level.
  • Databases and ontologies: used to organize published data, organize extracted and quantified data points as well as to provide hiearchial structure for browsing, querying, and searching.
  • Bioinformatics and literature mining: used to comb through thousands of published articles to find the quantifiable data needed to examine the pathological system and its dynamics. We are always interested in trying new tools to help us collect and quanitfy data more efficiently.

 

Recent News

  • Dr. Mitchell Recognized as Joint Faculty Outstanding Undergraduate Research Mentor April 30, 2022
  • Lab Alumni Mira Mutnick Awarded Prestigious Goldwater Scholarship April 30, 2022
  • Georgia Tech Names PhD Student Raghav Tandon Online TA of the Year April 30, 2022
  • Undergraduate Kevin McCoy Wins Sigma Xi Best Undergraduate Research Award and Named Outstanding Senior April 20, 2022
  • Pathology Dynamics Lab Gathers to Celebrate October 22, 2021

Archive

  • April 2022 (4)
  • October 2021 (1)
  • September 2021 (1)
  • March 2021 (2)
  • February 2021 (1)
  • January 2021 (1)
  • April 2020 (1)
  • February 2020 (3)
  • August 2019 (1)
  • June 2019 (3)
  • May 2019 (2)
  • April 2019 (2)
  • February 2019 (2)
  • January 2019 (1)
  • November 2018 (2)
  • September 2018 (1)
  • July 2018 (3)

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