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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.

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