Pathology Dynamics

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ALS Dynamic Meta-Analysis

Summary:

This project is developing a computational and mathematical model of amyotrophic lateral sclerosis (ALS). This is the first multi-factorial in silico network model that fully recapitulates the ALS pathophysiology. Our model framework enables high throughput testing of combination treatments using mechanistic modulatory feedback analysis and stability as a criterion for successful treatment. Dynamic meta-analysis (DMA) is a method that provides temporal, dynamic, and biological relationships by using ordinary differential equations to account for changes with time. A system of ordinary differential equations can be constructed to model and describe the time-dependent behavior of seven highly interrelated pathophysiological categorical disturbances that have been proven to influence ALS disease onset and progression. Using this, our treatments can provide critical insight for experimental development and testing of translational ALS treatments and etiological hypotheses.

Team Leaders:

Sarah Bi

Poster:

 

 

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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