Restoring Homeostasis: Treating Amyotrophic Lateral Sclerosis by Resolving Dynamic Regulatory Instability.

Area of Science:

• Neuroscience
• Computational Biology
• Systems Biology

Background:

• Amyotrophic lateral sclerosis (ALS) presents a complex, multifactorial etiology challenging effective treatment development.
• Understanding the regulatory dynamics underlying disease progression is crucial for identifying therapeutic targets.

Purpose of the Study:

• To evaluate the impact of regulatory dynamics on ALS disease progression and treatment response.
• To develop and utilize computational models for simulating ALS pathology and testing therapeutic interventions.

Main Methods:

• Developed first-principles-based computational models of wild-type (WT) and SOD1-G93A mouse physiology using dynamic meta-analysis.
• Simulated key molecular mechanisms including apoptosis, energetics, inflammation, and oxidative stress.
• Validated models against temporal disease progression measures (rotarod, grip strength, body weight).

Main Results:

• Untreated SOD1-G93A ALS mouse models exhibited homeostatic instability characterized by oscillating dynamics, correlating with disease onset and progression.
• High feedback gain due to hypervigilant regulation was identified as a cause of instability.
• Combination treatments successfully stabilized ALS mouse dynamics towards WT homeostasis, with timing and effect size being critical factors.

Conclusions:

• A dynamics-based approach redefines therapeutic strategies for ALS, emphasizing homeostasis restoration.
• Precisely timed and stabilizing combination therapies offer a promising framework for treating multifactorial neurodegenerative diseases.
• This computational framework provides a novel perspective for advancing ALS research and treatment.