Background: Modern network science has been used to reveal new and often fundamental aspects of brain network organization in physiological as well as pathological conditions. As a consequence, these discoveries, which relate to network hierarchy, hubs and network interactions, begun to change the paradigms of neurodegenerative disorders. We therefore explored the use of thermodynamics for protein-protein network interactions in Alzheimer disease (AD), Parkinson disease (PD), multiple sclerosis (MS), traumatic brain injury and epilepsy.
Methods: To assess the validity of using network interactions in neurological disease, we investigated the relationship between network thermodynamics and molecular systems biology for these neurological disorders. In order to uncover whether there was a correlation between network organization and biological outcomes, we used publicly available RNA transcription data from individual patients with these neurological conditions, and correlated these molecular profiles with their respective individual disability scores.
Results: We found a linear correlation (Pearson correlation of -0.828) between disease disability (a clinically validated measurement of a person's functional status), and Gibbs free energy (a thermodynamic measure of protein-protein interactions). In other words, we found an inverse relationship between disease entropy and thermodynamic energy.
Interpretation: Because a larger degree of disability correlated with a larger negative drop in Gibbs free energy in a linear, disability-dependent fashion, it could be presumed that the progression of neuropathology such as is seen in Alzheimer Disease, could potentially be prevented by therapeutically correcting the changes Gibbs free energy.
Gibbs free energy of protein-protein interactions correlates with ATP production in cancer cells
