Model-Driven Analysis Combining Probabilistic and Mechanistic Approaches to Generate Predictive Models Clinically Applicable to COPD

Skeletal muscle dysfunction is a systemic effect in one-third of patients with Chronic Obstructive Pulmonary Disease (COPD). It has a multifactorial nature generating increased
healthcare burden and poor patient prognosis. Cell hypoxia, high reactive-oxygen-species (ROS) production are recognized traits of the phenomenon.
The current study describes a novel constraint-based modeling method that combines mechanistic and probabilistic approaches to decipher the role of ROS on the activity state
of the gene regulatory network governing energy metabolism of skeletal muscle in COPD patients.
Our predictions indicate an abnormal activity of energy-associated pathways such as TCA cycle, electron transport chain or creatine-kinase and abnormal cytokines regulation,
suggesting defective tissue remodeling. This computational analysis also identified key regulators genes of the energy metabolism in COPD, which can be used as potential
therapeutic targets.
The model outcomes enhance knowledge on skeletal muscle dysfunction in COPD patients helping to design new avenues for cost-effective preventive therapies. The current research
overcomes limitations of previous computational approaches showing high potential forexploring relevant characteristics and effects of interventions on complex biological networks in other multifactorial diseases such as diabetes or cancer.