Modeling psychopathology as a complex dynamic system represents Borderline Personality Disorder (BPD) as a constellation of symptoms (e.g., nodes) that feedback and self-sustain each other shaping a network structure. Through in silico interventions, we simulated the evolution of the BPD system by manipulating: 1) the connectivity strength between nodes (i.e., vulnerability), 2) the external disturbances (i.e., stress) and 3) the predisposition of symptoms to manifest. Similarly, using network analysis we evaluated the effect of an in vivo group psychotherapy to detect the symptoms modified by the intervention. We found that a network with greater connectivity strength between nodes (more vulnerable) showed a higher number of activated symptoms than networks with less strength connectivity. We also found that increases in stress affected more vulnerable networks compared to less vulnerable ones, while decreases in stress revealed a hysteresis effect in the most strongly connected networks. The in silico intervention to symptom alleviation revealed the relevance of nodes related to difficulty in anger regulation, nodes which were also detected as impacted by the in vivo intervention. The complex systems methodology is an alternative to the common cause model with which research has approached the BPD phenomenon.