Computational integration of multi-pathway disease cascades remains a fundamental challenge in systems biology. We address this through bio-inspired algorithms that model hierarchical pathway interactions, demonstrated in amyotrophic lateral sclerosis (ALS). We developed and simulated a multi-pathway integration framework using 369 computationally reconstructed patient profiles derived from two published ALS cohorts (Lu et al. 2015: n=219; Verde et al. 2019: n=150), integrating three biological pathways: VCP-nuclear pore-TDP-43 cascade (5 features), V1 interneuron circuit disruption (4 features), and mitochondrial dysfunction (8 features). Target variables were decoupled from features via independent patient-level noise, biomarker measurement variability was modelled at realistic clinical assay coefficients of variation (~18%), and edge cases were isolated prior to scaler fitting. Random Forest achieved ROC-AUC: 0.539 on the test set following leakage remediation. Conformal prediction yielded 93.2% empirical coverage (≥90% target). V1 timing analysis revealed 98.6% of profiles in post-optimal intervention windows (mean V1 loss 28.0%). Bayesian pathway weighting quantified: VCP dominant (57.9%), V1 interneuron secondary (34.0%), mitochondrial downstream (6.9%). Four mitochondrial phenotypes were identified, with Energy-Depleted patients showing highest treatment response (81%). Prospective validation on independently collected raw patient data is the necessary next step toward clinical deployment.