Computational Identification of Key Molecular Targets and Signaling Pathways in Asthma-Associated Inflammation

Abstract

Asthma is a highly heterogeneous inflammatory disorder of the airways shaped by diverse molecular, cellular, and environmental determinants. The computational identification of key molecular targets and signaling pathways in asthma-associated inflammation has become a major systems-level research challenge because single-target and single-pathway approaches cannot adequately capture the regulatory complexity of the disease. This paper examines the conceptual architecture, data infrastructure, inference methods, and governance structures required for robust computational target discovery in asthma. It situates target identification within a broader socio-technical framework in which multi-omics data, network biology, machine learning, and clinical phenotyping interact through modular analytical pipelines. The discussion emphasizes structural trade-offs between mechanistic interpretability and predictive performance, between data completeness and representational diversity, and between innovation speed and regulatory accountability. It further considers deployment sustainability, algorithmic fairness, and policy implications for translating computational predictions into reproducible biomedical knowledge. Rather than proposing a single computational method, the paper develops a system-level perspective on how computational infrastructures can be designed to prioritize verifiable molecular targets, reduce bias, and align prediction with clinical and public health needs. The analysis concludes that durable progress in asthma target identification depends not only on algorithmic sophistication but also on coherent governance of data, models, evidence standards, and translational incentives.