Biology has rapidly evolved into an information-rich science, with vast datasets encompassing genomics, epigenomics, phenomics, clinical data, and molecular pathways. Despite this wealth of information, the lack of engineering frameworks that can systematically integrate and analyze these data for predictive and explanatory purposes limits their utility in clinical and scientific applications. This keynote analyzes how the integration of information systems (IS) and information systems engineering (ISE) with life engineering can provide adequate concepts and techniques to address this challenge by leveraging socio-technical systems, architecture modeling, and design science. We present a multi-level framework that bridges molecular, pathway, organ, and population-level biological data using AI principles based on a sound integration of predictive and explainable models. We argue that life systems can be viewed as complex information-processing systems by adopting the principles of active inference and predictive processing. Furthermore, we explore how causal modeling and knowledge-guided machine learning can improve the interpretability of systems, ensuring that their predictions are scientifically valid and clinically actionable. The paper emphasizes the role of structured knowledge assets, such as curated biological databases with a precise conceptual modeling foundation, in enabling meaningful integration across biological scales. The proposed approach ultimately offers a blueprint for engineering life science information systems that produce robust and traceable insights, as well as promote interdisciplinary collaboration across bioinformatics, systems biology, and clinical genomics.