The biological transformation of industry introduces new demands for data management in production systems. Biointelligent value creation (BVC) systems integrate biological, technical, and digital components within decentralized, modular, and adaptive production networks. In these systems, data platforms are essential for enabling integration, coordination, and data-driven control across heterogeneous actors and domains. However, a systematic understanding of which data platforms exist, how they support biointelligent value creation, to what extent they fulfil their requirements, and what is currently lacking. This paper addresses this gap by conducting a systematic literature review of 40 peer-reviewed publications, identifying and categorizing key technological approaches, infrastructure concepts, and application domains. A functional framework is developed to assess platform capabilities across core, additional, and future-oriented requirements—from data integration and automation to semantic processing, predictive modeling, and real-time analytics. The findings show that while current platforms address selected requirements, no existing system fulfils the comprehensive demands of BVC. Major challenges include limited interoperability across biological and technical domains, insufficient support for non-expert users, and the lack of standardized, scalable architectures. The paper concludes with design implications and proposes the vision of a Biointelligence Metaverse – a modular, semantically enriched ecosystem combining data lake architectures, AI-based analytics, and IoT infrastructures to enable sustainable and collaborative value creation.

Symbolic regression (SR) is a powerful method for creating interpretable models, but it often lacks functions to incorporate prior knowledge and to support root cause analysis. To address this problem, we propose Structure-Template Regression (STR), a method to integrate known structural components into symbolic regression. This allows for the generation of models that not only fit data well, but also reflect the desired behavior of the underlying system. We evaluated STR on two different problems, a hydrodynamic fluid problem of interconnected communicating vessels and a production logistic use case with multiple assembly lines, and show that STR is capable of generating accurate, interpretable models that adhere to integrated structures. Furthermore, we demonstrate that STR facilitates root cause analysis by enabling the tracing of error pathways within the model structure.