Generative AI for Knowledge Mining of Synthetic Biology and Bioprocess Engineering Literature

The development of microbial cell factories faces challenges due to the complexity of biological systems and the labor-intensive nature of experimental workflows. This chapter explores how generative artificial intelligence (GenAI) and large language models (LLMs) can bridge historical data, strain design, and experimentation by leveraging vast amounts of online publications in synthetic biology and bioprocess engineering. First, two knowledge graphing workflows are introduced: NEKO (Network for Knowledge Organization), a lightweight tool, and Graph-RAG (Retrieval-Augmented Generation), an advanced system. These tools enable researchers to map causal relationships – such as gene engineering methods and cell cultivation conditions – across journal articles, facilitating knowledge visualization, domain-specific queries, and trustworthy information retrieval. Second, a protocol for LLM-driven bioprocess data extraction is presented, which systematically converts unstructured text information into machine learning (ML)-ready datasets. This includes key bioprocess parameters such as substrates, products, pathway engineering strategies, and cultivation conditions. Third, by integrating GenAI and LLMs with RAG, significant advancements in microbial production prediction, strain design acceleration, and bioprocess development optimization can be enabled, ultimately enhancing researchers' ability to acquire and apply critical information efficiently.