Roke Biotechnologies has developed a systems engineering protein manufacturing platform in E. coli that enables advanced capabilities in protein expression and purification. By decoupling growth from production, the platform delivers standardized, scalable bioprocesses and an autolysis strain that works without tuning across diverse workflows, from high-throughput screening to in-bioreactor purification. Freed from growth-associated boundary conditions, we have also developed advanced tools that drive robust expression of hard-to-express proteins, establishing a versatile foundation for next-generation biomanufacturing

Harnessing and standardising metadata from public repositories (like NCBI’s GEO, CellxGene, ArrayExpress etc) can help unlock its potential for a range of research questions. We have developed an automated system based on Retrieval Augmented Generation (RAG) and Large Language Models (LLM) to quickly and accurately (> 95% on average) standardize biomedical terms from real world paragraphs to standard ontologies. Performance of this pipeline is demonstrated on manually curated ground truths from Strand’s scRNA-seq portal and provides ~3x improvements in turn around time compared to manual curation. The LLM pipeline also provides reliable metrics of quality to enable curators to prioritize manual followup checks and correction.

High-throughput biological assays frequently generate extensive feature spaces, posing challenges for downstream analysis, assay cost, and interpretability. Here, we present a versatile framework that employs genetic algorithms (GAs) for constrained feature selection in bioinformatics workflows. This approach evolves compact feature subsets optimized for both predictive performance and practical deployment. We demonstrate how hybridizing GAs with neural network–based predictive modeling can be used to generate robust predictive algorithms that can be deployed within laboratory assay constraints. Key implementation considerations and strategies for preventing overtraining are discussed. This work underscores the utility of GAs as generic optimizers in bioinformatics and provides best practices for their integration into feature‐selection tasks where technical and operational constraints are paramount.