Biology subjects

Larsen, N. J.

Publications and source records attributed to Larsen, N. J..

2 recordsLinked to original sources

bioRxiv2026

Design and development of online pressure sensing for microbial batch cultivation

Gas production and consumption is a direct consequence of microbial activity in environmental and industrial settings. In closed batch cultivations, headspace pressure changes therefore give valuable insights into the microbial metabolism. For laboratory scale anaerobic batch cultivations, manual manometer measurements are routinely applied, as a simple and robust method, but it is labour intensive, causes disturbances in the headspace gas and temperature, leading to suboptimal growth, inhibition and noisy data. We built and tested an automated online pressure sensor for closed batch cultivations. It is designed for microbial cultivation and integrates with sterile and anaerobic cultivation workflows. The system uses an absolute pressure sensor (0-30 bar) mounted on a custom designed PCB, with a gas-tight needle mount. An ESP32 microcontroller logs pressure and temperature locally and generates a Wi-Fi access point for real-time visualization and direct CSV download through a local homepage. We detail hardware and software design decisions, assembly, and validation including long-term stability. Case studies demonstrate the applicability for: a multiphasic biogas kinetics during anaerobic digestion, capturing gas uptake dynamics and metabolic shifts during syngas fermentations and co-feeding experiments, and long-term robustness in a multi-year monitoring of a compressed-air system. More than 130 individual sensors have been deployed over 3 years in laboratories, at various academic and industrial settings. The platform provides reproducible, high-resolution pressure measurements that enable calculation of gas formation/consumption rates and improve experimental throughput without disturbing cultures. Design files, firmware, and example analysis scripts are openly available to support adoption and further development.

bioengineering↗

bioRxiv2026

Using protein language models for pangenome construction

Current pangenome construction methods rely largely on nucleotide or protein sequence alignment, limiting their ability to detect remote orthologs and semantic relations. We introduce a novel method that leverages protein language model embeddings to capture functional and semantic relationships beyond sequence similarity. Our approach employs approximate nearest-neighbor search coupled with a clustering step utilizing HDBSCAN, DBSCAN, or weighted single-linkage clustering with multiple similarity thresholds. The method utilizes GPU acceleration, dynamic batching, and ONNX optimization to scale approximately linearly with the number of proteins, enabling the analysis of datasets containing millions of proteins. We evaluated our approach on a randomly sampled subset of OrthoDB and the CAFA5 dataset, benchmarking it against SCARAP. SCARAP is a recently published tool with similar performance to a variety of other common tools for computing pangenomics. Our benchmarking demonstrates that our method produces more specific clusters than SCARAP across both datasets. SCARAP excelled in term consistency within clusters on the OrthoDB dataset, where labels are inferred with sequence alignment (using MMseqs2). Both methods face a significant degradation in term consistency when transitioning to the experimentally validated CAFA5 dataset, ultimately resulting in similar term consistency scores for both approaches. Crucially, our approach yields superior cluster quality on both datasets and significantly outperforms SCARAP across all metrics of functional consistency and coherence on the experimental CAFA5 dataset. Finally, we demonstrate the methods scalability and utility by characterizing the pangenome of 1,034 Streptomyces genomes. The pipeline is available for use at our GitHub: https://github.com/jakob949/pan_genome

bioinformatics↗