COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.

Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogeneous biological datasets, which can be challenging without efficient tools. We developed Constraint-based Optimization of Metabolic Objectives (COMO), a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases and disease data to aid drug discovery. COMO can be installed as a Docker Image or with Conda and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for the integration of bulk and single-cell RNA-seq, microarrays and proteomics outputs to develop context-specific metabolic models. Using public databases, open-source solutions for model construction and a streamlined approach for predicting repurposable drugs, COMO enables researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease where metabolic inhibition is relevant. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies. The source code of the COMO pipeline is available at https://github.com/HelikarLab/COMO. The Docker image can be pulled at https://github.com/HelikarLab/COMO/pkgs/container/como.

A low-cost open-source handheld LiDAR-based automated understory timber stand surveying device.

Timber stand inventories are critical for making management decisions in both commercial and conservation related forestry applications. When taking these inventories, the most critical characteristics to record are diameter at breast height (DBH), stand density, tree height and tree species. As discovered in prior research, a relatively inexpensive device, which hereby will be referred to as AutoTSI (short for Automatic Timber Stand Inventory), can be assembled and deployed in the field to take accurate measurements of diameter at breast height (DBH) for timber stand sample plots in a short amount of time compared to the traditional method of manually measuring these trees. The specified range and angle of resolution of the sensor allows for estimation of stand density and basal area. Each element of both the hardware and software is modular by design allowing the user to customize according to whatever sensors may be readily available to the user. In silviculture research, methodical collection, and analysis of geometric measurements of trees is essential for developing land management plans that optimize biomass production, biodiversity, and other desired characteristics in a forest environment making this device useful to both tree farmers and researchers.