KinMod database: a tool for investigating metabolic regulation.

Current kinetic models struggle to accurately simulate cell metabolism due to limited understanding of regulatory mechanisms. Metabolic regulation involves enzyme activity control, particularly through small molecule regulation networks (SMRN), which allow cells to adapt to environmental changes. However, SMRN remains underexplored compared to metabolic networks. This gap motivated the development of a comprehensive database to address data sparsity and lack of standardized frameworks. Prior research has shown the importance of integrating multi-omics data for model accuracy. No prior work had resolved how to systematically organize and represent SMRN data. The absence of a unified database hindered progress in kinetic modeling. This paper introduces a resource to bridge this gap by compiling extensive metabolic and regulatory data.

The study aims to address the limitations in simulating cell metabolism by creating a structured database for metabolic regulation data. The goal is to provide a centralized repository for kinetic and regulatory information. This database supports the integration of in vitro data with protein activity. It also aims to highlight missing experimental measurements in SMRN. The study seeks to facilitate machine learning approaches for parameter estimation. The database is designed to enable cross-species comparisons of metabolic regulation. By organizing data hierarchically, the study promotes flexible data exploration. This approach encourages the development of novel computational tools for modeling metabolism.

The KinMod database was constructed using a hierarchical data structure to organize kinetic and regulatory data. The database includes over 2 million data points from 9814 organisms. It integrates information from in vitro experiments with protein activity data. The database emphasizes small molecule regulation network (SMRN) interactions. A key feature is the linkage of kinetic parameters with missing experimental measurements. The database provides a standardized framework for representing regulatory data. It supports machine learning by offering a homogeneous list of linked omics data. The database structure allows for within- and cross-species metabolic relationship investigations.

The KinMod database contains more than 2 million data points on metabolism and regulatory networks. It provides detailed kinetic parameters and identifies missing experimental measurements. The database includes hierarchical relationships between in vitro data and proteins. It supports machine learning by linking multi-omics data for parameter estimation. Six analyses were conducted to visualize associations between data classes. These analyses demonstrated the database's utility in retrieving and comparing metabolic data. The database enables the identification of gaps in experimental data for SMRN. It offers a unique framework for evaluating and comparing species' metabolic regulation.

The KinMod database offers a structured framework for investigating metabolic regulation. It provides a hierarchical structure for organizing kinetic and regulatory data. The database highlights missing experimental measurements in SMRN. It supports machine learning approaches for parameter estimation and model construction. The database allows flexible exploration of within- and cross-species metabolic relationships. It encourages the development of novel computational tools for modeling. The database enables biologists and engineers to compare species' metabolic regulation. It serves as a valuable resource for identifying gaps in experimental data.