Jacob D Davis1, Eberhard O Voit1. 1. Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
Abstract
MOTIVATION: The assessment of graphs through crisp numerical metrics has long been a hallmark of biological network analysis. However, typical graph metrics ignore regulatory signals that are crucially important for optimal pathway operation, for instance, in biochemical or metabolic studies. Here we introduce adjusted metrics that are applicable to both static networks and dynamic systems. RESULTS: The metrics permit quantitative characterizations of the importance of regulation in biochemical pathway systems, including systems designed for applications in synthetic biology or metabolic engineering. They may also become criteria for effective model reduction. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://gitlab.com/tienbien44/metrics-bsa.
MOTIVATION: The assessment of graphs through crisp numerical metrics has long been a hallmark of biological network analysis. However, typical graph metrics ignore regulatory signals that are crucially important for optimal pathway operation, for instance, in biochemical or metabolic studies. Here we introduce adjusted metrics that are applicable to both static networks and dynamic systems. RESULTS: The metrics permit quantitative characterizations of the importance of regulation in biochemical pathway systems, including systems designed for applications in synthetic biology or metabolic engineering. They may also become criteria for effective model reduction. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://gitlab.com/tienbien44/metrics-bsa.