A constructive bottom-up approach for the elaboration of metabolic networks: Case study of photosynthetic cyanobacteria Arthrospira spirulina platensis PCC 8005

Mathematical modeling has proven to be a highly effective tool for understanding microbial metabolism for which in-silico and experimental studies help to quantify intracellular mechanisms and pave the way for optimizing the production of molecules of interest. In that context, the development of metabolic networks turns out to be particularly interesting despite the challenges underlying their reconstruction. While the elaboration of genome-scale networks is computationally costly, small networks are often oversimplified and important biological mechanisms might be omitted, which limits their use in industrial applications. For this purpose, this study proposes a constructive bottom-up approach for the identification of metabolic networks of intermediate size, typically comprised of a couple of hundred reactions. It combines basic biological knowledge and a series of constraint-based methods in an iterative strategy, enabling the refinement of the network definition. The network is first validated using in-silico data, and subsequently refined using experimental data to enhance its biological relevance. Several case studies have been addressed to assess the efficiency of the methodology, and this paper focuses on the modeling of photosynthetic cyanobacteria Arthrospira sp. PCC 8005. The procedure is effective and provides promising results and metabolic analyses show consistent predictive capabilities of the network, in concordance with existing studies.