Mathematical modeling of monolignol biosynthesis in Populus xylem

Abstract

Recalcitrance of lignocellulosic biomass to sugar release is a central issue in the production of biofuel as an economically viable energy source. Among all contributing factors, variations in lignin content and its syringyl–guaiacyl monomer composition have been directly linked with the yield of fermentable sugars. While recent advances in genomics and metabolite profiling have significantly broadened our understanding of lignin biosynthesis, its regulation at the pathway level is yet poorly understood. During the past decade, computational and mathematical methods of systems biology have become effective tools for deciphering the structure and regulation of complex metabolic networks. As increasing amounts of data from various organizational levels are being published, the application of these methods to studying lignin biosynthesis appears to be very beneficial for the future development of genetically engineered crops with reduced recalcitrance. Here, we use techniques from flux balance analysis and nonlinear dynamic modeling to construct a mathematical model of monolignol biosynthesis in Populus xylem. Various types of experimental data from the literature are used to identify the statistically most significant parameters and to estimate their values through an ensemble approach. The thus generated ensemble of models yields results that are quantitatively consistent with several transgenic experiments, including two experiments not used in the model construction. Additional model results not only reveal probable substrate saturation at steps leading to the synthesis of sinapyl alcohol, but also suggest that the ratio of syringyl to guaiacyl monomers might not be affected by genetic modulations prior to the reactions involving coniferaldehyde. This latter model prediction is directly supported by data from transgenic experiments. Finally, we demonstrate the applicability of the model in metabolic engineering, where the pathway is to be optimized toward a higher yield of xylose through modification of the relative amounts of the two major monolignols. The results generated by our preliminary model of in vivo lignin biosynthesis are encouraging and demonstrate that mathematical modeling is poised to become an effective and predictive complement to traditional biotechnological and transgenic approaches, not just in microorganisms but also in plants.