Lignin polymers provide natural rigidity to plant cell walls by forming complex molecular networks with polysaccharides such as cellulose and hemicellulose. This evolved strategy equips plants with recalcitrance to biological and chemical degradation. While naturally beneficial, recalcitrance complicates the use of inedible plant materials as feedstocks for biofuel production. Genetically modifying lignin biosynthesis is an effective way to generate varieties of bioenergy crops with reduced recalcitrance, but certain lignin-modified plants display undesirable phenotypes and/or unexplained effects on lignin composition, suggesting that the process and regulation of lignin biosynthesis is not fully understood. Given the intrinsic complexities of metabolic pathways in plants and the technical hurdles in understanding them purely with experimental methods, the objective of this dissertation is to develop novel computational tools combining static, constraint-based, and dynamic, kinetics-based modeling approaches for a systematic analysis of lignin biosynthesis in wild-type and genetically engineered plants. Pathway models are constructed and analyzed, yielding insights that are difficult to obtain with traditional molecular and biochemical approaches and allowing the formulation of new, testable hypotheses with respect to pathway regulation. These model-based insights, once they are verified experimentally, will form a solid foundation for the rational design of genetic modification strategies towards the generation of lignin-modified crops with reduced recalcitrance. More generically, the methods developed in this dissertation are likely to have wide applicability in similar studies of complex, ill-characterized pathways where regulation occurring at the metabolic level is not entirely known.
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Computational modeling reveals new control mechanisms for lignin biosynthesis
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