Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods

Key Points Our understanding of genotype–phenotype relationships has classically been qualitative, but recent advances are enabling us to overcome conceptual and technological barriers, leading to a quantitative understanding of these relationships. Within the framework of constraint-based modelling, the generation of quantitative relationships is facilitated by the realization that cell phenotypes are limited by physical and genetic constraints. Physical laws, such as mass conservation and thermodynamics, constrain the possible metabolic and biosynthetic transformations that can occur in nature, and genetics specify which sets of biochemical reactions have been selected through evolution. Genome sequencing and annotation have allowed the comprehensive reconstruction of microbial metabolic networks, and constraint-based reconstruction and analysis (COBRA) modelling has emerged as a set of valuable tools that allows detailed analysis of the biochemical mechanisms underlying the metabolic genotype–phenotype relationship. Network-based pathway analysis tools, such as elementary flux modes and extreme pathways analysis, delineate pathways that can perform a given metabolic function in an organism of interest. Although these methods have been difficult to use in larger metabolic networks, simplifications are now beginning to allow their use on genome-scale models. As not all pathways are used in a cell at a given time, optimization algorithms are routinely used to identify pathway use that best reflects the in vivo metabolic state. Flux balance analysis, which uses linear programing to optimize a mathematical description of the cellular objective, has been widely used to understand microbial physiology and the effects of environmental and genetic perturbations. The ability of COBRA methods to model genetic perturbations has allowed such methods to help predict antimicrobial targets and to aid in the design of strains for chemical production. Reconstructed metabolic networks are often incomplete and can have a small fraction of incorrect reactions therein. However, the integration of phenotypic screens with model simulations can provide a systematic approach to refine the models and discover new metabolic functions in an organism. COBRA methods are extending beyond metabolism, and approaches are beginning to incorporate transcription regulation, either implicitly, by constraining models with multiple 'omic data types, or explicitly, with detailed descriptions of