Optimization-based models have been used to predict cellular behavior for over 25 years. The constraints in these models are derived from genome annotations, measured macro-molecular composition of cells, and by measuring the cell's growth rate and metabolism in different conditions. The cellular goal (the optimization problem that the cell is trying to solve) can be challenging to derive experimentally for many organisms, including human or mammalian cells, which have complex metabolic capabilities and are not well understood. Existing approaches to learning goals from data include (a) estimating a linear objective function, or (b) estimating linear constraints that model complex biochemical reactions and constrain the cell's operation. The latter approach is important because often the known observed biochemical reactions are not enough to explain observations, and hence there is a need to extend automatically the model complexity by learning new chemical reactions. However, this leads to nonconvex optimization problems, and existing tools cannot scale to realistically large metabolic models. Hence, constraint estimation is still used sparingly despite its benefits for modeling cell metabolism, which is important for developing novel antimicrobials against pathogens, discovering cancer drug targets, and producing value-added chemicals. Here, we develop the first approach to estimating constraint reactions from data that can scale to realistically large metabolic models. Previous tools have been used on problems having less than 75 biochemical reactions and 60 metabolites, which limits real-life-size applications. We perform extensive experiments using 75 large-scale metabolic network models for different organisms (including bacteria, yeasts, and mammals) and show that our algorithm can recover cellular constraint reactions, even when some measurements are missing.
@cite_30: Constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework, which enables a mechanistic description of metabolic physiology. Recent successes in using this approach have implications for microbial evolution, interaction networks, genetic engineering and drug discovery.
@cite_29: The first global metabolic models that incorporate reactive oxygen species in E. coli reveal targets for potentiating the action of antibacterials.
@cite_7: The design and implementation of a high-yielding enzymatic route to 1,4-butanediol—a compound not known to be produced naturally—provides a compelling example of how metabolic engineering can be harnessed for the microbial conversion of carbohydrate feedstocks to desired small molecules.
@cite_39: The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40 are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled.
As of mid-2013, over 640 published studies used COBRA for experimental investigation in various domains of engineering and health sciences @cite_30 . Successful applications of COBRA include engineering microbes to produce commodity or valuable chemicals @cite_7 , developing novel antimicrobials against infectious disease @cite_29 , and discovering new drug targets against cancer @cite_39 .