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_21: Pareto task inference (ParTI) computes a polytype that encloses a data set and determines the enrichment of features around the vertices (archetypes) of the polytype, which allows the identification of the task the archetype represents.
Currently, system-level understanding of living organisms requires the analysis of large-scale measurements originating from disparate biological processes operating at multiple length and time scales. Such measurements are collectively referred to as omics'', as they involve measuring the complete makeup of a given biological variable (e.g., proteomics attempts to measure the complete protein composition of a cell). Analysis of such omics measurements has shown that, despite the complexity inherent in living systems, relatively simple models are accurate enough for several biological studies. E.g., the gene expression profiles of various cell types can be described in terms of relatively few biological functions @cite_21 .