Cellular metabolism is predicted accurately at the genome-scale using constraint based modeling. Such predictions typically rely on optimizing an assumed cellular objective function, which takes the form of a stoichiometrically-determined reaction such as biomass synthesis, ATP yield, or reactive oxygen species formation. While these objective functions are typically constructed by hand, several algorithms have been developed to estimate them from data . Generally, two approaches for data-driven objective estimation exist: estimating objective weights for existing reactions, and de novo generation of a new objective reaction . The latter approach can discover objectives that are not describable as a linear combination of existing reactions. However, it requires solving a nonconvex optimization problem and its scalability to genome-scale modelshas not been demonstrated. Here, we develop a new algorithm that extends existing approaches for de novo objective generation and solve it using the alternating direction method of multipliers (ADMM) . We demonstrate our approach on a genome-scale model and show that it identifies de novo objectives from measured fluxes with tunable sparsity .
Optimization models have been used to predict cellular behavior for over 25 years. The constraints in these models are reconstructed from genome annotations, measured macro-molecular composition of cells, and measuring the cell's growth rate and metabolism in different conditions. The cellular goal (the optimization problem the cell is trying to solve) can be challenging to define experimentally for URLanisms, 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 certain cellular functions are not modeled; hence, there is a need to extend automatically the model complexity by learning new chemical reactions. However, it leads to nonconvex optimization problems, and existing tools cannot scale to realistically large metabolic models. Thus, 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 an approach to estimate constraint reactions from data that can scale to realistically large metabolic models. Previous tools have been used on problems having less than 70 biochemical reactions and 60 metabolites, which limits real-life-size applications. We perform extensive experiments using 70 large-scale metabolic network models for URLanisms (including bacteria, yeasts, and mammals) and show that our algorithm can recover cellular constraint reactions .
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Cellular metabolism is predicted accurately at the genome-scale using constraint based modeling. Such predictions typically rely on optimizing an assumed cellular objective function, which takes the form of a stoichiometrically-determined reaction such as biomass synthesis, ATP yield, or reactive oxygen species formation. While these objective functions are typically constructed by hand, several algorithms have been developed to estimate them from data . Generally, two approaches for data-driven objective estimation exist: estimating objective weights for existing reactions, and de novo generation of a new objective reaction
Optimization models have been used to predict cellular behavior for over 25 years. The constraints in these models are reconstructed from genome annotations, measured macro-molecular composition of cells, and measuring the cell's growth rate and metabolism in different conditions. The cellular goal (the optimization problem the cell is trying to solve) can be challenging to define experimentally for URLanisms, 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
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can discover objectives that are not describable as a linear combination of existing
is important because certain cellular functions are not modeled; hence, there is a need to extend automatically the model complexity by learning new chemical
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requires solving a nonconvex optimization problem and its scalability to genome-scale modelshas not been demonstrated.
leads to nonconvex optimization problems, and existing tools cannot scale to realistically large metabolic models. Thus, 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.
R
a new algorithm that extends existing approaches for de novo objective generation and solve it using the alternating direction method of multipliers (ADMM) . We demonstrate our approach on a genome-scale model
an approach to estimate constraint reactions from data that can scale to realistically large metabolic models. Previous tools have been used on problems having less than 70 biochemical reactions and 60 metabolites, which limits real-life-size applications. We perform extensive experiments using 70 large-scale metabolic network models for URLanisms (including bacteria, yeasts, and mammals)
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it identifies de novo objectives from measured fluxes with tunable sparsity
our algorithm can recover cellular constraint reactions