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  .
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 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 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 URLanisms (including bacteria, yeasts, and mammals) and show that our algorithm can recover cellular constraint reactions , even when some measurements are missing .
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Optimization
Optimization-based
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reconstructed
derived
A
by
A
that
R
define
derive
R
certain cellular functions are not modeled; hence,
often the known/observed biochemical reactions are not enough to explain observations, and hence
R
it
this
R
Thus
Hence
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an approach to estimate
the first approach to estimating
R
70
75
R
70
75
A
, even when some measurements are missing