title: Probabilistic strain optimization under constraint uncertainty.
abstract: BACKGROUND
  An important step in strain optimization is to identify reactions whose activities should be modified to achieve the desired cellular objective. Preferably, these reactions are identified systematically, as the number of possible combinations of reaction modifications could be very large. Over the last several years, a number of computational methods have been described for identifying combinations of reaction modifications. However, none of these methods explicitly address uncertainties in implementing the reaction activity modifications. In this work, we model the uncertainties as probability distributions in the flux carrying capacities of reactions. Based on this model, we develop an optimization method that identifies reactions for flux capacity modifications to predict outcomes with high statistical likelihood.
  
  
  RESULTS
  We compare three optimization methods that select an intervention set comprising up- or down-regulation of reaction flux capacity: CCOpt (Chance constrained optimization), DetOpt (Deterministic optimization), and MCOpt (Monte Carlo-based optimization). We evaluate the methods using a Monte Carlo simulation-based method, MCEval (Monte Carlo Evaluations). We present two case studies analyzing a CHO cell and an adipocyte model. The flux capacity distributions required for our methods were estimated from maximal reaction velocities or elementary mode analysis. The intervention set selected by CCOpt consistently outperforms the intervention set selected by DetOpt in terms of tolerance to flux capacity variations. MCEval shows that the optimal flux predicted based on the CCOpt intervention set is more likely to be obtained, in a probabilistic sense, than the flux predicted by DetOpt. The intervention sets identified by CCOpt and MCOpt were similar; however, the exhaustive sampling required by MCOpt incurred significantly greater computational cost.
  
  
  CONCLUSIONS
  Maximizing tolerance to variable engineering outcomes (in modifying enzyme activities) can identify intervention sets that statistically improve the desired cellular objective.
authors.affiliation: Department of Computer Science, Tufts University, Medford, MA, USA.
authors.forename: Mona
authors.initials: M
authors.lastname: Yousofshahi
authors.forename: Michael
authors.initials: M
authors.lastname: Orshansky
authors.forename: Kyongbum
authors.initials: K
authors.lastname: Lee
authors.forename: Soha
authors.initials: S
authors.lastname: Hassoun
mesh_terms: D017667:Adipocytes / Q000378:metabolism; D000818:Animals; D016466:CHO Cells; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006224:Cricetinae; D003412:Cricetulus; D004798:Enzymes / Q000378:metabolism*; D060847:Metabolic Engineering / Q000379:methods*; D008954:Models, Biological; D009010:Monte Carlo Method; D013269:Stochastic Processes
chemical_list: D004798:Enzymes
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