Comparison of pathway analysis and constraint-based methods for cell factory design

Abstract

Background Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs. Results In this work, we perform an in-depth analysis of potential strategies that can be obtained with both methods, providing a critical comparison of performance, robustness, predicted phenotypes as well as strategy structure and size. To this end, we devised a pipeline including enumeration of strategies from evolutionary algorithms (EA) and minimal cut sets (MCS), filtering and flux analysis of predicted mutants to optimize the production of succinic acid in Saccharomyces cerevisiae . We additionally attempt to generalize problem formulations for MCS enumeration within the context of growth-coupled product synthesis. Strategies from evolutionary algorithms show the best compromise between acceptable growth rates and compound overproduction. However, constrained MCSs lead to a larger variety of phenotypes with several degrees of growth-coupling with production flux. The latter have proven useful in revealing the importance, in silico, of the gamma-aminobutyric acid shunt and manipulation of cofactor pools in growth-coupled designs for succinate production, mechanisms which have also been touted as potentially useful for metabolic engineering. Conclusions The two main groups of CSOMs are valuable for finding growth-coupled mutants. Despite the limitations in maximum growth rates and large strategy sizes, MCSs help uncover novel mechanisms for compound overproduction and thus, analyzing outputs from both methods provides a richer overview on strategies that can be potentially carried over in vivo. Electronic supplementary material The online version of this article (10.1186/s12859-019-2934-y) contains supplementary material, which is available to authorized users.

Systematizing the different notions of growth‐coupled product synthesis and a single framework for computing corresponding strain designs

“…For assessing the suitability and production potential of designed strains, different measures can be used. [2,25,[39][40][41]…”

Saccharomyces cerevisiae as a Host for Chondroitin Production

“…Genome-scale metabolic models (GEMs) are powerful resources that consist in the representation of the entire metabolic network of a biological system, including enzymes, metabolites, reactions, genes, and their associations, containing information on stoichiometry, compartmentalization, and biomass composition [14]. The use of these models to evaluate the organism biological capabilities requires the representation of the biochemical conversions following a stoichiometric matrix representation containing the stoichiometric coefficients for each metabolite in each reaction, where reactions are the columns and the metabolites the rows [14,15]. Constraint-based modelling assumes that cells operate in a steady-state, meaning that the metabolites may not be accumulated, and by applying flux constrains through upper and lower bounds, this matrix is transformed into a system of linear equations which can be used to calculate the flux of each reaction [14,15].…”

“…The use of these models to evaluate the organism biological capabilities requires the representation of the biochemical conversions following a stoichiometric matrix representation containing the stoichiometric coefficients for each metabolite in each reaction, where reactions are the columns and the metabolites the rows [14,15]. Constraint-based modelling assumes that cells operate in a steady-state, meaning that the metabolites may not be accumulated, and by applying flux constrains through upper and lower bounds, this matrix is transformed into a system of linear equations which can be used to calculate the flux of each reaction [14,15]. As this represents an undetermined system, a biological relevant reaction, usually biomass production, is used as the objective function to formulate a linear problem that can be solved using mathematical programming [14].…”

In Silico Design of Saccharomyces cerevisiae Strains for Improved Production of Chondroitin

“…Genome-scale metabolic models (GEMs) are powerful resources that consist in the representation of the entire metabolic network of a biological system, including the enzymes, metabolites, reactions, genes, and their associations, containing information on stoichiometry, compartmentalization and biomass composition [14]. The use of these models to evaluate the organism biological capabilities requires the representation of the biochemical conversions following a stoichiometric matrix representation containing the stoichiometric coefficients for each metabolite in each reaction, where reactions are the columns and the metabolites the rows [14,15]. Constraintbased modelling assumes that cells operate in a steady-state, meaning that the metabolites may not be accumulated, and by applying flux constrains through upper and lower bounds, this matrix is transformed into a system of linear equations which can be used to calculate the flux of each reaction [14,15].…”

“…The use of these models to evaluate the organism biological capabilities requires the representation of the biochemical conversions following a stoichiometric matrix representation containing the stoichiometric coefficients for each metabolite in each reaction, where reactions are the columns and the metabolites the rows [14,15]. Constraintbased modelling assumes that cells operate in a steady-state, meaning that the metabolites may not be accumulated, and by applying flux constrains through upper and lower bounds, this matrix is transformed into a system of linear equations which can be used to calculate the flux of each reaction [14,15]. As this represents an undetermined system, a biological relevant reaction, usually biomass production, is used as the objective function to formulate a linear problem that can be solved using mathematical programming [14].…”