k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design

Abstract

Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

“…The most recent effort in MIP-based CSOMs combines the kinetic descriptions of metabolic steps with traditional stoichiometric models to improve their predictive power and suggest more accurate designs (113). By bridging the gap between stoichiometric-and kinetic-based models (114), k-OptForce may represent a game changer and a new chassis for future CSOM development efforts.…”

Applications of genome-scale metabolic network model in metabolic engineering

“…OptStrain [67], OptReg [68], OptForce [72], k-OptForce [16], OptORF [44], CosMos [20] Omics data integration Transcriptome GIMME [5], iMAT [82], GIM 3 E [76], E-Flux [18], PROM [13], MADE [38], tFBA [90], RELATCH [45], TEAM [19], AdaM [89], GX-FBA [60], mCADRE [92], FCGs [43], EXAMO [75], TIGER [37] Proteome GIMMEp [6] Pathway prediction BNICE [29], Cho et al [14], RetroPath [11], PathPred [59], DESHARKY [74], BioPath [94], XTMS [12], GEM-Path [56] phenotype and gene essentiality [24]. Even further, taking advantage of a large set of genome sequences available for various E. coli strains, the GEMs for 55 E. coli strains were used to investigate the variations in gene, reaction and metabolite contents, and the capabilities to adapt to different nutritional environments among the strains [40].…”

“…When compared with OptForce for succinate production in E. coli, CosMos suggested new strategies which required fewer modifications and gave higher succinate yield. Recently, k-OptForce was developed by incorporating known kinetic information of metabolic reactions into the OptForce platform [16]. In a benchmark study on the overproduction of l-serine in E. coli, k-OptForce identified key regulatory bottlenecks that OptForce failed to predict and eliminated unnecessary genetic interventions predicted by OptForce [16].…”

“…Recently, k-OptForce was developed by incorporating known kinetic information of metabolic reactions into the OptForce platform [16]. In a benchmark study on the overproduction of l-serine in E. coli, k-OptForce identified key regulatory bottlenecks that OptForce failed to predict and eliminated unnecessary genetic interventions predicted by OptForce [16]. k-OptForce also suggested genetic interventions leading to increased production of the target compound by alleviating the substratelevel inhibition of key enzymes, which was emphasized as an example demonstrating the benefit of integrating enzyme kinetic information into the stoichiometric model.…”

Advances in de novo strain design using integrated systems and synthetic biology tools

“…Couples production of the target product with growth Reduces the run time through successive linear programming BiMOMA [20] Identifies a set of KO strategies for overproduction of a target product Implements the homeostasis effect instead of optimal growth RobustKnock [79] Identifies a set of KO strategies for overproduction of a target product Couples production of the target product objective (i.e., bioengineering) with growth (i.e., biological) objective A three level optimization problem (max-min of target product production in outer problem and max of biomass production in the inner problem) k-OptForce [80] Integrates the available kinetic information Metabolite concentrations and enzyme abundance are constraints within the physiologically relevant ranges for reactions with available kinetic information Kamp and Klamt [81] Uses duality between elementary modes (EMs) and minimal cut sets (MCSs) This approach enumerates the smallest intervention strategies that ensure overproduction of a target product.…”