Skip to main content
. 2017 Aug 2;2(2):87–96. doi: 10.1016/j.synbio.2017.07.003

Table 2.

Examples of different algorithms for in silico simulation [45], [48].

Purpose of simulation Algorithm Objective
To accurately describe cellular physiology OMNI Identifies a set of bottleneck reactions to be removed in the model, to minimize the disagreement between the model predictions and experimental data
SR-FBA Predicts gene expression and metabolic fluxes
TMFA Predicts intracellular flux distribution with thermodynamic constraints
To predict metabolic capability after genetic perturbation MOMA Minimizes the Euclidian distance from a wild type flux distribution under knock-out condition
ROOM Minimizes the number of significant flux changes in the knock-out mutant compared to the wild type
OptKnock Predicts gene knock-out targets through bilevel optimization framework
OptGene Predicts gene knock-out targets using genetic algorithm and constraints-based flux analysis
OptReg Determines the activation/inhibition and elimination reaction set for biochemical production