Table 3.
Tools for computational strain design. (HI)–Heterologous insertion; (RD)–Reaction deletion; (RA)–Reaction amplification; (RDR)–Reaction down-regulation; (GD)–Gene deletion; (GA)–Gene amplification; (GDR)–Gene down-regulation.
| Algorithm | Description | Type of intervention | Heuristic/Exact | Ref. |
|---|---|---|---|---|
| OptKnock | It is a bi-level optimisation framework where the inner problem maximises the cellular objective while the outer problem maximises the bioengineering objective. Most of the future algorithms adopt a similar framework. | RD | Exact | [142] |
| OptStrain | Identifies the non-native reactions to be cloned into the microbe to achieve heterologous functionality. | RD/ HI | Exact | [149] |
| OptReg | Identifies both reaction deletions and amplifications using OptKnock framework | RD/RA | Exact | [155] |
| OptORF | Uses GPR association rules to identify gene knock-outs and amplifications. (Penalty for each intervention) | GD/GA | Exact | [156] |
| OptForce | Compares the flux ranges for wild type and mutant network using FVA and thereby identifies intervention targets. | RD/RA/RDR | Exact | [157] |
| FSEOF | Scans the changes in the flux distribution of the metabolic network when the product synthesis is pushed. The reactions that show an increase/decrease in flux, as a result, are chosen as potential over-expression/deletion targets | RD/RA | Exact | [145] |
| EMILiO | Uses iterative linear optimisation to identify the optimal flux values for each intervention target. | Flux value | Exact | [150] |
| CASOP | Uses elementary modes to identify deletion and overexpression targets. | RD/RA | Exact | [158] |
| cMCS | Identifies reaction deletions by identifying constrained Minimal Cut-Sets (cMCS), which are MCS that are restricted to maintain certain functionalities. These constraints are chosen such that the bioengineering objective is met. | RD | Exact | [159] |
| CosMos | Identifies optimal flux value by continuous modification of flux bounds of a reaction | Flux value | Exact | [160] |
| NIHBA | Uses evolutionary game theory and a hybrid Bender’s algorithm to optimise the strain design | RD | Exact | [151] |
| OptGene | Uses genetic algorithms to identify knock-out targets | RD | Heuristic | [148] |
| ModCell2 | Uses evolutionary algorithms to achieve modular cell engineering. Here, the parent strain is transformed into a modular cell, and many such exchange modules constitute a strain design | RD | Heuristic | [152] |
| OptRAM | Uses simulated annealing to identify knock-outs, up-regulation, and down-regulation of genes and transcription factors from the IDREAM integrated network framework | GD/GA/GDR | Heuristic | [153] |