In cameo we have two ways of predicting gene knockout targets: using evolutionary algorithms (OptGene) or linear programming (OptKnock)
from cameo import models
model = models.bigg.iJO1366
wt_solution = model.solve()
growth = wt_solution.fluxes["BIOMASS_Ec_iJO1366_core_53p95M"]
acetate_production = wt_solution.fluxes["EX_ac_e"]
from cameo import phenotypic_phase_plane
p = phenotypic_phase_plane(model, variables=['BIOMASS_Ec_iJO1366_core_53p95M'], objective='EX_ac_e')
p.plot(points=[(growth, acetate_production)])
OptGene is an approach to search for gene or reaction knockouts that relies on evolutionary algorithms[1]. The following image from authors summarizes the OptGene workflow.
Every iteration we keep the best 50 individuals so we can generate a library of targets.
from cameo.strain_design.heuristic.evolutionary_based import OptGene
optgene = OptGene(model)
result = optgene.run(target="EX_ac_e",
biomass="BIOMASS_Ec_iJO1366_core_53p95M",
substrate="glc__D_e",
max_evaluations=5000,
plot=False)
Starting optimization at Wed, 29 Mar 2017 20:41:05
Finished after 00:15:48
result
reactions | genes | size | fva_min | fva_max | target_flux | biomass_flux | yield | fitness | |
---|---|---|---|---|---|---|---|---|---|
0 | (ATPS4rpp,) | ((b3738,), (b3736,)) | 1.0 | 0.000000 | 14.187819 | -0.000000 | 0.402478 | -0.000000 | -0.000000 |
1 | (ACNAMt2pp, ATPS4rpp) | ((b3731, b3224),) | 2.0 | 0.000000 | 14.187819 | 12.540613 | 0.402478 | 1.254061 | 0.504732 |
2 | (SUCptspp, ATPS4rpp, ACMUMptspp) | ((b2429, b3731),) | 2.0 | 0.000000 | 14.187819 | 13.920956 | 0.402478 | 1.392096 | 0.560288 |
3 | (SUCOAS, ATPS4rpp) | ((b0728, b3731),) | 2.0 | 12.224462 | 14.187819 | 13.942932 | 0.402478 | 1.394293 | 0.561172 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
29 | (ADMDC, ATPS4rpp) | ((b0120, b3731),) | 2.0 | 0.000000 | 14.187819 | 13.942932 | 0.402478 | 1.394293 | 0.561172 |
30 | (TDSR1, ATPS4rpp, DSBCGT) | ((b2893, b3731),) | 2.0 | 0.000000 | 14.187819 | 12.729881 | 0.402478 | 1.272988 | 0.512350 |
31 | (ATPS4rpp,) | ((b0997, b2489, b3731),) | 3.0 | 0.000000 | 14.187819 | 13.942932 | 0.402478 | 1.394293 | 0.561172 |
32 | (CYTBO3_4pp, ATPS4rpp, ZNabcpp) | ((b1858, b3731, b0432),) | 3.0 | 0.000000 | 14.187819 | 13.942932 | 0.402478 | 1.394293 | 0.561172 |
33 rows × 9 columns
result.plot(0)
result.display_on_map(0, "iJO1366.Central metabolism")
OptKnock uses a bi-level mixed integer linear programming approach to identify reaction knockouts[2]:
$$ \begin{matrix} maximize & \mathit{v_{chemical}} & & (\mathbf{OptKnock}) \\ \mathit{y_j} & & & \\ subject~to & maximize & \mathit{v_{biomass}} & (\mathbf{Primal}) \\ & \mathit{v_j} & & & & \\ \end{matrix}\\ \begin{bmatrix} subject~to & \sum_{j=1}^{M}S_{ij}v_{j} = 0,\\ & v_{carbon\_uptake} = v_{carbon~target}\\ & v_{apt} \ge v_{apt\_main}\\ & v_{biomass} \ge v_{target\_biomass}\\ & v_{j}^{min} \cdot y_j \le v_j \le v_{j}^{max} \cdot y_j, \forall j \in \boldsymbol{M} \\ \end{bmatrix}\\ \begin{align} & y_j = {0, 1}, & & \forall j \in \boldsymbol{M} & \\ & \sum_{j \in M} (1 - y_j) \le K& & & \\ \end{align} $$from cameo.strain_design.deterministic.linear_programming import OptKnock
optknock = OptKnock(model, fraction_of_optimum=0.1)
2583 1705
/Users/niso/Dev/cameo/cameo/flux_analysis/structural.py:166: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix
Running multiple knockouts with OptKnock can take a few hours or days...
result = optknock.run(max_knockouts=1, target="EX_ac_e", biomass="BIOMASS_Ec_iJO1366_core_53p95M")
result
reactions | size | EX_ac_e | biomass | fva_min | fva_max | |
---|---|---|---|---|---|---|
0 | {ATPS4rpp} | 1.0 | 13.942932 | 0.402477 | 0.0 | 14.187819 |
result.plot(0)
result.display_on_map(0, "iJO1366.Central metabolism")
[1]Patil, K. R., Rocha, I., Förster, J., & Nielsen, J. (2005). Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics, 6, 308. doi:10.1186/1471-2105-6-308
[2]Burgard, A.P., Pharkya, P., Maranas, C.D. (2003), "OptKnock: A Bilevel Programming Framework for Identifying Gene Knockout Strategies for Microbial Strain Optimization," Biotechnology and Bioengineering, 84(6), 647-657.