In [1]:
from cameo import models
In [2]:
model = models.bigg.iJO1366
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wt_solution = model.optimize()
growth = wt_solution.fluxes["BIOMASS_Ec_iJO1366_core_53p95M"]
acetate_production = wt_solution.fluxes["EX_ac_e"]
In [4]:
from cameo import phenotypic_phase_plane
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p = phenotypic_phase_plane(model, variables=['BIOMASS_Ec_iJO1366_core_53p95M'], objective='EX_ac_e')
p.plot(points=[(growth, acetate_production)])
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from cameo.strain_design import OptGene
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optgene = OptGene(model)
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result = optgene.run(target=model.reactions.EX_ac_e,
biomass=model.reactions.BIOMASS_Ec_iJO1366_core_53p95M,
substrate=model.metabolites.glc__D_e,
max_evaluations=5000,
plot=False)
In [9]:
result
Out[9]:
In [10]:
result.plot(0)
In [11]:
result.display_on_map(0, "iJO1366.Central metabolism")
OptKnock uses a bi-level mixed integer linear programming approach to identify reaction knockouts[2]:
$$ \begin{matrix} maximize & \mathit{v_{chemical}} & & (\mathbf{OptKnock}) \\ \mathit{y_j} & & & \\ subject~to & maximize & \mathit{v_{biomass}} & (\mathbf{Primal}) \\ & \mathit{v_j} & & & & \\ \end{matrix}\\ \begin{bmatrix} subject~to & \sum_{j=1}^{M}S_{ij}v_{j} = 0,\\ & v_{carbon\_uptake} = v_{carbon~target}\\ & v_{apt} \ge v_{apt\_main}\\ & v_{biomass} \ge v_{target\_biomass}\\ & v_{j}^{min} \cdot y_j \le v_j \le v_{j}^{max} \cdot y_j, \forall j \in \boldsymbol{M} \\ \end{bmatrix}\\ \begin{align} & y_j = {0, 1}, & & \forall j \in \boldsymbol{M} & \\ & \sum_{j \in M} (1 - y_j) \le K& & & \\ \end{align} $$
In [12]:
from cameo.strain_design import OptKnock
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optknock = OptKnock(model, fraction_of_optimum=0.1)
Running multiple knockouts with OptKnock can take a few hours or days...
In [14]:
result = optknock.run(max_knockouts=1, target="EX_ac_e", biomass="BIOMASS_Ec_iJO1366_core_53p95M")
In [15]:
result
Out[15]:
In [16]:
result.plot(0)
In [17]:
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.