Predict gene knockout strategies ================================ In cameo we have two ways of predicting gene knockout targets: using evolutionary algorithms (OptGene) or linear programming (OptKnock) .. raw:: html
If you're running this notebook on `try.cameo.bio `__, things might run very slow due to our inability to provide access to the proprietary `CPLEX `__ solver on a public webserver. Furthermore, Jupyter kernels might crash and restart due to memory limitations on the server. .. raw:: html
.. code:: python from cameo import models .. code:: python model = models.bigg.iJO1366 .. code:: python wt_solution = model.solve() growth = wt_solution.fluxes["BIOMASS_Ec_iJO1366_core_53p95M"] acetate_production = wt_solution.fluxes["EX_ac_e"] .. code:: python from cameo import phenotypic_phase_plane .. code:: python p = phenotypic_phase_plane(model, variables=['BIOMASS_Ec_iJO1366_core_53p95M'], objective='EX_ac_e') p.plot(points=[(growth, acetate_production)]) .. raw:: html .. raw:: html
OptGene ------- 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. .. code:: python from cameo.strain_design.heuristic.evolutionary_based import OptGene .. code:: python optgene = OptGene(model) .. code:: python result = optgene.run(target="EX_ac_e", biomass="BIOMASS_Ec_iJO1366_core_53p95M", substrate="glc__D_e", max_evaluations=5000, plot=False) .. parsed-literal:: Starting optimization at Fri, 17 Jun 2016 15:01:57 Finished after 00:01:48 .. parsed-literal:: /Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/bokeh/io.py:532: UserWarning: Cannot find a last shown plot to update. Call output_notebook() and show() before push_notebook() .. code:: python result .. raw:: html

OptGene Result

reactions genes size fva_min fva_max target_flux biomass_flux yield fitness
0 (UM4PL, PGCD, CPH4S, UM3PL, HEPT4, ATPS4rpp) ((b4233, b2913, b2765, b3738, b3623),) 5.0 0.0 14.976296 13.006011 0.388364 1.300601 0.505107
1 (PGCD, ARBabcpp, ACNAMt2pp, ATPS4rpp) ((b1900, b2913, b1033, b3738, b3224),) 5.0 0.0 14.976296 14.801391 0.388364 1.480139 0.574833
2 (PGCD, ATPS4rpp, ALAt4pp, GLYt4pp) ((b2913, b0007, b1612, b1033, b3738), (b2913, ... 5.0 0.0 14.976296 14.801391 0.388364 1.480139 0.574833
3 (HPYRI, PGCD, MALDDH, QUINDH, ATPS4rpp) ((b1800, b0508, b2913, b3738, b1692),) 5.0 0.0 14.976296 14.471909 0.388364 1.447191 0.562037
4 (PGCD, QUINDH, ATPS4rpp) ((b4024, b2913, b1033, b3738, b1692),) 5.0 0.0 14.976296 14.801391 0.388364 1.480139 0.574833
5 (ALAt4pp, PGCD, GLYt4pp, QUINDH, ATPS4rpp) ((b4226, b2913, b0007, b3738, b1692),) 5.0 0.0 14.976296 14.801391 0.388364 1.480139 0.574833
6 (PGCD, DDGLK, CPH4S, QUINDH, ATPS4rpp) ((b3738, b2913, b2765, b3526, b1692),) 5.0 0.0 14.976296 14.471909 0.388364 1.447191 0.562037
... ... ... ... ... ... ... ... ... ...
41 (PGCD, CINNDO, ATPS4rpp, PPPNDO, ARGDCpp) ((b2913, b1033, b2938, b4226, b2542, b3738, b2... 7.0 0.0 14.976296 14.801391 0.388364 1.480139 0.574833
42 (PGCD, SUCptspp, EDA, ACMUMptspp, AGMt2pp, ATP... ((b2913, b0007, b2429, b3738, b1850, b0433, b1... 7.0 0.0 14.976296 14.597554 0.388364 1.459755 0.566916
43 (FUCtpp, PGCD, G6PDA, QUINDH, ATPS4rpp, NO3R1bpp) ((b2913, b1033, b2204, b1692, b3738, b0678, b2... 7.0 0.0 14.976296 14.801391 0.388364 1.480139 0.574833
44 (HKNDDH, CITL, PGCD, FRD3, FRD2, SUCptspp, ACM... ((b4154, b2913, b2429, b3738, b0614, b3517, b0... 7.0 0.0 14.976296 14.801391 0.388364 1.480139 0.574833
45 (PGCD, HCYSMT2, QUINDH, HCYSMT, ATPS4rpp, ALAt... ((b2913, b0007, b1033, b1692, b3738, b0261, b2... 7.0 0.0 14.976296 14.304026 0.388364 1.430403 0.555517
46 (PGCD, FRD3, FRD2, SUCptspp, ACMUMptspp, HKNTD... ((b4154, b2913, b2429, b3738, b0614, b1101, b0... 7.0 0.0 14.976296 14.801391 0.388364 1.480139 0.574833
47 (PGCD, QUINDH, ATPS4rpp) ((b2913, b1033, b4226, b1692, b3738, b4244, b3... 8.0 0.0 14.976296 14.801391 0.388364 1.480139 0.574833

48 rows × 9 columns

.. code:: python result.plot(0) .. raw:: html
.. code:: python result.display_on_map(0, "iJO1366.Central metabolism") .. parsed-literal:: /Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/escher/plots.py:155: UserWarning: Map not in cache. Attempting download from https://escher.github.io/1-0-0/5/maps/Escherichia%20coli/iJO1366.Central%20metabolism.json .. raw:: html
OptKnock -------- OptKnock uses a bi-level mixed integer linear programming approach to identify reaction knockouts[2]: .. math:: \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} .. code:: python from cameo.strain_design.deterministic.linear_programming import OptKnock .. code:: python optknock = OptKnock(model, fraction_of_optimum=0.1) Running multiple knockouts with OptKnock can take a few hours or days... .. code:: python result = optknock.run(max_knockouts=1, target="EX_ac_e", biomass="BIOMASS_Ec_iJO1366_core_53p95M") .. raw:: html .. parsed-literal:: .. code:: python result .. raw:: html

OptKnock:

reactions size EX_ac_e biomass fva_min fva_max
0 {ATPS4rpp} 1.0 13.942943 0.402477 0.0 14.187817
.. code:: python result.plot(0) .. raw:: html
.. code:: python result.display_on_map(0, "iJO1366.Central metabolism") .. raw:: html
References ---------- [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.