Predict gene knockout strategies

In cameo we have two ways of predicting gene knockout targets: using evolutionary algorithms (OptGene) or linear programming (OptKnock)

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.

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

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 Fri, 17 Jun 2016 15:01:57
Finished after 00:01:48

/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()

result

OptGene Result

• Simulation: fba
  
• Objective Function: $$bpcy = \frac{(BIOMASS\_Ec\_iJO1366\_core\_53p95M * EX\_ac\_e)}{EX\_glc\_\_D\_e}$$
  

	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

result.plot(0)

result.display_on_map(0, "iJO1366.Central metabolism")

/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

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View and build options

Identifiers:

ID'sDescriptive names

Scroll to zoom (instead of scroll to pan)Hide secondary metabolitesShow gene reaction rulesHide reaction, gene, and metabolite labelsAllow duplicate reactionsHighlight reactions not in model

Tip: To increase map performance, turn off text boxes (i.e. labels and gene reaction rules).

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Reactions

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Reaction or Gene data

Options:	Absolute valueSizeColorText (Show data in label)
Comparison:	Fold ChangeLog2(Fold Change)Difference

Method for evaluating AND:

MeanMin

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Comparison:	Fold ChangeLog2(Fold Change)Difference

— Jump to map —

OptKnock

OptKnock uses a bi-level mixed integer linear programming approach to identify reaction knockouts[2]:

$$\begin{split}\begin{matrix} maximize & \mathit{v_{chemical}} & & (\mathbf{OptKnock}) \\ \mathit{y_j} & & & \\ subject~to & maximize & \mathit{v_{biomass}} & (\mathbf{Primal}) \\ & \mathit{v_j} & & & & \\ \end{matrix}\\ \begin{bmatrix} subject~to & \sum_{j=1}^{M}S_{ij}v_{j} = 0,\\ & v_{carbon\_uptake} = v_{carbon~target}\\ & v_{apt} \ge v_{apt\_main}\\ & v_{biomass} \ge v_{target\_biomass}\\ & v_{j}^{min} \cdot y_j \le v_j \le v_{j}^{max} \cdot y_j, \forall j \in \boldsymbol{M} \\ \end{bmatrix}\\ \begin{align} & y_j = {0, 1}, & & \forall j \in \boldsymbol{M} & \\ & \sum_{j \in M} (1 - y_j) \le K& & & \\ \end{align}\end{split}$$

from cameo.strain_design.deterministic.linear_programming import OptKnock

optknock = OptKnock(model, fraction_of_optimum=0.1)

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")

<IPython.core.display.Javascript object>

result

OptKnock:

• Target: EX_ac_e

	reactions	size	EX_ac_e	biomass	fva_min	fva_max
0	{ATPS4rpp}	1.0	13.942943	0.402477	0.0	14.187817

result.plot(0)

result.display_on_map(0, "iJO1366.Central metabolism")

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• +
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• ↔

Tip: Hover over an option to see more details about it.

---

View and build options

Identifiers:

ID'sDescriptive names

Scroll to zoom (instead of scroll to pan)Hide secondary metabolitesShow gene reaction rulesHide reaction, gene, and metabolite labelsAllow duplicate reactionsHighlight reactions not in model

Tip: To increase map performance, turn off text boxes (i.e. labels and gene reaction rules).

---

Reactions

Reaction and gene data not loaded

Value:

Color:

Size:

Styles for reactions with no data

Color:

Size:

valuemeanQ1medianQ3

valuemeanQ1medianQ3

valuemeanQ1medianQ3

valuemeanQ1medianQ3

valuemeanQ1medianQ3

valuemeanQ1medianQ3

valuemeanQ1medianQ3

Reaction or Gene data

Options:	Absolute valueSizeColorText (Show data in label)
Comparison:	Fold ChangeLog2(Fold Change)Difference

Method for evaluating AND:

MeanMin

---

Metabolites

Metabolite data not loaded

Value:

Color:

Size:

Styles for metabolites with no data

Color:

Size:

value

value

Metabolite data

Options:	Absolute valueSizeColorText (Show data in label)
Comparison:	Fold ChangeLog2(Fold Change)Difference

— Jump to map —

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.