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]
}
],
"source": [
"from cameo import config\n",
"config.default_view = config.SequentialView()\n",
"from pandas import options\n",
"options.display.max_rows = 8"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using meta-heuristics to search for knockout strategies.\n",
"\n",
"Cameo uses Evolutionary Algorithms, which allow the search of near-optimal solutions very fast by combining linear programming to simulate flux distributions and Evolutionary Algorithms to find combinations that improve, for example the yield of a desired product. The OptKnock[1] method uses this approach.\n",
"\n",
"The evolutionary algorithms are iterative algorithms. In each iteration multiple solutions are evaluated and assigned a fitness value. The solutions that improve the objective are kept. Some of them are altered and reassigned to the next iteration. After some rounds the objective has been improved and if the algorithm is ran long enought all possible solutions will be covered.\n",
"\n",
"Thanks to the inspyred library, we implemented a low level interface to allow the implementation of more elaborate strategies than OptGene."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load model\n",
"The first step is to load a model as usual."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from cameo import models\n",
"iJO1366 = models.bigg.iJO1366"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define objective functions\n",
"\n",
"An objective function defines how the fitness of a solution is computed in the evaluation phase.\n",
"\n",
"Cameo comes with prebuilt objective functions, e.g., Biomass-Product coupled yield[1]."
]
},
{
"cell_type": "code",
"execution_count": 5,
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"text/latex": [
"$$bpcy = \\frac{(BIOMASS\\_Ec\\_iJO1366\\_core\\_53p95M * EX\\_ac\\_e)}{EX\\_glc\\_\\_D\\_e}$$"
],
"text/plain": [
""
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"execution_count": 5,
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"source": [
"from cameo.strain_design.heuristic.evolutionary.objective_functions import biomass_product_coupled_yield\n",
"of = biomass_product_coupled_yield(iJO1366.reactions.BIOMASS_Ec_iJO1366_core_53p95M,\n",
" iJO1366.reactions.EX_ac_e,\n",
" iJO1366.reactions.EX_glc__D_e)\n",
"of"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other fitness functions such as yield or number of knockouts are also available in cameo."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from cameo.strain_design.heuristic.evolutionary.objective_functions import product_yield, number_of_knockouts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Costumized objectives can be implemented by extending the base class ObjectiveFunction.\n",
"\n",
"During the evaluation phase, all objective functions will be called with the follwing parameters of(model, flux_distribution, decoded_solution)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Search for gene knockouts with single objective"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we are looking for gene knockouts leading to biomass coupled acetate production with *E. coli* through iJO1366 model. This is very similar to OptGene."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Setup optimization strategy for gene knockouts\n",
"\n",
"There are multiple configurations for this strategy. The most basic configuration requires a model and an objective funtion. More parameters can be used, such as the method to simulate the flux distributions (FBA is the defualt), the Evolutionary Computation (Genetic Algorithm inspyred.ec.GAis the default). The implemetation removes the essential genes from the search, as they won't yield soutions that are viable. More genes can be removed from the search if defined (either due to biological knowlege or user strategy)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from cameo.strain_design.heuristic.evolutionary import GeneKnockoutOptimization\n",
"ko = GeneKnockoutOptimization(model=iJO1366, objective_function=of)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Run optimization for gene knockouts with single objective"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"Starting optimization at Thu, 14 Jan 2016 13:49:55\n"
]
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"text": [
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"To override, pass 'load_from_config=False' to Session\n"
]
},
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"text": [
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