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"source": [
"# Core structure\n",
"The core framework is responsible for parsing and interpreting microbial physiology data by parsing it into a object-oriented and database-driven hierarchy. The data schema is based on the workflow of designing experiments and the associated data analysis process. \n",
"\n",
"This is an overview of the models:\n",
"\n",
"| Model | Function |\n",
"|-----------------------|----------------------------------------------------------------------------------|\n",
"| TrialIdentifier | Describes a trial (time, analyte, strain, media, etc.) |\n",
"| AnalyteData | Time, data points and vectors for quantified data (g/L product, OD, etc.) |\n",
"| SingleTrial | All analytes for a given unit (e.g. a tube, well on plate, bioreactor, etc.) |\n",
"| ReplicateTrial | Contains a set of `SingleTrial`s with replicates grouped to calculate statistics |\n",
"| Experiment | All of the trials performed on a given date |\n",
"| Project | Groups of experiments with overall goals | \n",
"\n",
"## `TrialIdentifier` class\n",
"\n",
"Before any data importing, a description should be generated. Data is described based on the strain (organism, plasmids, etc.), the media (salts, carbon sources, nitrogen sources, etc.) and the environment (temperature, labware, shaking speed, etc.) A trial identifier is everything required to uniquely identify a point of data.\n",
"\n",
"The three fundamental units of a trial identifier are the `Strain`, `Media` and `Environment` classes. \n",
"\n",
"| Model | Function |\n",
"|-------------------|--------------------------------------------------------------------------------------|\n",
"| Strain | Describes the organism being characterized (e.g. strain, knockouts, plasmids, etc.) |\n",
"| Media | Described the medium used to characterize the organism (e.g. M9 + 0.02% glc_D) |\n",
"| Environment | The conditions and labware used (e.g. 96-well plate, 250RPM, 37C) |\n",
"\n",
"We will build a trial identifier with all its components and use it to describe some data.s"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LMSE001+pTrc99a\n",
"20g/L IPTG\n",
"96MTP 250RPM 37C\n"
]
}
],
"source": [
"import impact as impt\n",
"from importlib import reload\n",
"reload(impt)\n",
"strain = impt.Strain()\n",
"strain.name = 'LMSE001'\n",
"strain.plasmids.append(impt.Plasmid(name='pTrc99a'))\n",
"print(strain)\n",
"\n",
"media = impt.Media()\n",
"media.add_component('IPTG',concentration= 20,unit='ng/mL')\n",
"print(media)\n",
"\n",
"env = impt.Environment(labware=impt.Labware(name='96MTP'),\n",
" shaking_speed = 250,\n",
" temperature = 37)\n",
"print(env)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With these fundamental units, we can construct a trial identifier."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"strain: LMSE001+pTrc99a,\tmedia: 20g/L IPTG,\tenv: 96MTP 250RPM 37C,\tanalyte: None,\trep: -1\n"
]
}
],
"source": [
"ti = impt.TimeCourseIdentifier(strain=strain,media=media,environment=env)\n",
"print(ti)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see the strain, media and environment set and some empty values for analyte and replicate. Let's fill in the missing values required to fully describe the analyte."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"strain: LMSE001+pTrc99a,\tmedia: 20g/L IPTG,\tenv: 96MTP 250RPM 37C,\tanalyte: glc__D,\trep: 1\n"
]
}
],
"source": [
"ti.analyte_name = 'glc__D'\n",
"ti.analyte_type = 'substrate'\n",
"ti.replicate_id = 1\n",
"print(ti)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We can now use this trial identifier to build objects with experimental data.\n",
"\n",
"## `AnalyteData`\n",
"### `TimePoint` and `TimeCourse`\n",
"These time points are rarely used directly, but are included in order to flatten data into a relational database. These time points can either be created and added individually, or a time vector and data vector can be provided and the associated time points will automatically be generated."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0\n",
"1 1\n",
"2 2\n",
"3 3\n",
"4 4\n",
"5 5\n",
"dtype: int64\n"
]
}
],
"source": [
"substrate = impt.Substrate()\n",
"# Add each time point individually\n",
"for t, data in zip([0,1,2,3,4,5],[0,1,2,3,4,5]):\n",
" tp = impt.TimePoint(trial_identifier=ti,time=t,data=data)\n",
" substrate.add_timepoint(tp)\n",
"\n",
"# or, add the vectors\n",
"substrate = impt.Substrate(trial_identifier=ti,time_vector=[0,1,2,3,4,5],data_vector=[0,1,2,3,4,5])\n",
"print(substrate.pd_series)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we instantiated a substrate object because we are dealing with a substrate - this differentiation allows impact to choose the appropriate model for the data, as well as calculate features. Any time series data can be imported as a `impt.TimeCourse`, but additional details can be extracted if a specific data type is chosen. \n",
"\n",
"| Analyte type | Function |\n",
"|----|----|\n",
"| `Substrate` | An analyte which is consumed |\n",
"| `Product` | An analyte which is produced |\n",
"| `Biomass` | A measurement of the biomass concentration |\n",
"| `Reporter`| A reporter, such as fluorescence from gfp or mCherry |"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
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"text/html": [
""
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""
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],
"source": [
"import numpy as np\n",
"import impact.plotting as implot\n",
"\n",
"def exp_growth(t):\n",
" X0 = 0.05\n",
" mu = 0.1\n",
" return X0 * np.exp(mu*t)\n",
"\n",
"# def production(t, product_yield, biomass_concentration):\n",
"# rate = \n",
"\n",
"x = np.linspace(0,20,20)\n",
"y = exp_growth(x)\n",
"implot.plot([implot.go.Scatter(x=x,y=y)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `SingleTrial`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `ReplicateTrial`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `Experiment`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `Project`"
]
}
],
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