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Adjust makemodel so it can also be used for competition assay. #102

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sonyahanson opened this issue May 10, 2017 · 1 comment
Open

Adjust makemodel so it can also be used for competition assay. #102

sonyahanson opened this issue May 10, 2017 · 1 comment

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@sonyahanson
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Adjust makemodel so it can also be used for competition assay modeling. This might be easier if we use a dictionary as input instead a bunch of separate terms.

@sonyahanson
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This is the input file used for the quickmodel competition assay analysis.

inputs = {
    'xml_file_path' :  "$GITHUB/assaytools/examples/competition-fluorescence-assay/data/",
    'file_set'      :  {'Abl': glob("$GITHUB/assaytools/examples/competition-fluorescence-assay/data/Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate*.xml"),
                        'Abl-IMA': glob("$GITHUB/assaytools/examples/competition-fluorescence-assay/data/Abl Gef Ima gain 120 bw1020 2016-01-19 16-22-45_plate*.xml")},
    'ligand_order'  :  ['Gefitinib','Gefitinib','Gefitinib','Gefitinib'],
    'section'       :  '280_BottomRead',
    'Lstated'       :  np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0], np.float64), # ligand concentration
    'Pstated'       :  0.5e-6 * np.ones([12],np.float64), # protein concentration, M
    'assay_volume'  :  100e-6, # assay volume, L
    'well_area'     :  0.3969, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]
    }

Here is an alternative that captures this information slightly better to eventually be fed into a Bayesian model for fitting the competition assay:

inputs = {
    'xml_file_path' :  "$GITHUB/assaytools/examples/competition-fluorescence-assay/data/",
    'file_set'      :  {'Abl': glob("$GITHUB/assaytools/examples/competition-fluorescence-assay/data/Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate*.xml"),
                        'Abl-IMA': glob("$GITHUB/assaytools/examples/competition-fluorescence-assay/data/Abl Gef Ima gain 120 bw1020 2016-01-19 16-22-45_plate*.xml")},
    'ligand_order'  :  ['Gefitinib','Gefitinib','Gefitinib','Gefitinib'],
    'competitive_ligand'  :  'Imatinib',
    'section'       :  '280_BottomRead',
    'Lstated'       :  np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0], np.float64), # ligand concentration, M
    'Astated'       :  10.0e-6 * np.ones([12],np.float64), # competitive ligand concentration, M
    'Pstated'       :  0.5e-6 * np.ones([12],np.float64), # protein concentration, M
    'assay_volume'  :  100e-6, # assay volume, L
    'well_area'     :  0.3969, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]
    }

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