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‎Contributed/Mitra2019Rab/README.md‎

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## Name
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Mitra2019Rab
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## Description
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A model of Rab5 and Rab7 activation and EGFR degradation in response to stimulation with EGF.
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This model is supplementary material for the following manuscript in preparation:
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Shideler T, Palsuledesai C, Suderman R, Mitra E, Cleyrat C, Hlavacek WS, Wandinger-Ness A. A High Dose of EGF Promotes EGFR Degradation by Increasing Rab7 Activity.
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## Files
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* [rab_wt.bngl](rab_wt.bngl) - Wild type model
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* [rab_mon1ccz1_ox.bngl](rab_mon1ccz1_ox.bngl) - Model with overexpression of Mon1 and Ccz1
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* [rab_rab5_ox](rab_rab5_ox.bngl) - Model with overexpression of Rab5
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* [rab_rab7_ox](rab_rab7_ox.bngl) - Model with overexpression of Rab7
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* [pybnf_files](pybnf_files) - Model, data, and configuration files required to run model parameterization in PyBioNetFit
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## Notes
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This model was parameterized using PyBioNetFit v1.0.0

‎Contributed/Mitra2019Rab/pybnf_files/data_highegf3.exp‎

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# time egfr egfr_SD rab7gtp rab7gtp_SD
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120 0.78812667 0.13437561 0.70698575 0.57958402
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300 0.65062333 0.10533044 1.152388 0.46246672
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600 0.62620667 0.20758745 2.31854275 0.50726175
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900 0.50361333 0.10937092 1.616742 0.4883859
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1800 0.43597 0.11395946 1.99361775 0.88879382
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3600 NaN NaN 2.2752175 1.10003285

‎Contributed/Mitra2019Rab/pybnf_files/data_highegf3_rab5_ox.exp‎

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# time egfr egfr_SD
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120 0.5610868309 0.06698159023
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360 0.5425303104 0.04132484859
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600 0.4164146263 0.05312691097
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900 0.3466113921 0.05004826809
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1800 0.2907308885 0.06742306608

‎Contributed/Mitra2019Rab/pybnf_files/data_highegf3_rab7_ox.exp‎

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# time egfr egfr_SD
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120 0.6748848546 0.1078264643
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360 0.5488554259 0.08151190992
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600 0.5318678489 0.02185065848
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900 0.5235578495 0.05365246312
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1800 0.5951124657 0.09779005093

‎Contributed/Mitra2019Rab/pybnf_files/data_lowegf2.exp‎

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# time egfr egfr_SD rab7gtp rab7gtp_SD
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120 NaN NaN 0.90025 0.137
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300 1.029308 0.1882729 1.07533 0.704
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600 0.869068 0.016 1.333 0.802
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900 0.6725329 0.05449072 1.285 0.6595
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1200 NaN NaN 1.991 1.0895
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1800 0.632574 0.1330357 NaN NaN
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2400 NaN NaN 1.9058 1.6473
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3600 NaN NaN 2.0282 1.8242

‎Contributed/Mitra2019Rab/pybnf_files/data_lowegf2_mon1ccz1_ox.exp‎

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# time egfr egfr_SD
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120 0.6681650793 0.4960285697
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360 0.6065041199 0.6980735303
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600 0.5103908816 0.5674712948
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900 0.3509189565 0.8870195258
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1800 0.422759868 0.1683747323

‎Contributed/Mitra2019Rab/pybnf_files/data_lowegf2_rab5_ox.exp‎

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# time egfr egfr_SD
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120 0.6231788803 0.04248432928
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360 0.667628705 0.09318916349
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600 0.5302978561 0.06907484439
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900 0.483879472 0.06037754005
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1800 0.5561836567 0.1118154818

‎Contributed/Mitra2019Rab/pybnf_files/data_lowegf2_rab7_ox.exp‎

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# time egfr egfr_SD
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120 0.7980366766 0.03396702072
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360 0.7004101708 0.03849201648
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600 0.748727328 0.04460668323
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900 0.7238936169 0.01969352377
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1800 0.8110201569 0.08422553207

‎Contributed/Mitra2019Rab/pybnf_files/egfr_ss_norm2.exp‎

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# time egfr egfr_SD
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45000.0 1.0 0.00328
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47500.0 1.0 0.00328
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50000.0 1.0 0.00328

‎Contributed/Mitra2019Rab/pybnf_files/rab.conf‎

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# Configuration to fit Rab model in PyBNF
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model = rab_wt.bngl : data_highegf3.exp, data_lowegf2.exp, egfr_ss_norm2.exp, transfer.exp
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# Overexpression models.
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model = rab_rab5_ox.bngl : data_highegf3_rab5_ox.exp, data_lowegf2_rab5_ox.exp
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model = rab_rab7_ox.bngl : data_highegf3_rab7_ox.exp, data_lowegf2_rab7_ox.exp
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model = rab_mon1ccz1_ox.bngl : data_lowegf2_mon1ccz1_ox.exp
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output_dir = rab_74
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simulation_dir = /tmp
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fit_type = ss
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objfunc = chi_sq
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# parallel_count = 36
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delete_old_files = 1
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output_every = 50
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loguniform_var = Kd_RABGEF1__FREE 1e-2 1e6
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loguniform_var = k_to_endo__FREE 1e-8 0.01
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loguniform_var = k_recyc__FREE 1e-3 1e3
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loguniform_var = k_dephos__FREE 1e-2 1e1
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loguniform_var = k_deub__FREE 1e-2 1e1
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loguniform_var = kr_KUb_UIM__FREE 1e-3 1e1
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loguniform_var = kf_pTyr_SH2__FREE 1e-8 1e-5
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loguniform_var = kr_pTyr_SH2__FREE 1e-2 1e1
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loguniform_var = k_insert__FREE 1e-8 1e-2
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loguniform_var = k_extract__FREE 1e-5 1e-1
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loguniform_var = kcat_Rab7__FREE 1e-3 1e1
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loguniform_var = kcat_Rab5__FREE 1e-3 1e1
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loguniform_var = kf_Rab5_Rabep1__FREE 1e-8 1e-4
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loguniform_var = kr_Rab5_Rabep1__FREE 1e-3 1e1
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loguniform_var = kf_Rab5_Mon1__FREE 1e-8 1e-4
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loguniform_var = kr_Rab5_Mon1__FREE 1e-3 1e1
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loguniform_var = d_threshold__FREE 1e1 1e5
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loguniform_var = k_deg__FREE 1e-1 1e3
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uniform_var = d_hill__FREE 1 16
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uniform_var = ub_hill_coef__FREE 0.1 6
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uniform_var = ub_half_coef__FREE 0.1 100
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loguniform_var = ub_basal_coef__FREE 0.001 1
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loguniform_var = ub_scale_coef__FREE 0.1 10
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uniform_var = py_hill_coef__FREE 0.1 6
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uniform_var = py_half_coef__FREE 0.1 100
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loguniform_var = py_basal_coef__FREE 0.001 1
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loguniform_var = py_scale_coef__FREE 0.1 10
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loguniform_var = endo_incr_p__FREE 1 100
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loguniform_var = endo_incr_ub__FREE 1 100
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loguniform_var = k_synth__FREE 0.1 100
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loguniform_var = r_threshold__FREE 1e1 1e5
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uniform_var = r_hill__FREE 1 16
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wall_time_sim = 360
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verbosity = 2
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normalization = init : (data_highegf3.exp: egfr, rab7gtp)
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normalization = init : data_lowegf2.exp
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normalization = init : egfr_ss_norm2.exp
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normalization = unit : transfer.exp
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normalization = init : data_highegf3_rab5_ox.exp
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normalization = init : data_lowegf2_rab5_ox.exp
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normalization = init : data_highegf3_rab7_ox.exp
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normalization = init : data_lowegf2_rab7_ox.exp
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normalization = init : data_lowegf2_mon1ccz1_ox.exp
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population_size = 15
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max_iterations = 800
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initialization = lh
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refine = 1
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simplex_max_iterations = 100

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