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README.txt
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* A model of PIP3 metabolism using Bayesian parameter estimation
* Test GIT again...
————— Part 1 —————
* Models
* user_1: A single forward and reverse reaction with ‘PI3K’ defined as ‘iSH2’ i.e. a spline of the data
max_pip3 = 1
max_pi3k = 1
* user_2: As Bandara et al. 2009 with ‘PI3K’ still defined by the data
max_pip3 = 200
max_pi3k = 100
ph_total = max(ph_per_trace)
* user_3: As user_1 but with Michaelis-Menten kinetics for PI3K and PTEN activity
### user_4/5 models are flawed ### (if the observable consists of 2 species, it cannot be used)
* user_4: ‘iSH2 (model)’ == ‘iSH2 (data)’ and a tunable p110_total parameter (the goal here is to include the ‘iSH2’ trace in the model but its effect is to saturate p110 at the PM)
* user_5: As user_4 but with Michaelis-Menten kinetics for PI3K and PTEN activity
————— Part 2 —————
* Modify user_4 for post-FK506 dynamics
* user_6: TOADD
————— Preliminary —————
* pip3bayes.model_1: A pysb model with ‘PI3K’ modeled as having a finite source
max_pip3 = 0.5
max_pi3k = 0.08 (uM)
initial conditions:
pi3k_source = 0.08
pten = 0.08
pten = 10
* pip3bayes.model_2: As model_1 but with a degradation term for the H2O2 inhibition of PIP3
* model_1_scale_free: As model 1 but with:
max_pip3 = 1
max_pi3k = 1
pten = 1
pi3k_source = 1
pip2 = 100
* model_1_scale_free: as model 2 with model_1_scale_free parameters
* user_1_scale_free: As user_1 but with ‘model_1_scale_free’ parameters (where applicable)
————— 02/02/2015 —————
* Make sure all files are correctly annotated (from paper >> R plots >> raw data)
————— 02/12/2014 —————
* Move the scaling data code out of ‘prepdata’ - getting very verbose
* Some hacks currently
* keep = range(110,191) - line 48 optimize.py
* post_inhib_idx = 30 - line 215 scale_data.py
————— 26/11/2014 —————
* Stitch the H2O2 data together
* Modify ‘model_2’ to incorporate H2O2 inhibition
————— 12/11/2014 —————
* Is the ‘PH’ variation explained by ‘iSH2’? Or is it noise?
* Is the ‘iSH2’ trace representative of active PI3K? - Test 2 alternative model sets to explore this
* Can these models predict the H2O2 data?
* What does the model say about PIP3 turnover at the PM?
————— TODO —————
* Be careful with references:
* model = m.model
* m_opts = m.options
* mcmc.options = o_opts
* Check the likelihood weighting is working okay