* 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