https://github.com/matthiaskoenig/methacetin_fitting
https://github.com/matthiaskoenig/methacetin_fitting
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/matthiaskoenig/methacetin_fitting
- Owner: matthiaskoenig
- Created: 2017-12-05T16:51:36.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-11-15T11:21:24.000Z (over 5 years ago)
- Last Synced: 2025-01-04T20:24:38.021Z (5 months ago)
- Language: HTML
- Size: 269 MB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# LiMAx fitting
Information, data, models and fitting results for the LiMAx model.
## Model
All models are SBML models stored in the `model` folder.
```
./model//limax_.xml
```
A HTML model report is provided next to the models as
```
./model//limax_.html
```
For every model reference simulations have been performed in `R` and `python`
available in the
```
./model//results
```
folder.
## Datasets
The corresponding datasets are in the `data` folder, named in line with the model as tab separated files (`tsv`)
```
./data//limax_52_data.tsv
```
## Fitting
Fitting is performed with `dMod`
### Setup
A dockerized version of dmod is available from
https://github.com/matthiaskoenig/dmod-docker
Which allows simple setup of the environment to run the parameter fitting.
Alternatively the required dependencies can be installed via
```
# bash
sudo -E add-apt-repository -y ppa:marutter/rrutter
sudo -E apt-get update
sudo -E apt-get -y install openjdk-11-jdk
sudo -E apt-get -y install r-base-core r-base r-base-dev r-recommended libssl-dev libcurl4-openssl-dev libxml2-dev liblzma-dev lzma lzma-dev
# R
sudo RUN R CMD javareconf
sudo R
sudo R -e 'install.packages("devtools")'
sudo R -e 'devtools::install_github("dkaschek/cOde")'
sudo R -e 'devtools::install_github("dkaschek/dMod")'
sudo R -e 'devtools::install_github("dlill/conveniencefunctions")'
```
# Optimization problem
**Parameters to set**
For the simulation a subset of parameters must be set. These are provided as
separate columns in the `data` spreadsheet.
```python
BW # [kg] body weight
PODOSE_apap # [mg] oral dose apap
IVDOSE_apap # [mg] intravenous dose apap
PODOSE_co2c13 # [mg] oral bicarbonate dose
IVDOSE_co2c13 # [mg] iv bicarbonate dose
Ri_co2c13 # [?] injection rate bicarbonate
ti_co2c13 # [?] injection duration bicarbonate
PODOSE_metc13 # [mg] oral dose methacetin
IVDOSE_metc13 # [mg] iv dose methacetin
ti_metc13 # [?] injection duration methacetin
```
**Observer Function**
The following observer functions are used in the model
```python
Mve_apap = Mve_apap # [mg/dl] paracetamol concentration plasma
Mve_metc13 = Mve_metc13 # [mg/dl] methacetin concentration plasma
DOB = DOB # [‰] Delta over baseline
P_CO2F = P_CO2Fc13 - init_P_CO2Fc13 # co2c13 fraction corrected for baseline
mom_rec_co2c13 = Exhalation_co2c13/60*Mr_co2c13/Ri_co2c13*100.0 # [%] recovery after continuous IV injection
mom_rec_metc13 = Exhalation_co2c13/(init(PODOSE_metc13)/Mr_metc13) * 100 # [% dose/h] momentary recovery
cum_rec_metc13 = Abreath_co2c13/(init(PODOSE_metc13)/Mr_metc13) * 100 # [% dose] cumulative recovery
```
### Free Parameters
```python
# apap
Kp_apap # [-] partition coefficient tissue/plasma apap
Vp_apap # [1/hr] partition velocity apap
Ka_apap # [1/hr] absorption rate apap from gut (appearance after oral dose)
APAPD_CL # [?] Vmax hepatic clearance (
APAPD_Km_apap # [mM] Km value for apap
# bicarbonate and co2
Kp_co2c13 # [-] partition coefficient tissue/plasma bicarbonate
Vp_co2c13 # [1/hr] partition velocity bicarbonate
Ka_co2c13 # [1/hr] absorption rate bicarbonate gut (appearance after oral dose)
EXHCO2_CL # [mmole_per_min] Vmax CO2 rate of disposal in air
EXHCO2_Km_co2 # [mM] Km CO2 rate of disposal in air
# methacetin
Kp_metc13 # [-] partition coefficient tissue/plasma 13C-methacetin
Vp_metc13 # [1/hr] partition velocity 13C-methacetin
Ka_metc13 # [1/hr] absorption rate methacetin gut (appearance after oral dose)
CYP1A2MET_CL # [] Vmax hepatic clearance methacetin
CYP1A2MET_Km_met # [mM] Km value clearance for methacetin
```