pseudobatch 0.0.1

Transform data from fed batch cultures to get equivalent batch culture values

# Welcome to the Pseudobatch transformation repository This repository holds the code for the Pseudobatch Python package, Excel-templates, the documentation, example datasets and the code required to reproduce simulations, analysis and figures in the article LINK-TO-ARTICLE.

## What is this? The Pseudobatch transformation is a method to transform data from fed-batch processes to ease analysis both through visual inspection and through statistical modelling. The transformation is especially useful when samples are drawn during the fed-batch fermentation.

A simple example is the following (simulated) fed-batch fermentation. During the fermentation we have simulated several samples. Typically samples are removed to have more comprehensive analysis through e.g. HPLC, transcriptomics, proteomics, metabolomics, Optical density (OD), etc.

The grey line show the simulated biomass in mass units (not concentration). The biomass curve makes sudden drops because mass is removed samples are withdrawn. The blue point are biomass measurements at the sampling point e.g. OD measurements.


A typical analysis of such fermentation data is the estimate the growth rate. This is typically done through a log-linear model, i.e. first log-transforming the biomass measurements and the fit a linear model. If we fit a log-linear model to the raw growth data, we obtain the wrong estimate of the growth rate (blue line). It visually clear that the slope of the blue line is not the same as the slope of the grey line. However, if we first apply the Pseudobatch transformation, we remove the sudden drops in mass and now we obtain the correct growth rate from the log-linear model.


## How to use? There are multiple ways to use the pseudobatch transformation.

### Excel templates Download the excel template from the [excel-pseudobatch folder](./excel-pseudobatch/) and follow the instructions in the template. NB the excel template can also be used to import data into Python (See LINK-TO-TUTORIAL).

### Python The Python package holds functions which apply the pseudobatch transformation to data either in the form of Numpy Arrays or Pandas dataframe. Please visit [our documentation]() to how to use the Python package.

## How to install? The Pseudobatch Python package can be install through PYPI using pip. ` pip install pseudobatch `

## How to cite If you use the pseudobatch transformation please cite the original article XXX.

## How to reproduce the simulations, analysis and figures from the article? The [article folder](./article/) holds the code which was used create the simulated datasets, to proof that the transformation works, analyse the error propagation and create the illustrations for the paper.

### Docker container To increase reproducibility we have provided a docker container. Inside this container all simulations and analysis can be reproduced. Instructions on how to download and use the docker container is found in the [article folder](./article/README.md).

## Building the documentation

Pseudobatch’s documentation lives in the folder docs, written in Jupyter notebooks and restructured text documents and built using [Sphinx](https://www.sphinx-doc.org).

The source files can be found in the folder docs/source.

In order to rebuild the documentation after editing, first make sure that you have installed all the dependencies by running pip install -e .’[development]’ from the project root. Next, change directory to docs and run the command make html. To view your changes run open build/html/index.html or just click through to this file using your file explorer.

## Building docker image This is a note to developers who wants to rebuild/update the docker image. If you simply want to use the docker image see the description in the [article folder](./article/README.md).

The Docker container relies on the [jupyter/datascience-notebook](https://hub.docker.com/r/jupyter/datascience-notebook/tags/), see also [here](https://jupyter-docker-stacks.readthedocs.io/en/latest/using/selecting.html#jupyter-datascience-notebook). We used this image because it includes both Python and Julia out of the box, the down side is that it is quite large. To create the image you need a local copy of this repository and inside that folder run the following command:

` docker build . -t pseudobatch:{version} `

This recreates the docker image. Be aware that installing the Julia packages and cmdstan are both take quite some time, thus expect that it take ~ 15 - 30 min to build.