Journal: Computational and Structural Biotechnology Journal

Article Title: DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control – An efficient data analysis for cancer research

doi: 10.1016/j.csbj.2024.04.010

Figure Lengend Snippet: DataXflow. All three main steps, including JimenaE, D2D, and externalStimuli, are shown in the workflow. The input data consists of real data, such as single-cell data, which must undergo prior normalization. The output topology is crafted in yED. Subsequently, this topology is opened in JimenaE to generate scripts to use D2D, an integral part of JimenaE, for achieving the best parameter fit on the input data. An iterative topology adaption might be necessary to generate a fitting model. Once the model garners approval, the identification of drug targets using the external stimuli framework is done by generating necessary scripts automatically. To complete this process, the "mapping.file" from JimenaE and the "parameter.file" from D2D, generated post-fitting through "arExportPEtab," are essential. It's important to note that D2D and external stimuli require access to MATLAB. The normalization is conducted using the provided Python script, and all relevant scripts are accessible in the git repository under https://github.com/MarvelousHopefull/DataXflow . Created with BioRender.com.

Article Snippet: In the Supplement, we explain how to use the JimenaE GUIs to set up scripts to implement this optimization procedure into MATLAB scripts that can be executed in the D2D framework and explain their components, in particular how they are related to the optimization problem described above.

Techniques: Generated

Journal: Computational and Structural Biotechnology Journal

Article Title: DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control – An efficient data analysis for cancer research

doi: 10.1016/j.csbj.2024.04.010

Figure Lengend Snippet: Setup scripts for D2D. Start script is required for the first fitting of the model. Advanced script can be run after a first good fitting of the model. The difference in both scripts is the read-in of the initial values. In the start script (left), the starting values are sourced from the outputs provided by JimenaE. In the advanced script (right), we replace these initial values found in the “initValues” file with the best-fit parameters obtained from the previous run, which are stored in the PEtab folder as parameter.tsv. This process generates an updated “update_arSetPars” file. All explanations of the respective functions are included in the text.

Article Snippet: In the Supplement, we explain how to use the JimenaE GUIs to set up scripts to implement this optimization procedure into MATLAB scripts that can be executed in the D2D framework and explain their components, in particular how they are related to the optimization problem described above.

Techniques:

Journal: Computational and Structural Biotechnology Journal

Article Title: DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control – An efficient data analysis for cancer research

doi: 10.1016/j.csbj.2024.04.010

Figure Lengend Snippet: External stimuli ground state. A) Best-fitting topology of an NSCLC cell line H358 . B) The model’s findings reveal an increase in cellular proliferation and a concomitant reduction in apoptotic events. C) Set up flags in the MATLAB script generated by JimenaE to find external stimuli.

Article Snippet: In the Supplement, we explain how to use the JimenaE GUIs to set up scripts to implement this optimization procedure into MATLAB scripts that can be executed in the D2D framework and explain their components, in particular how they are related to the optimization problem described above.

Techniques: Generated