contains code and data accompanying

Computational modeling of stem and progenitor cell kinetics identifies plausible hematopoietic lineage hierarchies

Lisa Bast^1,2,3,*, Michèle C. Buck^4,*, Judith S. Hecker⁴, Robert A.J. Oostendorp⁴, Katharina S. Götze^4,6,+ and Carsten Marr^1,2,+

<sub><sup>1Helmholtz Zentrum München–German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.
2Technical University of Munich, Department of Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany.
3Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
4Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Internal Medicine III, Munich, Germany.
5Institute of Microbiology, Technische Universität München, Munich, Germany.
6German Cancer Consortium (DKTK), Heidelberg, Partner Site Munich.
* Equal contribution
+ Joint corresponding authors</sup></sub>

which can be accessed via https://www.cell.com/iscience/fulltext/S2589-0042(21)00088-2

required software:

• MATLAB R2017a
• Python 3.7.6

MATLAB toolboxes:

• [PESTO] 1.1.0 (https://github.com/ICB-DCM/PESTO/)
• [AMICI] 0.10.7 (https://github.com/ICB-DCM/AMICI)
• [STRIKEGOLDD] 2.1 (https://github.com/afvillaverde/strike-goldd_2.1)

which are already included in folder ./MATLAB/toolboxes. Note that AMICI uses .mex files and requires MinGW as C/C++ compiler. If you have not used mex with MATLAB before you might need to set it up first (by following these instructions.

Python tools:

• pandas 1.0.1
• numpy 1.18.1
• seaborn 0.10.0
• matplotlib 3.1.3
• scipy 1.4.1

a) Go to folder ./MATLAB/model_comparison_analysis and run intermediate_states_main(). Settings can be adapted in getIntermediateStatesSettings.m.

b) For Results visualization run jupyter notebook results_visualization_intermediate_states.ipynb.

a) Go to folder ./MATLAB/model_comparison_analysis and run lineage_hierarchies_main(). Settings can be adapted in getLineageHierarchySettings.m.

b) For Results visualization run jupyter notebook results_visualization_lineage_hierarchies.ipynb.

To perform structural identifiability analysis go to .MATLAB/structural_identifiability_analysis and run structural_identifiability_main(). Settings and paths can be updated iun getSISettings.m if necessary.

a) To perform in silico model selection go to folder ./MATLAB/in_silico_analysis, specify settings in getInSilicoSettings.m and run in_silico_main().

b) For Results visualization go to folder ./Python and run jupyter notebook results_visualization_in_silico_analysis.ipynb

In general, to change settings regarding optimization such as number of multistarts or number of workers (to run code in parallel) go to folder ./MATLAB/utils and make changes in getOptimizationSettings.m