Assays

Calculation of control and response coefficients. Generates Fig. 5, Fig. S4, and Table S2 in the associated publication.

Download "Model_notebooks.rar", unzip, and run: "8, Fig5-control-coefficients-[needs-(1)]-20221109.nb", "9, TableS2-response-coefficients-[needs-(1)]-20230302.nb", and "15, FigS4-control-coefficients-low-AcetylCoA-[needs-(1)]-20221109.nb" after running "1, generate-model-20221109.nb"

Creation of personalised models of control, symptomatic MCADD, asymptomatic MCADD, and early diagnosis MCADD individuals using fibroblast proteomics to adjust model Vmaxes. Generates Fig. 7 and S7.

Download and unzip "Model_notebooks.rar" and run "13, Fig7-personalised-models-[needs-(1-and-12)]-20221109.nb" after running "1, generate-model-20221109.nb" and "12, Fig7-S7-preprocessing-[needs-(1)-]-20221109.nb".

Validation of model's ability to predict oxygen consumption flux as measured usign permeabilised cells in an Oroboros Oxygraph. Generates Fig. 2A in the associated publication.

Download "Model_notebooks.rar", unzip, and run: "2, generate-model-Oroboros-validation-[needs(1)]-20221109.nb" and "4, Fig2A-Oroboros-simulation-data-[needs-(1-2-and-3)]-20221109.nb" after running "1, generate-model-20221109.nb"

Validation of model's ability to predict whole-body ketogeneic flux as extracted form Fletcher et al. (2019). Generates Fig. 2B in the associated publication.

Download "Model_notebooks.rar", unzip, and run: "2, generate-model-Oroboros-validation-[needs(1)]-20221109.nb" and "5, Fig2B-ketogenesis-validation-[needs-(1)]-20221109.nb" after running "1, generate-model-20221109.nb"

Testing the model's ability to predict palmitoyl-CoA and octanoyl-CoA dehydrogenation in human liver lysate, with and without anti-MCAD and anti-VLCAD antibodies. Generates Fig. 2 C and D in the associated publication. Data from Aoyama et al. (1995).

Downoad and unzip "Model_notebooks.rar" and run "6, Fig2C+D-ACAD-partitioning-validation-[needs-(1)]-20221109.nb" after running "1, generate-model-20221109.nb".

Prepared multimeric tubulin protein receptors for docking studies, generated by alignment of two identical models of alpha-tubulin on alpha-tubulin chains of two neighboring protofilaments.

Docking results of trifluraline and dinitroaniline-etherphospholipid hybrids against different kinetoplastid alpha-tubulin receptors with an induced fit docking routine. The docking protocol involves an initial crude ligand placement step, subsequent receptor optimization in response to ligand binding, and another docking step into the optimized receptor.

Creation of homology models of various tubulins from dinitroaniline-sensitive and -resistant species, and a comparative analysis of their electrostatic potential grids overall and in putative binding site regions using PIPSA (Protein Interaction Property Similarity Analysis).

Alignments of various alpha-tubulin and beta-tubulin sequences from dinitroaniline-sensitive and dinitroaniline-resistant species. Sequences were retrieved from UniProt with the identifiers listed below and subjected to a multiple sequence alignment using ClustalOmega (ebi.ac.uk/Tools/msa/clustalo/; ClustalOmega webserver, last accessed 16-02-23):

alpha-tubulin:

• [dinitroaniline sensitive] T. cruzi - Q27352; T. brucei brucei - Q4GYY5; L. infantum - ...

Incrementally increase the activity of some target rescue enzymes from 20% of default expression to 200% of default expression in a control and MCADD model to see if flux and CoASH concentration are rescued. Generates Fig. 6, S5, and S6.

Download "Model_notebooks.rar", unzip, and run "11, Fig6+S5-rescues-[needs-(1-and-10)]-20221109.nb", "10, Fig6B-inset-rescues-(low-acetylCoA)-[needs-(1)]-20221109.nb", and "16, FigS6-rescues-20221109-fixed-[needs-(1)]-CoASH.nb" after running "1, generate-model-20221109.nb" ...

Based on odendaal1, a control model is made and compared to model deficient for short-chain acyl-CoA dehydrogenase (SCADD, 0%), medium-chain acyl-CoA dehydrogenase (MCADD, 0%), and very long-chain acyl-CoA dehydrogenase (VLCADD, 10%). With and withou metabolite partitioning, and with a fixed mitohondrial free CoASH. Generates Figures 3, 4, S1, S2, and S3 in the related paper.

Download "Model_notebooks.rar", unzip, and run: "7, Fig3+4+S1+S3-ACADDs-[needs-(1)]-20221109.nb" and "14, ...

Generic metadata template describing High Content Screening data that conform to the REMBI and ISA specification. LEI-MIHCSME empty template that can serve as basis for filling in metadata. This template was created and modified from templates produced by Leiden University.

MIHCSME template example for IDR0022 dataset. Primary and validation RNAi screen using smartpool and single siRNAs to identify cell migratory regulators in Hs578T and MDA-MB-231 triple-negative breast cancer cells.

RNAi Screen of 100 candidate genes predicted to be involved in mitotic chromosome condensation. MIHCSME template example for IDR0002 dataset.

Compound screen on HepG2 CHOP-GFP reporter, to measure CHOP-GFP protein induction upon treatment with compounds. Template and associated files describe High Content Screening experimental data that conform to the MIHCSME specification.

Compound screen on 13 HepG2 -GFP reporter lines, to measure GFP protein induction, and cell death induction. Template and associated files describe High Content Screening experimental data that conform to the MIHCSME specification.

The statistical analysis was performed in a jupyter notebook. This notebook contains the commands for all performed analyses (Statistical_analysis_of_FAIR_citations.ipynb)

The Bayesian Estimation Superseeds the t Test (BEST) method of Kruschke 2013 was used for the Bayesian significance testing. The method was implemented in a python class together with visualization and distributional analysis methods (BEST_method_python_Kruschke2012.py). Also the bayesian multiple comparison analysis can be ...