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ALT Fig 1. Example of a fuzzy logic-based model representing the repressilator, an oscillatory genetic regulatory network [10]. (a) Schematic representation of the variables present in the model and their interactions. (b) Fuzzy sets representing each of the variable’s states (only LacI is shown). (c) Fuzzy rule base of the model. (d) Resulting oscillatory dynamic of the fuzzy model. We partially adapted these figures from [15], where the original figures were released under the CC BY-NC 4.0 DEED (Creative Commons Attribution-NonCommercial 4.0 International) license.
ALT Fig 5. Purine biosynthesis is saved from gene knockouts by external amino acids. This figure shows an abbreviated view of purine synthesis in B. fragilis. The nitrogen in purine bases is taken from amino acids. These amino acids are produced in transamination reactions involving TCA intermediates. Thus, the gene knockouts corresponding to these transamination reactions (red Xs) are lethal, as the production of key amino acids and all purines is halted. However, if amino acids are made available in the environment (blue arrows), purine biosynthesis is able to proceed. Pyrimidine production is also aspartate dependent, so it is rescued similarly.
ALT Fig 5. Principal component, allele frequency and linkage disequilibrium (LD) analyses of artificial genomes with 65,535-SNP size. a) Density plot of the PCA of combined real genomes and artificial genomes generated by WGAN and CRBM. Density increases from red to blue. b) Allele frequency correlation between real and artificial genome datasets. Bottom figures are zoomed at low frequency alleles (from 0 to 0.2 overall frequency in the real dataset). Values presented inside the figures are Pearson’s r, ordinary least squares regression slope and intercept. The dashed black line is the identity line. c) LD decay approximation for real (grey), WGAN generated (blue) and CRBM generated (red) genomes (see Materials and methods for details). d) Nearest neighbour adversarial accuracy (AATS) of artificial genomes generated by different models for the 65,535-SNP dataset. Values below 0.5 (black line) indicate overfitting and values above indicate underfitting.
ALT Fig 8. CNA-associated drug resistance with CONGAS+. CONGAS+ application to a prostate cancer dataset from [55], composed of a mixture of four cell lines with 7600 scRNA-seq cells and 8800 scATAC-seq cells. Full caption available here: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011557
ALT Fig 4. Participants’ reaching and target selection behavior across bias angles. A: Reach angle error as a function of trial number (in consecutive 10-trial bins). Solid curves show empirical results (mean ± SEM across participants) and dashed curves show model fits. B: Reach angle error as a function of the deviation from the participants’ preferred direction for probe trials. Solid curves show empirical results (45° bins, mean ± SEM across participants) and dashed curves show model fits. C: Change in target selection error as a function of trial number (in consecutive 10-trial bins). Solid curves show empirical results (mean ± SEM; for clarity SEM only shown at the end of each block) and dashed curves show model fits. D: Mean change in target selection error across the experiment. Colored dots show individual participant’s data. Black solid curve shows mean ± SEM across participants. Black dashed curve shows mean of model fits.
ALT Fig 5. Purine biosynthesis is saved from gene knockouts by external amino acids. This figure shows an abbreviated view of purine synthesis in B. fragilis. The nitrogen in purine bases is taken from amino acids. These amino acids are produced in transamination reactions involving TCA intermediates. Thus, the gene knockouts corresponding to these transamination reactions (red Xs) are lethal, as the production of key amino acids and all purines is halted. However, if amino acids are made available in the environment (blue arrows), purine biosynthesis is able to proceed. Pyrimidine production is also aspartate dependent, so it is rescued similarly.
ALT Fig 5. Principal component, allele frequency and linkage disequilibrium (LD) analyses of artificial genomes with 65,535-SNP size. a) Density plot of the PCA of combined real genomes and artificial genomes generated by WGAN and CRBM. Density increases from red to blue. b) Allele frequency correlation between real and artificial genome datasets. Bottom figures are zoomed at low frequency alleles (from 0 to 0.2 overall frequency in the real dataset). Values presented inside the figures are Pearson’s r, ordinary least squares regression slope and intercept. The dashed black line is the identity line. c) LD decay approximation for real (grey), WGAN generated (blue) and CRBM generated (red) genomes (see Materials and methods for details). d) Nearest neighbour adversarial accuracy (AATS) of artificial genomes generated by different models for the 65,535-SNP dataset. Values below 0.5 (black line) indicate overfitting and values above indicate underfitting.