ALT Fig 1. Summary of the Ten Simple Rules presented in this manuscript.

ALT Silhouette of people standing next to one another against a white background, with images of data science symbols.

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 2. Sample label. The first line includes a unique computer-readable barcode as well as a human-readable computer-generated sample identification number. The second and third lines include a description of the sample content, the date, and the identity of the inoculum.

ALT Fig 2. Sample label. The first line includes a unique computer-readable barcode as well as a human-readable computer-generated sample identification number. The second and third lines include a description of the sample content, the date, and the identity of the inoculum.

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 1. TRPV3 protomer domain structure embedded in a lipid membrane cross-section with the intracellular region below and extracellular region above.

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 1. TRPV3 protomer domain structure embedded in a lipid membrane cross-section with the intracellular region below and extracellular region above.

ALT "EACR Conference Cellular Bases f Patient Response to Cancer Therapies 14-16 November 2023, Lyon"

ALT Fig 1. Maximum entropy degree distributions with predicted numbers of interactions. (a) Probability density of the mean degree of a food web obtained using different values of species richness S. The number of interactions L was simulated 1000 times using the flexible links model fitted to all empirical networks (Eq (19)). The mean degrees 2L/S were then obtained from these simulated values. (b) Degree distributions of maximum entropy for a network of S = 27 species and different numbers of interactions. The numbers of interactions correspond to the lower and upper bounds of the 67%, 89%, and 97% percentile intervals (PI), as well as the median (in blue), of the counterfactuals of the flexible links model. Each degree distribution of maximum entropy was obtained using Eq (15) after solving numerically Eq (16) using different values of the mean degree constraint.

ALT Fig 5. Value RNN dynamics resembled belief dynamics in each task. Full image caption available at: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011067

ALT Fig 6. Distortion in trajectory inference and continuous relationships. (a) Velocyto RNA velocity embeddings for UMAPs made with 17 or 50 n_neighbors. Cell types of interest highlighted in gray. (b) Velocyto RNA velocity embeddings for t-SNEs made with perplexity of 17 or 50.

ALT Fig 6. Distortion in trajectory inference and continuous relationships. (a) Velocyto RNA velocity embeddings for UMAPs made with 17 or 50 n_neighbors. Cell types of interest highlighted in gray. (b) Velocyto RNA velocity embeddings for t-SNEs made with perplexity of 17 or 50.

ALT Fig 2. Increasing the number of commonly represented features decreases the assignment error rate, but increases the level of redundancy between the representations. Gull caption available at: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011327

ALT Fig 1. Population dynamics of the resistant (orange) and sensitive (black) strains. The sensitive subpopulation declines almost deterministically during treatment, which is administered for seven days (gray shaded region). At the same time the resistant subpopulation, if it survives, increases in size. There is strong variation between the resistant growth curves because of stochastic birth and death events. Non-surviving trajectories are omitted. After treatment, both subpopulations grow until the overall pathogen population reaches the carrying capacity, which happens approximately two days after the end of treatment when the resistant subpopulation reaches its maximum. The overall size of the resistant subpopulation at that point shows variation among the ten sample trajectories, again because of demographic stochasticity. This variation carries over to the time at which the resistant subpopulation is outcompeted by the sensitive subpopulation in the absence of antibiotic treatment.

ALT Fig 5. Summary of results and methodology used. Full photo caption available at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011329