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

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

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. Neuron tracking and mapping of neuronal ensembles in behaving Hydra. Read the full caption: here https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009432

ALT An illustrated collage image of double helixs, cells, and more biological sciences symbols. Color-washed in hues of purple, blue, and pink.

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 An illustrated collage image of double helixs, cells, and more biological sciences symbols. Color-washed in hues of purple, blue, and pink.

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 5. Neuron tracking and mapping of neuronal ensembles in behaving Hydra. Read the full caption: here https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009432

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 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.

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

ALT Fig 5. Latest regional nowcast by specimen date. Upper panels–instantaneous growth rate. Lower panels–nowcast modelled incidence. In the growth rate plots, the shaded regions indicate 95% confidence intervals, with the solid lines indicating median estimates. The shaded regions in the nowcasting plots are 95% prediction intervals of the model, with the solid line indicating the median nowcast.

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 1. The diagram illustrates the architecture of Trackplot. It is developed using python and JavaScript which greatly facilitates future maintenance, could visualize genomic data from a variety of sources and bioinformatic formats on the given genomic coordinates

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

ALT "EACR Conference Cellular Bases for Patient Response to Cancer Therapies 14 - 19 November 2023, Lyon"

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 1. Overview of simulation approach. Full caption available at: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010712

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

ALT Fig 2. Performance of different methods in a tree graph. The tree has 781 nodes 780 edges. Five different methods, including the ensemble inference (ENS-I), modified dynamic message-passing with a fixed transmission rate (DMP1), modified dynamic message-passing with uniformly distributed transmission rates (DMP2), number of connections (Degree), and contact with observed infections (Contact), are compared. The ROC curves for various observation rates 18.44% (A), 32.64% (B), and 55.95% (C) are shown. The corresponding AUC values are compared in (D, E, F). The numbers of infections identified among high-risk individuals selected by different methods are shown in (G, H, I).

ALT Fig 1. Overview of simulation approach. Full caption available at: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010712

ALT Fig 2. Performance of different methods in a tree graph. The tree has 781 nodes 780 edges. Five different methods, including the ensemble inference (ENS-I), modified dynamic message-passing with a fixed transmission rate (DMP1), modified dynamic message-passing with uniformly distributed transmission rates (DMP2), number of connections (Degree), and contact with observed infections (Contact), are compared. The ROC curves for various observation rates 18.44% (A), 32.64% (B), and 55.95% (C) are shown. The corresponding AUC values are compared in (D, E, F). The numbers of infections identified among high-risk individuals selected by different methods are shown in (G, H, I).

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

ALT The 10 rules broken down into the categories of planning, understanding, changes, and publishing in a colorful diagram. Caption: "Working with other people’s code is not a step by step process. As you build your understanding of the code, your goals may change and that will influence how, and whether, you make changes." Citation: Pilgrim C, Kent P, Hosseini K, Chalstrey E (2023) Ten simple rules for working with other people’s code. PLoS Comput Biol 19(4): e1011031. https://doi.org/10.1371/journal.pcbi.1011031

ALT The 10 rules broken down into the categories of planning, understanding, changes, and publishing in a vertical colorful chart. Caption reads "Working with other people’s code is not a step by step process. As you build your understanding of the code, your goals may change and that will influence how, and whether, you make changes." Citation: Pilgrim C, Kent P, Hosseini K, Chalstrey E (2023) Ten simple rules for working with other people’s code. PLoS Comput Biol 19(4): e1011031. https://doi.org/10.1371/journal.pcbi.1011031

ALT PLOS Computational Biology logo in a white circle surrounded by animated abstract images of code, molecules and atoms.

ALT PLOS Computational Biology logo in a white circle surrounded by animated abstract images of code, molecules and atoms.

ALT The 10 rules broken down into the categories of planning, understanding, changes, and publishing in a vertical colorful chart. Caption reads "Working with other people’s code is not a step by step process. As you build your understanding of the code, your goals may change and that will influence how, and whether, you make changes." Citation: Pilgrim C, Kent P, Hosseini K, Chalstrey E (2023) Ten simple rules for working with other people’s code. PLoS Comput Biol 19(4): e1011031. https://doi.org/10.1371/journal.pcbi.1011031

ALT The 10 rules broken down into the categories of planning, understanding, changes, and publishing in a colorful diagram. Caption: "Working with other people’s code is not a step by step process. As you build your understanding of the code, your goals may change and that will influence how, and whether, you make changes." Citation: Pilgrim C, Kent P, Hosseini K, Chalstrey E (2023) Ten simple rules for working with other people’s code. PLoS Comput Biol 19(4): e1011031. https://doi.org/10.1371/journal.pcbi.1011031

ALT PLOS Computational Biology logo in a white circle surrounded by animated abstract images of code, molecules and atoms.

ALT PLOS Computational Biology logo in a white circle surrounded by animated abstract images of code, molecules and atoms.