Evolutionary conserved bistable motifs.

1<p>p-value is based on Fisher exact test for enrichment of MH genes within bistable motifs.</p

Evolutionary conservation of MH genes and interactions.

<p>Human genes and interactions are classified as evolutionary conserved or non-conserved using inter-species conservation cutoff of 70% (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036488#s4" target="_blank">Materials and Methods</a>) for human protein-coding genes (HomoloGene), protein and gene regulatory interactions (KEGG and Reactome). The p-values are based on Fisher exact test for enrichment of MH compared to non-MH genes or interactions. Enrichment is tested for all available protein-coding genes and/or protein interactions (All); for cell division and mitosis-focused genes and/or interactions from KEGG MIN (KEGG MIN); and for the regulatory genes and/or interactions from KEGG MIN (Regulatory KEGG MIN).</p

Mirrored trees.

<p>Comparison of similarity of mitotic hit genes regarding their location in KEGG MIN and the phenoprints observed after gene silencing. Left side tree represents shortest path between MH genes in the KEGG MIN; right side tree represents similarity measure for phenoprints of MH genes based on Hausdorff distance. A line connects a gene in a pair of corresponding clusters of both trees. Thickness of the line reflects the depth in the tree, on which considered gene remains within corresponding clusters on both sides.</p

Relation between network and phenotypic distances in the KEGG MIN.

<p>Network and phenotypic distances for all pairs of MH genes in KEGG MIN were calculated (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036488#s4" target="_blank">Materials and Methods</a>). The range of values for MH network distances is depicted on x-axis (Network distance). For each element <i>d</i> of this range the set of phenotypic distances was split into two parts: group I consists of phenotypic distances with corresponding network distance lower or equal to <i>d</i>; and group II consists of phenotypic distances with corresponding network distance greater than <i>d</i>. A) One-sided Wilcoxon rank-sum test was performed to determine whether the values of phenotypic distances in the first group are lower than in the second group. P-values are shown on the y-axis (p-value). The range of statistically significant values (***: p-value<0.001) is depicted by a horizontal bar. The dotted vertical bar indicates the median of observed network distances for MH genes. B) Table containing p-values displayed above and the number of MH genes in group I, absolute, and relative to the total number of genes in group I and II, respectively, for all analyzed values of network distance.</p

Aggregation of evolutionary conserved bistable motifs.

<p>Aggregation of instances of bistable motifs forms a cluster in the evolutionary conserved KEGG molecular interaction network. The node labels correspond to the official gene symbols. MH genes are depicted as red nodes. Activation and inhibition edges are depicted in red and blue, respectively.</p

Concept of evolutionary motif conservation.

<p>Phylogenetic tree for different species and corresponding species-specific networks of conserved gene regulatory interactions compared to Homo sapiens. A network motif shared between regulatory networks in human and other species is highlighted in red.</p

Fragmentation analysis of evolutionary conserved networks.

<p>GCS¹: giant component size.</p><p>s.d.²: standard deviation.</p><p>p-value³: p-value based on the Z-score of the GCS after MH removal.</p

DataSheet_2_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.pdf

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.</p

DataSheet_1_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.xlsx

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.</p