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Boolean models of CRC progression calibrated with patient data. Models are developed in a software tool CellNetAnalyzer (CNA). The models uploaded here are ready to simulate in CNA. To gain an in-depth understanding of mechanisms underlying CRC tumorigenesis as well as to identify novel drug targets, we constructed a molecular interaction map (MIM) combining key signaling pathways from the cancer cell and the immune cells in the microenvironment. The MIM was dynamically simulated using stimulus-response analysis for two CRC patient datasets GSE1323 (primary tumor to metastasis progression) and GSE8671 (normal colonic mucosa to colorectal adenomas progression), here used as experimental conditions, and the analysis revealed disease signatures for each condition. More specifically, the identified disease signatures for the dataset GSE1323 were: (1) simultaneous activation of TNF/TNFRSF1A,B; EGF/EGFR and inactivation of ERE/ESR, (2) simultaneous activation of TNF/TNFRSF1A,B; TLR4 and inactivation of ERE/ESR, and the signature for the dataset GSE8671 was simultaneous activation of TNF/TNFRSF1A,B, and TLR41. Further, in silico perturbation analyses were performed and identified that concurrently inhibiting: (1) MAPK3 and STAT3 in case of the GSE1323, (2) ELK1/ATF2 and STAT3, or (3) MAPK14 and STAT3 in case of GSE8671, can achieve significant anti-cancer effects (i.e., lower EMT, proliferation and inflammation, and increase apoptosis). Disease signatures and therapeutic targets were validated through patient data using Kaplan Meier survival analysis and several machine learning methods. In conclusion, this holistic study mimics known characteristics of cancer, systematically predicts disease signatures and therapeutic targets, and can be applied to other immunogenic cancers.

Background A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.e. expanded, corrected, and refined. Even small changes in the encoding of a model can, however, significantly affect its interpretation. Our motivation for the present study is to identify changes in models and make them transparent and traceable. Methods We analysed 13734 models from BioModels Database and the Physiome Model Repository. For each model, we studied the frequencies and types of updates between its first and latest release. To demonstrate the impact of changes, we explored the history of a Repressilator model in BioModels Database. Results We observed continuous updates in the majority of models. Surprisingly, even the early models are still being modified. We furthermore detected that many updates target annotations, which improves the information one can gain from models. To support the analysis of changes in model repositories we developed MoSt, an online tool for visualisations of changes in models. The scripts used to generate the data and figures for this study are available from GitHub github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at hub.docker.com/r/binfalse/bives-statsgenerator. The website most.bio.informatik.uni-rostock.de provides interactive access to model versions and their evolutionary statistics. Conclusion The reuse of models is still impeded by a lack of trust and documentation. A detailed and transparent documentation of all aspects of the model, including its provenance, will improve this situation. Knowledge about a model’s provenance can avoid the repetition of mistakes that others already faced. More insights are gained into how the system evolves from initial findings to a profound understanding. We argue that it is the responsibility of the maintainers of model repositories to offer transparent model provenance to their users. © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Many biosynthetic gene clusters (BGCs) in the genomes of environmental microorganisms require heterologous expression in order to realize their genetic potential, including cryptic and metagenomic BGCs. Streptomyces coelicolor M1152 is a widely used host strain for the heterologous expression of BGCs, as it has been genetically engineered for this purpose via the deletion of four of its native biosynthetic gene clusters (BGCs) and the introduction of a point mutation in the rpoB gene that encodes the beta subunit of RNA polymerase. This latter mutation was shown to have a strong positive impact on antibiotic biosynthesis via processes that remain poorly understood. Therefore, a systemic understanding of the consequences on cellular metabolism of the genomic changes of M1152 could greatly contribute to this understanding. Here we carried out a comparative analysis of M1152 and its ancestor strain M145, connecting observed phenotypic differences to changes in transcript and protein abundance. Measured protein abundance was used to constrain an amended genome-scale model (GEM) and to predict metabolic fluxes. This approach connects observed differences in growth rate and glucose consumption to changes in central carbon metabolism, accompanied by differential expression of important regulons. Our results suggest that precursor availability is not limiting the biosynthesis of secondary metabolites. This implies that alternative strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds. Importance This study provides the first systems description of S. coelicolor M1152, an engineered host widely used for the heterologous expression of BGCs directing the synthesis of natural products. By combining time-series proteomics and transcriptomics, batch fermentation data and genome-scale modelling, we can connect observed phenotypes to known genetic modifications and find extensive metabolic rewiring in the M1152 strain compared to the wild-type stain M145. Our study indicates that the deletion of secondary metabolite biosynthetic pathways thought to enhance precursor availability, only has a minor impact on the ability of the modified strain to produced heterologous molecules. In contrast, the rpoB mutation is likely responsible for the most dramatic changes in regulatory features and precursor availability. The amended genome-scale model, reconstructed in an open-science framework, allowed us to contextualize the transcriptional changes. This framework facilitates further development by the research community in an organized manner, including version control, continuous integration and quality control and tracking of individual contributions.

Clustering properties results. These properties include connectivity, Dunn and Silhouette scores. These scores suggest the sufficient clustering method by a specific number of clusters. (DOCX 16 kb)

Fitted power law distribution. The Degree distribution of each network has been compared to the power law distribution in order to visualize the scale free property in the structure of each network. (PDF 203 kb)

Visual assessment of cluster tendency plots. Each rectangular represents the clusters of the calculated results of the centrality measures. (PDF 313 kb)

Contribution values of centralities in each network. These values were computed based on the principal components. The red line shows the threshold used for identifying effective centralities. (PDF 441 kb)

Optimal number of clusters. The suitable number of clusters for hierarchical clustering method was computed using the average silhouette values. (PDF 321 kb)

Clusters silhouette plots. Each color represents a cluster and each bar with specific color indicates a centrality. (PDF 417 kb)

Scatterplots between groups of centralities. Each panel indicates scatterplots between centralities groups of two networks. (PPTX 1963 kb)