Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
Eur J Cell Biol: "A mechanistic protrusive-based model for 3D cell migration"
Francisco Merino-Casallo et al
Abstract
Cell migration is essential for a variety of biological processes, such as embryogenesis, wound healing, and the immune response. After more than a century of research-mainly on flat surfaces-, there are still many unknowns about cell motility. In particular, regarding how cells migrate within 3D matrices, which more accurately replicate in vivo conditions. We present a novel in silico model of 3D mesenchymal cell migration regulated by the chemical and mechanical profile of the surrounding environment. This in silico model considers cell's adhesive and nuclear phenotypes, the effects of the steric hindrance of the matrix, and cells ability to degradate the ECM. These factors are crucial when investigating the increasing difficulty that migrating cells find to squeeze their nuclei through dense matrices, which may act as physical barriers. Our results agree with previous in vitro observations where fibroblasts cultured in collagen-based hydrogels did not durotax toward regions with higher collagen concentrations. Instead, they exhibited an adurotactic behavior, following a more random trajectory. Overall, cell's migratory response in 3D domains depends on its phenotype, and the properties of the surrounding environment, that is, 3D cell motion is strongly dependent on the context.
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Biomech Model Mechanobiol: "Spatiotemporal modeling of first and second wave outbreak dynamics of COVID-19 in Germany"
Dorothee Lippold et al
Abstract
In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.
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Biochem J: "TDeciphering signal transduction networks in the liver by mechanistic mathematical modelling"
Lorenza A D'Alessandro et al
Abstract
In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.
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Nat Methods: "Exploring genomic data coupled with 3D chromatin structures using the WashU Epigenome Browser"
Daofeng Li et all
Abstract
Three-dimensional (3D) genomic structures are vital for gene regulation and cell function. High-throughput technologies based on chromosome conformation capture have been used to study genome-wide physical chromosome interactions. These interactions can be visualized as 2D heatmaps or as interaction networks decorated with genomic features. In addition, computational approaches using interaction data based on models, such as constrained physical models, polymer models and population-based analysis, have been developed to predict the physical 3D structures of chromosomes. Large consortia, such as ENCODE, Roadmap, and 4D Nucleome, have generated tens of thousands of genome-wide datasets of transcription factor binding sites and epigenetic marks across numerous cell types and tissues. Biologists wish to visually explore the connections between these genome-wide profiles and 3D genome structures, which will facilitate the generation and testing of diverse hypotheses. This presents a challenge to conventional genome browsers, where most genomic data is visualized in linear genomic coordinates. The WashU Epigenome Browser was invented in 2011 as an interactive tool for exploring genomic data in a web browser. We have now expanded the browser functions to allow investigators to visually explore 1D, 2D and 3D genomic data on a single webpage. The key innovation is to thread the linear genomic coordinates onto a multi-resolution 3D model of the chromosome.
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Biochem Genet. 2022: "Prediction of Genes Involved in Lung Cancer with a Systems Biology Approach Based on Comprehensive Gene Information"
Shahram Parvin et all
Abstract
Over the past few years, hundreds of genes have been reported in relation to lung cancer. Systems biology studies can help validate this association and find the most valid genes to use in the diagnosis and treatment. We reviewed the candidate genes for lung cancer in 120 published articles from September 1, 1993, to September 1, 2020. We obtained 134 up- and 36 downregulated genes for lung cancer in this article. The genes extracted from the articles were imported to Search Tool for the Retrieval of Interacting genes/proteins (STRING) to construct the protein–protein interaction (PPI) Network and pathway enrichment. GO ontology and Reactome databases were used for describing the genes, average length of survival, and constructing networks. Then, the ClusterONE plugin of Cytoscape software was used to analyze and cluster networks. Hubs and bottleneck nodes were defined based on their degree and betweenness. Common genes between the ClusterONE plugin and network analysis consisted of seven genes (BRCA1-TP53-CASP3-PLK1-VEGFA-MDM2-CCNB1 and PLK1), and two genes (PLK1 and TYMS) were selected as survival factors. Our drug–gene network showed that CASP3, BRCA1, TP53, VEGFA, and MDM2 are common genes that are involved in this network. Also, among the drugs recognized in the drug–gene network, five drugs such as paclitaxel, oxaliplatin, carboplatin, irinotecan, and cisplatin were examined in different studies. It seems that these seven genes, with further studies and confirmatory tests, could be potential markers for lung cancer, especially PLK1 that has a significant effect on the survival of patients. We provide the novel genes into the pathogenesis of lung cancer, and we introduced new potential biomarkers for this malignancy.
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Heart Fail Clin: "Modeling Susceptibility to Cardiotoxicity in Cancer Therapy Using Human iPSC-Derived Cardiac Cells and Systems Biology"
McKay Mullen et all
Abstract
The development of human-induced pluripotent stem cell-derived cardiac cell types has created a new paradigm in assessing drug-induced cardiotoxicity. Advances in genomics and epigenomics have also implicated several genomic loci and biological pathways that may contribute to susceptibility to cancer therapies. In this review, we first provide a brief overview of the cardiotoxicity associated with chemotherapy. We then provide a detailed summary of systems biology approaches being applied to elucidate potential molecular mechanisms involved in cardiotoxicity. Finally, we discuss combining systems biology approaches with iPSC technology to help discover molecular mechanisms associated with cardiotoxicity.
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Molecular Systems Biology: "Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. Molecular Systems Biology"
Ines Thiele et all
Abstract
Comprehensive molecular-level models of human metabolism have been generated on a cellular level. However, models of whole-body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ-specific information from literature and omics data to generate two sex-specific whole-body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole-body organ-resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter-organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co-metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.
