Modelling Biological Systems: A New Algorithm for the Inference of Boolean Networks

Biological systems are commonly constituted by a high number of interacting agents. This great dimensionality hinders biological modelling due to the high computational cost. Therefore, new modelling methods are needed to reduce computation time while preserving the properties of the depicted systems. At this point, Boolean Networks have been revealed as a modelling tool with high expressiveness and reduced computing times. The aim of this work has been to introduce an automatic and coherent procedure to model systems through Boolean Networks. A synergy that harnesses the strengths of both approaches is obtained by combining an existing approach to managing information from biological pathways with the so-called Nested Canalising Boolean Functions (NCBF). In order to show the power of the developed method, two examples of an application with systems studied in the bibliography are provided: The epithelial-mesenchymal transition and the lac operon. Due to the fact that this method relies on directed graphs as a primary representation of the systems, its applications exceed life sciences into areas such as traffic management or machine learning, in which these graphs are the main expression of the systems handled.

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Modulating Tumor Cell Functions by Tunable Nanopatterned Ligand Presentation

Nanomaterials ◽

10.3390/nano10020212 ◽

2020 ◽

Vol 10 (2) ◽

pp. 212

Author(s):

Katharina Amschler ◽

Michael P. Schön

Keyword(s):

Extracellular Matrix ◽

Tumor Cell ◽

Cell Fate ◽

Epithelial Mesenchymal Transition ◽

Model Systems ◽

Cellular Functions ◽

Biophysical Parameters ◽

Ligand Density ◽

Mesenchymal Transition ◽

Cell Functions

Cancer comprises a large group of complex diseases which arise from the misrouted interplay of mutated cells with other cells and the extracellular matrix. The extracellular matrix is a highly dynamic structure providing biochemical and biophysical cues that regulate tumor cell behavior. While the relevance of biochemical signals has been appreciated, the complex input of biophysical properties like the variation of ligand density and distribution is a relatively new field in cancer research. Nanotechnology has become a very promising tool to mimic the physiological dimension of biophysical signals and their positive (i.e., growth-promoting) and negative (i.e., anti-tumoral or cytotoxic) effects on cellular functions. Here, we review tumor-associated cellular functions such as proliferation, epithelial-mesenchymal transition (EMT), invasion, and phenotype switch that are regulated by biophysical parameters such as ligand density or substrate elasticity. We also address the question of how such factors exert inhibitory or even toxic effects upon tumor cells. We describe three principles of nanostructured model systems based on block copolymer nanolithography, electron beam lithography, and DNA origami that have contributed to our understanding of how biophysical signals direct cancer cell fate.

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An Integrated View of Virus-Triggered Cellular Plasticity Using Boolean Networks

Cells ◽

10.3390/cells10112863 ◽

2021 ◽

Vol 10 (11) ◽

pp. 2863

Author(s):

Jenny Paola Alfaro-García ◽

María Camila Granados-Alzate ◽

Miguel Vicente-Manzanares ◽

Juan Carlos Gallego-Gómez

Keyword(s):

Cell Biology ◽

Epithelial Mesenchymal Transition ◽

Boolean Networks ◽

Cellular Plasticity ◽

Mortality And Morbidity ◽

Mesenchymal Transition ◽

Logical Operators ◽

Cell Tissue ◽

Resource Exhaustion ◽

Modeling Data

Virus-related mortality and morbidity are due to cell/tissue damage caused by replicative pressure and resource exhaustion, e.g., HBV or HIV; exaggerated immune responses, e.g., SARS-CoV-2; and cancer, e.g., EBV or HPV. In this context, oncogenic and other types of viruses drive genetic and epigenetic changes that expand the tumorigenic program, including modifications to the ability of cancer cells to migrate. The best-characterized group of changes is collectively known as the epithelial–mesenchymal transition, or EMT. This is a complex phenomenon classically described using biochemistry, cell biology and genetics. However, these methods require enormous, often slow, efforts to identify and validate novel therapeutic targets. Systems biology can complement and accelerate discoveries in this field. One example of such an approach is Boolean networks, which make complex biological problems tractable by modeling data (“nodes”) connected by logical operators. Here, we focus on virus-induced cellular plasticity and cell reprogramming in mammals, and how Boolean networks could provide novel insights into the ability of some viruses to trigger uncontrolled cell proliferation and EMT, two key hallmarks of cancer.

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Fbxw7 is a driver of uterine carcinosarcoma by promoting epithelial-mesenchymal transition

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1911310116 ◽

2019 ◽

Vol 116 (51) ◽

pp. 25880-25890 ◽

Cited By ~ 6

Author(s):

Ileana C. Cuevas ◽

Subhransu S. Sahoo ◽

Ashwani Kumar ◽

He Zhang ◽

Jill Westcott ◽

...

Keyword(s):

Endometrial Cancer ◽

Epithelial Mesenchymal Transition ◽

Genetic Model ◽

Precancerous Lesions ◽

Lineage Tracing ◽

Model Systems ◽

Cancer Type ◽

Cell Of Origin ◽

Uterine Carcinosarcoma ◽

Mesenchymal Transition

Uterine carcinosarcoma is an aggressive variant of endometrial carcinoma characterized by unusual histologic features including discrete malignant epithelial and mesenchymal components (carcinoma and sarcoma). Recent studies have confirmed a monoclonal origin, and comprehensive genomic characterizations have identified mutations such asTp53andPten. However, the biological origins and specific combination of driver events underpinning uterine carcinosarcoma have remained mysterious. Here, we explored the role of the tumor suppressorFbxw7in endometrial cancer through defined genetic model systems. Inactivation ofFbxw7andPtenresulted in the formation of precancerous lesions (endometrioid intraepithelial neoplasia) and well-differentiated endometrioid adenocarcinomas. Surprisingly, all adenocarcinomas eventually developed into definitive uterine carcinosarcomas with carcinomatous and sarcomatous elements including heterologous differentiation, yielding a faithful genetically engineered model of this cancer type. Genomic analysis showed that most tumors spontaneously acquiredTrp53mutations, pointing to a triad of pathways (p53, PI3K, and Fbxw7) as the critical combination underpinning uterine carcinosarcoma, and to Fbxw7 as a key driver of this enigmatic endometrial cancer type. Lineage tracing provided formal genetic proof that the uterine carcinosarcoma cell of origin is an endometrial epithelial cell that subsequently undergoes a prominent epithelial–mesenchymal transition underlying the attainment of a highly invasive phenotype specifically driven by Fbxw7.

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Resistance to BET inhibitors in lung adenocarcinoma is mediated by casein kinase phosphorylation of BRD4

Oncogenesis ◽

10.1038/s41389-021-00316-z ◽

2021 ◽

Vol 10 (3) ◽

Author(s):

Jack Calder ◽

Amy Nagelberg ◽

Jennifer Luu ◽

Daniel Lu ◽

William W. Lockwood

Keyword(s):

Lung Adenocarcinoma ◽

Cell Lines ◽

Epithelial Mesenchymal Transition ◽

Pi3k Pathway ◽

Resistant Cell ◽

Casein Kinase ◽

Model Systems ◽

Cancer Type ◽

Mesenchymal Transition ◽

Bet Inhibitors

AbstractTargeting the epigenome to modulate gene expression programs driving cancer development has emerged as an exciting avenue for therapeutic intervention. Pharmacological inhibition of the bromodomain and extraterminal (BET) family of chromatin adapter proteins has proven effective in this regard, suppressing growth of diverse cancer types mainly through downregulation of the c-MYC oncogene, and its downstream transcriptional program. While initially effective, resistance to BET inhibitors (BETi) typically occurs through mechanisms that reactivate MYC expression. We have previously shown that lung adenocarcinoma (LAC) is inhibited by JQ1 through suppression of FOSL1, suggesting that the epigenetic landscape of tumor cells from different origins and differentiation states influences BETi response. Here, we assessed how these differences affect mechanisms of BETi resistance through the establishment of isogenic pairs of JQ1 sensitive and resistant LAC cell lines. We found that resistance to JQ1 in LAC occurs independent of FOSL1 while MYC levels remain unchanged between resistant cells and their JQ1-treated parental counterparts. Furthermore, while epithelial–mesenchymal transition (EMT) is observed upon resistance, TGF-β induced EMT did not confer resistance in JQ1 sensitive LAC lines, suggesting this is a consequence, rather than a driver of BETi resistance in our model systems. Importantly, siRNA knockdown demonstrated that JQ1 resistant cell lines are still dependent on BRD4 expression for survival and we found that phosphorylation of BRD4 is elevated in resistant LACs, identifying casein kinase 2 (CK2) as a candidate protein mediating this effect. Inhibition of CK2, as well as downstream transcriptional targets of phosphorylated BRD4—including AXL and activators of the PI3K pathway—synergize with JQ1 to inhibit BETi resistant LAC. Overall, this demonstrates that the mechanism of resistance to BETi varies depending on cancer type, with LAC cells developing JQ1 resistance independent of MYC regulation, and identifying CK2 phosphorylation of BRD4 as a potential target to overcome resistance in this cancer.

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