Can Gamification Contribute to Computer Modeling-Driven Biomedical Research? - Vera, Julio; Santos Rosales, Guido (Frontiers in Physiology, 2018)
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Model-based genotype-phenotype mapping used to investigate gene signatures of immune sensitivity and resistance in melanoma micrometastasis - Santos Rosales, Guido; Nikolov, Svetoslav; Lai, Xin; Eberhardt, Martin; Dreyer, Florian; Paul, Sushmita; Schuler, Gerold; Vera, Julio (Scientific Reports, 2016)
In this paper, we combine kinetic modelling and patient gene expression data analysis to elucidate biological mechanisms by which melanoma becomes resistant to the immune system and to immunotherapy. To this end, we systematically perturbed the parameters in a kinetic model and performed a mathematical analysis of their impact, thereby obtaining signatures associated with the emergence of phenotypes of melanoma immune sensitivity and resistance. Our phenotypic signatures were compared with published clinical data on pretreatment tumor gene expression in patients subjected to immunotherapy against metastatic melanoma. To this end, the differentially expressed genes were annotated with standard gene ontology terms and aggregated into metagenes. Our method sheds light on putative mechanisms by which melanoma may develop immunoresistance. Precisely, our results and the clinical data point to the existence of a signature of intermediate expression levels for genes related to antigen presentation that constitutes an intriguing resistance mechanism, whereby micrometastases are able to minimize the combined anti-tumor activity of complementary responses mediated by cytotoxic T cells and natural killer cells, respectively. Finally, we computationally explored the efficacy of cytokines used as low-dose co-adjuvants for the therapeutic anticancer vaccine to overcome tumor immunoresistance.
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Model-Based Phenotypic Signatures Governing the Dynamics of the Stem and Semi-differentiated Cell Populations in Dysplastic Colonic Crypts - Nikolov, Svetoslav; Santos Rosales, Guido; Wolkenhauer, Olaf; Vera, Julio (Bulletin of Mathematical Biology, 2018)
Mathematical modeling of cell differentiated in colonic crypts can contribute to a better understanding of basic mechanisms underlying colonic tissue organization, but also its deregulation during carcinogenesis and tumor progression. Here, we combined bifurcation analysis to assess the effect that time delay has in the complex interplay of stem cells and semi-differentiated cells at the niche of colonic crypts, and systematic model perturbation and simulation to find model-based phenotypes linked to cancer progression. The models suggest that stem cell and semi-differentiated cell population dynamics in colonic crypts can display chaotic behavior. In addition, we found that clinical profiling of colorectal cancer correlates with the in silico phenotypes proposed by the mathematical model. Further, potential therapeutic targets for chemotherapy resistant phenotypes are proposed, which in any case will require experimental validation.
EVALuna2:34801

Multi-Level Computational Modeling of Anti-Cancer Dendritic Cell Vaccination Utilized to Select Molecular Targets for Therapy Optimization - Lai, Xin; Keller, Christine; Santos Rosales, Guido; Schaft, Niels; Dörrie, Jan; Vera, Julio (Frontiers in Cell and Developmental Biology, 2022)
Dendritic cells (DCs) can be used for therapeutic vaccination against cancer. The success of this therapy depends on efficient tumor-antigen presentation to cytotoxic T lymphocytes (CTLs) and the induction of durable CTL responses by the DCs. Therefore, simulation of such a biological system by computational modeling is appealing because it can improve our understanding of the molecular mechanisms underlying CTL induction by DCs and help identify new strategies to improve therapeutic DC vaccination for cancer. Here, we developed a multi-level model accounting for the life cycle of DCs during anti-cancer immunotherapy. Specifically, the model is composed of three parts representing different stages of DC immunotherapy – the spreading and bio-distribution of intravenously injected DCs in human organs, the biochemical reactions regulating the DCs’ maturation and activation, and DC-mediated activation of CTLs. We calibrated the model using quantitative experimental data that account for the activation of key molecular circuits within DCs, the bio-distribution of DCs in the body, and the interaction between DCs and T cells. We showed how such a data-driven model can be exploited in combination with sensitivity analysis and model simulations to identify targets for enhancing anti-cancer DC vaccination. Since other previous works show how modeling improves therapy schedules and DC dosage, we here focused on the molecular optimization of the therapy. In line with this, we simulated the effect in DC vaccination of the concerted modulation of combined intracellular regulatory processes and proposed several possibilities that can enhance DC-mediated immunogenicity. Taken together, we present a comprehensive time-resolved multi-level model for studying DC vaccination in melanoma. Although the model is not intended for personalized patient therapy, it could be used as a tool for identifying molecular targets for optimizing DC-based therapy for cancer, which ultimately should be tested in in vitro and in vivo experiments.
CRIS-Team Scopus Importer:2022-03-04
Keywords: bio-distribution; cellular therapy; dendritic cell vaccine; gene networks; immunotherapy; kinetic modeling; melanoma; systems medicine