Implementation of cellular transport models within a multiscale simulation software

Agent-based models (ABM) have been increasingly employed to study dynamics of biological systems. However, these mostly lack transport mechanisms that interface between the agents and their microenvironment. We considered a set of 5 general transport mechanisms (see Fig. 1), written as ODE models and built on top of Fick’s Second Law of Diffusion and R-MM kinetics, and their implementation within an ABM software. Unit testing of said models was performed on static, liposome-like agents. We also studied the effect of varying agent and microenvironment-related parameters on the dynamic. We then connected a few of these mechanisms to the agent phenotype, developing a toy example that emulates the experimental decreased tumorigenic growth dynamics of Cytochalasin β

Patient-specific Boolean models of signalling networks guide personalised treatments

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell-line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell-line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.This work has been partially supported by the European Commission under the PrECISE project (H2020-PHC-668858), the INFORE project (H2020-ICT-825070) and the PerMedCoE (H2020-ICT-951773)Peer Reviewed"Article signat per 12 autors/es: Arnau Montagud, Jonas Béal, Luis Tobalina, Pauline Traynard, Vigneshwari Subramanian, Bence Szalai, Róbert Alföldi, László Puskás, Alfonso Valencia, Emmanuel Barillot, Julio Saez-Rodriguez, Laurence Calzone"Postprint (author's final draft

Lessons learned from a performance analysis and optimization of a multiscale cellular simulation

This work presents a comprehensive performance analysis and optimization of a multiscale agent-based cellular simulation. The optimizations applied are guided by detailed performance analysis and include memory management, load balance, and a locality-aware parallelization. The outcome of this paper is not only the speedup of 2.4x achieved by the optimized version with respect to the original PhysiCell code, but also the lessons learned and best practices when developing parallel HPC codes to obtain efficient and highly performant applications, especially in the computational biology field

Optimizing dosage-specific treatments in a multi-Scale model of a tumor growth

The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells’ spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells’ spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.This research has received funding from the Horizon 2020 INFORE Project, GA n° 825070 and the Horizon 2020 PerMedCoE Project, GA n° 951773.Peer ReviewedPostprint (published version

Large scale evaluation of differences between network-based and pairwise sequence-alignment-based methods of dendrogram reconstruction

[EN] Dendrograms are a way to represent relationships between organisms. Nowadays, these are inferred based on the comparison of genes or protein sequences by taking into account their differences and similarities. The genetic material of choice for the sequence alignments (all the genes or sets of genes) results in distinct inferred dendrograms. In this work, we evaluate differences between dendrograms reconstructed with different methodologies and for different sets of organisms chosen at random from a much larger set. A statistical analysis is performed to estimate fluctuations between the results obtained from the different methodologies that allows us to validate a systematic approach, based on the comparison of the organisms' metabolic networks for inferring dendrograms. This has the advantage that it allows the comparison of organisms very far away in the evolutionary tree even if they have no known ortholog gene in common. Our results show that dendrograms built using information from metabolic networks are similar to the standard sequence-based dendrograms and can be a complement to them.All authors received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement number 308518 (CyanoFactory) (https://ec.europa.eu/research/fp7/index_en.cfm).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Gamermann, D.; Montagud, A.; Conejero, JA.; Fernández De Córdoba, P.; Urchueguía Schölzel, JF. (2019). Large scale evaluation of differences between network-based and pairwise sequence-alignment-based methods of dendrogram reconstruction. PLoS ONE. 14(9):1-13. https://doi.org/10.1371/journal.pone.0221631S113149Robinson, D. F., & Foulds, L. R. (1981). Comparison of phylogenetic trees. Mathematical Biosciences, 53(1-2), 131-147. doi:10.1016/0025-5564(81)90043-2Day, W. H. E. (1985). Optimal algorithms for comparing trees with labeled leaves. Journal of Classification, 2(1), 7-28. doi:10.1007/bf01908061Pattengale, N. D., Gottlieb, E. J., & Moret, B. M. E. (2007). Efficiently Computing the Robinson-Foulds Metric. Journal of Computational Biology, 14(6), 724-735. doi:10.1089/cmb.2007.r012Woese, C. R., & Fox, G. E. (1977). Phylogenetic structure of the prokaryotic domain: The primary kingdoms. Proceedings of the National Academy of Sciences, 74(11), 5088-5090. doi:10.1073/pnas.74.11.5088Ciccarelli, F. D., Doerks, T., von Mering, C., Creevey, C. J., Snel, B., & Bork, P. (2006). Toward Automatic Reconstruction of a Highly Resolved Tree of Life. Science, 311(5765), 1283-1287. doi:10.1126/science.1123061Kurt Lienau, E., DeSalle, R., Allard, M., Brown, E. W., Swofford, D., Rosenfeld, J. A., … Planet, P. J. (2010). The mega-matrix tree of life: using genome-scale horizontal gene transfer and sequence evolution data as information about the vertical history of life. Cladistics, 27(4), 417-427. doi:10.1111/j.1096-0031.2010.00337.xWu, M., & Eisen, J. A. (2008). A simple, fast, and accurate method of phylogenomic inference. Genome Biology, 9(10), R151. doi:10.1186/gb-2008-9-10-r151Wu, D., Hugenholtz, P., Mavromatis, K., Pukall, R., Dalin, E., Ivanova, N. N., … Eisen, J. A. (2009). A phylogeny-driven genomic encyclopaedia of Bacteria and Archaea. Nature, 462(7276), 1056-1060. doi:10.1038/nature08656Mai, H., Lam, T.-W., & Ting, H.-F. (2017). A simple and economical method for improving whole genome alignment. BMC Genomics, 18(S4). doi:10.1186/s12864-017-3734-2Feng, B., Lin, Y., Zhou, L., Guo, Y., Friedman, R., Xia, R., … Tang, J. (2017). Reconstructing Yeasts Phylogenies and Ancestors from Whole Genome Data. Scientific Reports, 7(1). doi:10.1038/s41598-017-15484-5Rokas, A., Williams, B. L., King, N., & Carroll, S. B. (2003). Genome-scale approaches to resolving incongruence in molecular phylogenies. Nature, 425(6960), 798-804. doi:10.1038/nature02053JEFFROY, O., BRINKMANN, H., DELSUC, F., & PHILIPPE, H. (2006). Phylogenomics: the beginning of incongruence? Trends in Genetics, 22(4), 225-231. doi:10.1016/j.tig.2006.02.003Gamermann, D., Montagud, A., Conejero, J. A., Urchueguía, J. F., & de Córdoba, P. F. (2014). New Approach for Phylogenetic Tree Recovery Based on Genome-Scale Metabolic Networks. Journal of Computational Biology, 21(7), 508-519. doi:10.1089/cmb.2013.0150Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000). The large-scale organization of metabolic networks. Nature, 407(6804), 651-654. doi:10.1038/35036627Clemente, J. C., Satou, K., & Valiente, G. (2007). Phylogenetic reconstruction from non-genomic data. Bioinformatics, 23(2), e110-e115. doi:10.1093/bioinformatics/btl307Deyasi, K., Banerjee, A., & Deb, B. (2015). Phylogeny of metabolic networks: A spectral graph theoretical approach. Journal of Biosciences, 40(4), 799-808. doi:10.1007/s12038-015-9562-0Kanehisa, M. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 28(1), 27-30. doi:10.1093/nar/28.1.27Reyes, R., Gamermann, D., Montagud, A., Fuente, D., Triana, J., Urchueguía, J. F., & de Córdoba, P. F. (2012). Automation on the Generation of Genome-Scale Metabolic Models. Journal of Computational Biology, 19(12), 1295-1306. doi:10.1089/cmb.2012.0183Gross, J. L., Yellen, J., & Zhang, P. (Eds.). (2013). Handbook of Graph Theory. doi:10.1201/b16132Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443-453. doi:10.1016/0022-2836(70)90057-4Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1-7), 107-117. doi:10.1016/s0169-7552(98)00110-xAllesina, S., & Pascual, M. (2009). 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Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients

Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumors and their therapeutic responses. We present here a novel framework, referred to as PROFILE, to tailor logical models to a particular biological sample such as a patient tumor. This methodology permits to compare the model simulations to individual clinical data, i.e., survival time. Our approach focuses on integrating mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) to logical models. These data need first to be either binarized or set between 0 and 1, and can then be incorporated in the logical model by modifying the activity of the node, the initial conditions or the state transition rates. The use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models results in model state probabilities, and allows for a semi-quantitative study of the model phenotypes and perturbations. As a proof of concept, we use a published generic model of cancer signaling pathways and molecular data from METABRIC breast cancer patients. For this example, we test several combinations of data incorporation and discuss that, with these data, the most comprehensive patient-specific cancer models are obtained by modifying the nodes' activity of the model with mutations, in combination or not with CNA data, and altering the transition rates with RNA expression. We conclude that these model simulations show good correlation with clinical data such as patients' Nottingham prognostic index (NPI) subgrouping and survival time. We observe that two highly relevant cancer phenotypes derived from personalized models, Proliferation and Apoptosis, are biologically consistent prognostic factors: patients with both high proliferation and low apoptosis have the worst survival rate, and conversely. Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient-relevant models. This work leads to the use of logical modeling for precision medicine and will eventually facilitate the choice of patient-specific drug treatments by physicians

Parallel model exploration for tumor treatment simulations

Abstract Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi-scale simulator for tumor cell growth and a genetic algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.This work has received funding from the EU Horizon 2020 RIA program INFORE under grant agreement No 825070Peer ReviewedPostprint (author's final draft

Effects of High-Fat Diet and Maternal Binge-Like Alcohol Consumption and Their Influence on Cocaine Response in Female Mice Offspring

Backgroud: Prenatal alcohol exposure is a leading cause of neurobehavioral and neurocognitive deficits collectively known as fetal alcohol spectrum disorders (FASD), including eating disorders and increased risk for substance abuse as very common issues. In this context, the present study aimed to assess the interaction between alcohol exposure during gestation and lactation periods (PLAE) and a high fat diet (HFD) during childhood and adolescence. Methods: Pregnant C57BL/6 mice underwent a procedure for alcohol binge drinking during gestation and lactation periods. Subsequently, PLAE female offspring were fed with a HFD for 8 weeks and thereafter, nutrition-related parameters as well as their response to cocaine were assessed. Results: In our model, feeding young females with a HFD increased their triglyceride blood levels but did not induce an overweight compared to those fed with a standard diet. Moreover, PLAE affected how females responded to the fatty diet as they consumed less amount of food than water-exposed offspring, consistent with a lower gain of body weight. HFD increased the psychostimulant effects of cocaine. Surprisingly, PLAE reduced the locomotor responses to cocaine without modifying cocaine-induced reward. Moreover, PLAE prevented the striatal overexpression of cannabinoid 1 receptors induced by a HFD and induced an alteration of myelin damage biomarker in the prefrontal cortex, an effect that was mitigated by a HFD-based feeding. Conclusion: Therefore, in female offspring, some effects triggered by one of these factors, PLAE or a HFD, were blunted by the other, suggesting a close interaction between the involved mechanisms

Desarrollo de una plataforma computacional para el modelado metabólico de microorganismos

[EN] Synthetic biology focuses on the design and construction of artificial genetic systems that are capable of carrying out a specific function after being inserted into a living system. With the development of synthetic biology a new generation of bioengineers has appeared who develop complex, highly integrated genetic biological pathways. Te improvement of this scientific discipline aims to establish a computational and conceptual framework that will support the development of modular artificial biological systems based on an engineering and systematic methodology. To achieve this, it will be necessary to provide new integrated computational tools in a common environment for the analysis of metabolic phenotypes, the design of new complex genetic pathways and the visualisation of metabolic maps to the next generation of designers in synthetic biology and future biotechnologists and biological engineers. A result of this research is the Hydra platform (Hybrid Draw and Routes Analysis) that integrates various tools for the design, analysis, and visualisation of metabolic networks.[ES] La Biología Sintética (BS) se centra en el diseño y la construcción de sistemas genéticos artificiales, capaces de desarrollar una función específica después de haber sido introducidos en un sistema vivo. Con el desarrollo de la BS, se observa una nueva generación de bioingenieros que desarrollan complejos circuitos biológicos genéticos con un alto nivel de integración. La mejora de esta disciplina científica tiene por objeto establecer un marco computacional y conceptual que dé asistencia al desarrollo de sistemas biológicos artificiales modulares basándose en una metodología ingenieril y sistemática, para lo que se necesita proveer a la próxima generación de diseñadores en Biología Sintética y a los futuros biotecnólogos e ingenieros biológicos de nuevas herramientas computacionales integradas en un entorno común para el análisis de fenotipos metabólicos, el diseño de nuevos circuitos genéticos complejos y la visualización de mapas metabólicos. Como resultado de esta investigación se obtiene la plataforma Hydra (Hybrid Draw and Routes Analysis), que integra diversas herramientas para el diseño, análisis y visualización de las redes metabólicas.Los autores desean agradecer el soporte financiero recibido por el Ministerio de Ciencia e Innovación a través de la concesión TIN2009- 12359; la Conselleria de Inmigración y Ciudadanía de la Generalitat Valenciana (concesión 3012/2009) y la Comisión Europea (Proyecto TARPOL FP7 EU KBBE 212894).Reyes, R.; Garrido, J.; Jaime, RA.; Córdova, V.; Triana, J.; Villar, L.; Castro, JC.... (2011). Desarrollo de una plataforma computacional para el modelado metabólico de microorganismos. Nereis. Revista Iberoamericana Interdisciplinar de Métodos, Modelización y Simulación. (3):25-31. http://hdl.handle.net/10251/91952S2531