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Publications (10 of 47) Show all publications
Bouchnita, A., Hellander, S. & Hellander, A. (2019). A 3D multiscale model to explore the role of EGFR overexpression in tumourigenesis. Bulletin of Mathematical Biology, 81(7), 2323-2344
Open this publication in new window or tab >>A 3D multiscale model to explore the role of EGFR overexpression in tumourigenesis
2019 (English)In: Bulletin of Mathematical Biology, ISSN 0092-8240, E-ISSN 1522-9602, Vol. 81, no 7, p. 2323-2344Article in journal (Refereed) Published
Abstract [en]

The epidermal growth factor receptor (EGFR) signalling cascade is one of the main pathways that regulate the survival and division of mammalian cells. It is also one of the most altered transduction pathways in cancer. Acquired mutations in the EGFR/ERK pathway can cause the overexpression of EGFR on the surface of the cell, while others downregulate the inactivation of switched on intracellular proteins such as Ras and Raf. This upregulates the activity of ERK and promotes cell division. We develop a 3D multiscale model to explore the role of EGFR overexpression on tumour initiation. In this model, cells are described as individual objects that move, interact, divide, proliferate, and die by apoptosis. We use Brownian Dynamics to describe the extracellular and intracellular regulations of cells as well as the spatial and stochastic effects influencing them. The fate of each cell depends on the number of active transcription factors in the nucleus. We use numerical simulations to investigate the individual and combined effects of mutations on the intracellular regulation of individual cells. Next, we show that the distance between active receptors increase the level of EGFR/ERK signalling. We demonstrate the usefulness of the model by quantifying the impact of mutational alterations in the EGFR/ERK pathway on the growth rate of in silico tumours.

Keywords
EGFR, Tumour growth, Agent-based modelling, Brownian Dynamics
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-382735 (URN)10.1007/s11538-019-00607-y (DOI)000474571900011 ()31016574 (PubMedID)
Funder
Swedish Research Council, 2015-03964eSSENCE - An eScience Collaboration
Available from: 2019-04-23 Created: 2019-04-30 Last updated: 2019-08-15Bibliographically approved
Ausmees, K., John, A., Toor, S. Z., Hellander, A. & Nettelblad, C. (2018). BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data. BMC Bioinformatics, 19, 240:1-11, Article ID 240.
Open this publication in new window or tab >>BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data
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2018 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 19, p. 240:1-11, article id 240Article in journal (Refereed) Published
National Category
Software Engineering Genetics
Identifiers
urn:nbn:se:uu:diva-360033 (URN)10.1186/s12859-018-2241-z (DOI)000436517200001 ()29940842 (PubMedID)
Projects
eSSENCE
Available from: 2018-06-26 Created: 2018-09-09 Last updated: 2019-07-01Bibliographically approved
Torruangwatthana, P., Wieslander, H., Blamey, B., Hellander, A. & Toor, S. (2018). HarmonicIO: Scalable data stream processing for scientific datasets. In: Proc. 11th International Conference on Cloud Computing: . Paper presented at CLOUD 2018, July 2–7, San Francisco, CA (pp. 879-882). Los Alamitos, CA: IEEE Computer Society
Open this publication in new window or tab >>HarmonicIO: Scalable data stream processing for scientific datasets
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2018 (English)In: Proc. 11th International Conference on Cloud Computing, Los Alamitos, CA: IEEE Computer Society, 2018, p. 879-882Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Los Alamitos, CA: IEEE Computer Society, 2018
National Category
Software Engineering
Identifiers
urn:nbn:se:uu:diva-364671 (URN)10.1109/CLOUD.2018.00126 (DOI)000454741700119 ()978-1-5386-7235-8 (ISBN)
Conference
CLOUD 2018, July 2–7, San Francisco, CA
Projects
eSSENCE
Available from: 2018-09-10 Created: 2018-10-31 Last updated: 2019-01-24Bibliographically approved
Singh, P. & Hellander, A. (2018). Hyperparameter optimization for approximate Bayesian computation. In: Proc. 50th Winter Simulation Conference: . Paper presented at WSC 2018, December 9–12, Gothenburg, Sweden (pp. 1718-1729). Piscataway, NJ: IEEE
Open this publication in new window or tab >>Hyperparameter optimization for approximate Bayesian computation
2018 (English)In: Proc. 50th Winter Simulation Conference, Piscataway, NJ: IEEE, 2018, p. 1718-1729Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018
National Category
Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-373961 (URN)10.1109/WSC.2018.8632304 (DOI)000461414101078 ()978-1-5386-6572-5 (ISBN)
Conference
WSC 2018, December 9–12, Gothenburg, Sweden
Projects
eSSENCE
Available from: 2019-02-04 Created: 2019-01-17 Last updated: 2019-05-02Bibliographically approved
Singh, P. & Hellander, A. (2018). Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference. In: Proc. 32nd European Simulation and Modelling Conference: . Paper presented at ESM 2018, October 24–26, Ghent, Belgium (pp. 22-27). EUROSIS
Open this publication in new window or tab >>Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference
2018 (English)In: Proc. 32nd European Simulation and Modelling Conference, EUROSIS , 2018, p. 22-27Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
EUROSIS, 2018
National Category
Computational Mathematics Computer Sciences
Identifiers
urn:nbn:se:uu:diva-373959 (URN)978-94-92859-05-1 (ISBN)
Conference
ESM 2018, October 24–26, Ghent, Belgium
Projects
eSSENCE
Available from: 2018-10-26 Created: 2019-01-17 Last updated: 2019-01-18Bibliographically approved
Coulier, A. & Hellander, A. (2018). Orchestral: a lightweight framework for parallel simulations of cell–cell communication. In: Proc. 14th International Conference on e-Science: . Paper presented at e-Science 2018, October 29 – November 1, Amsterdam, The Netherlands (pp. 168-176). Los Alamitos, CA: IEEE Computer Society
Open this publication in new window or tab >>Orchestral: a lightweight framework for parallel simulations of cell–cell communication
2018 (English)In: Proc. 14th International Conference on e-Science, Los Alamitos, CA: IEEE Computer Society, 2018, p. 168-176Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Los Alamitos, CA: IEEE Computer Society, 2018
National Category
Software Engineering
Identifiers
urn:nbn:se:uu:diva-379431 (URN)10.1109/eScience.2018.00032 (DOI)000459856400024 ()978-1-5386-9156-4 (ISBN)
Conference
e-Science 2018, October 29 – November 1, Amsterdam, The Netherlands
Projects
eSSENCE
Available from: 2018-12-27 Created: 2019-03-18 Last updated: 2019-03-28Bibliographically approved
Jallow, A., Hellander, A. & Toor, S. (2017). Cost-aware application development and management using CLOUD-METRIC. In: Proc. 7th International Conference on Cloud Computing and Services Science: . Paper presented at CLOSER 2017 (pp. 515-522). Setúbal, Portugal: SciTePress
Open this publication in new window or tab >>Cost-aware application development and management using CLOUD-METRIC
2017 (English)In: Proc. 7th International Conference on Cloud Computing and Services Science, Setúbal, Portugal: SciTePress, 2017, p. 515-522Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Setúbal, Portugal: SciTePress, 2017
National Category
Software Engineering
Identifiers
urn:nbn:se:uu:diva-326539 (URN)10.5220/0006307505150522 (DOI)978-989-758-243-1 (ISBN)
Conference
CLOSER 2017
Projects
eSSENCE
Available from: 2017-04-29 Created: 2017-07-13 Last updated: 2018-01-13Bibliographically approved
Hellander, S., Hellander, A. & Petzold, L. (2017). Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning. Journal of Chemical Physics, 147(23), Article ID 234101.
Open this publication in new window or tab >>Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning
2017 (English)In: Journal of Chemical Physics, ISSN 0021-9606, E-ISSN 1089-7690, Vol. 147, no 23, article id 234101Article in journal (Refereed) Published
Abstract [en]

The reaction-diffusion master equation (RDME) is a model that allows for efficient on-lattice simulation of spatially resolved stochastic chemical kinetics. Compared to off-lattice hard-sphere simulations with Brownian dynamics or Green's function reaction dynamics, the RDME can be orders of magnitude faster if the lattice spacing can be chosen coarse enough. However, strongly diffusion-controlled reactions mandate a very fine mesh resolution for acceptable accuracy. It is common that reactions in the same model differ in their degree of diffusion control and therefore require different degrees of mesh resolution. This renders mesoscopic simulation inefficient for systems with multiscale properties. Mesoscopic-microscopic hybrid methods address this problem by resolving the most challenging reactions with a microscale, off-lattice simulation. However, all methods to date require manual partitioning of a system, effectively limiting their usefulness as "black-box" simulation codes. In this paper, we propose a hybrid simulation algorithm with automatic system partitioning based on indirect a priori error estimates. We demonstrate the accuracy and efficiency of the method on models of diffusion-controlled networks in 3D.

National Category
Other Chemistry Topics
Identifiers
urn:nbn:se:uu:diva-339766 (URN)10.1063/1.5002773 (DOI)000418648200003 ()29272930 (PubMedID)
Funder
NIH (National Institute of Health), R01-EB014877Swedish Research Council, 2015-03964eSSENCE - An eScience Collaboration
Available from: 2018-02-09 Created: 2018-02-09 Last updated: 2018-02-09Bibliographically approved
Engblom, S., Hellander, A. & Lötstedt, P. (2017). Multiscale simulation of stochastic reaction–diffusion networks. In: Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology: (pp. 55-79). Springer
Open this publication in new window or tab >>Multiscale simulation of stochastic reaction–diffusion networks
2017 (English)In: Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, Springer, 2017, p. 55-79Chapter in book (Refereed)
Place, publisher, year, edition, pages
Springer, 2017
National Category
Computational Mathematics Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-331828 (URN)10.1007/978-3-319-62627-7_3 (DOI)978-3-319-62626-0 (ISBN)
Projects
eSSENCE
Available from: 2017-10-05 Created: 2017-10-18 Last updated: 2018-11-12Bibliographically approved
Kaucka, M., Zikmund, T., Tesarova, M., Gyllborg, D., Hellander, A., Jaros, J., . . . Adameyko, I. (2017). Oriented clonal cell dynamics enables accurate growth and shaping of vertebrate cartilage. eLIFE, 6, Article ID e25902.
Open this publication in new window or tab >>Oriented clonal cell dynamics enables accurate growth and shaping of vertebrate cartilage
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2017 (English)In: eLIFE, E-ISSN 2050-084X, Vol. 6, article id e25902Article in journal (Refereed) Published
National Category
Cell Biology Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-320902 (URN)10.7554/eLife.25902 (DOI)000400576600001 ()
Projects
eSSENCE
Available from: 2017-04-17 Created: 2017-04-26 Last updated: 2018-01-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7273-7923

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