Literature DB >> 20439274

ImmunoGrid: towards agent-based simulations of the human immune system at a natural scale.

Mark Halling-Brown1, Francesco Pappalardo, Nicolas Rapin, Ping Zhang, Davide Alemani, Andrew Emerson, Filippo Castiglione, Patrice Duroux, Marzio Pennisi, Olivo Miotto, Daniel Churchill, Elda Rossi, David S Moss, Clare E Sansom, Massimo Bernaschi, Marie-Paule Lefranc, Søren Brunak, Ole Lund, Santo Motta, Pier-Luigi Lollini, Annalisa Murgo, Arianna Palladini, Kaye E Basford, Vladimir Brusic, Adrian J Shepherd.   

Abstract

The ultimate aim of the EU-funded ImmunoGrid project is to develop a natural-scale model of the human immune system-that is, one that reflects both the diversity and the relative proportions of the molecules and cells that comprise it-together with the grid infrastructure necessary to apply this model to specific applications in the field of immunology. These objectives present the ImmunoGrid Consortium with formidable challenges in terms of complexity of the immune system, our partial understanding about how the immune system works, the lack of reliable data and the scale of computational resources required. In this paper, we explain the key challenges and the approaches adopted to overcome them. We also consider wider implications for the present ambitious plans to develop natural-scale, integrated models of the human body that can make contributions to personalized health care, such as the European Virtual Physiological Human initiative. Finally, we ask a key question: How long will it take us to resolve these challenges and when can we expect to have fully functional models that will deliver health-care benefits in the form of personalized care solutions and improved disease prevention?

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Year:  2010        PMID: 20439274     DOI: 10.1098/rsta.2010.0067

Source DB:  PubMed          Journal:  Philos Trans A Math Phys Eng Sci        ISSN: 1364-503X            Impact factor:   4.226


  18 in total

Review 1.  Systems immunology: a survey of modeling formalisms, applications and simulation tools.

Authors:  Vipin Narang; James Decraene; Shek-Yoon Wong; Bindu S Aiswarya; Andrew R Wasem; Shiang Rong Leong; Alexandre Gouaillard
Journal:  Immunol Res       Date:  2012-09       Impact factor: 2.829

Review 2.  A review of quantitative modeling of B cell responses to antigenic challenge.

Authors:  Timothy P Hickling; Xiaoying Chen; Paolo Vicini; Satyaprakash Nayak
Journal:  J Pharmacokinet Pharmacodyn       Date:  2014-10-19       Impact factor: 2.745

Review 3.  Review of the systems biology of the immune system using agent-based models.

Authors:  Snehal B Shinde; Manish P Kurhekar
Journal:  IET Syst Biol       Date:  2018-06       Impact factor: 1.615

4.  Optimal vaccination schedule search using genetic algorithm over MPI technology.

Authors:  Cristiano Calonaci; Ferdinando Chiacchio; Francesco Pappalardo
Journal:  BMC Med Inform Decis Mak       Date:  2012-11-13       Impact factor: 2.796

5.  Modeling the competition between lung metastases and the immune system using agents.

Authors:  Marzio Pennisi; Francesco Pappalardo; Ariannna Palladini; Giordano Nicoletti; Patrizia Nanni; Pier-Luigi Lollini; Santo Motta
Journal:  BMC Bioinformatics       Date:  2010-10-15       Impact factor: 3.169

6.  SimB16: modeling induced immune system response against B16-melanoma.

Authors:  Francesco Pappalardo; Ivan Martinez Forero; Marzio Pennisi; Asis Palazon; Ignacio Melero; Santo Motta
Journal:  PLoS One       Date:  2011-10-19       Impact factor: 3.240

7.  Spatial and functional heterogeneities shape collective behavior of tumor-immune networks.

Authors:  Daniel K Wells; Yishan Chuang; Louis M Knapp; Dirk Brockmann; William L Kath; Joshua N Leonard
Journal:  PLoS Comput Biol       Date:  2015-04-23       Impact factor: 4.475

8.  A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins: part 1-theoretical model.

Authors:  X Chen; T P Hickling; P Vicini
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2014-09-03

9.  Mathematical modeling of the immune system recognition to mammary carcinoma antigen.

Authors:  Carlo Bianca; Ferdinando Chiacchio; Francesco Pappalardo; Marzio Pennisi
Journal:  BMC Bioinformatics       Date:  2012-12-13       Impact factor: 3.169

10.  Physio-environmental sensing and live modeling.

Authors:  Filippo Castiglione; Vanessa Diaz; Andrea Gaggioli; Pietro Liò; Claudia Mazzà; Emanuela Merelli; Carel G M Meskers; Francesco Pappalardo; Rainer von Ammon
Journal:  Interact J Med Res       Date:  2013-01-30
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