Computational simulations of the immune system for personalized medicine

State of the art and challenges

Francesco Pappalardo, Ping Zhang, Mark Halling-Brown, Kaye Basford, Antonio Scalia, Adrian Shepherd, David Moss, Santo Motta, Vladimir Brusic

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

The main goal of pharmacogenomics is to study the effects of genetic variation on patient responses to therapies. Its applications range from the evaluation of safety and efficacy of treatment to the optimization of therapies and therapeutic regimens. Pharmacogenomics is becoming increasingly important in immunology, for the development of new generation vaccines, immunotherapies and transplantation. The human immune system is a complex and adaptive learning system which operates at multiple levels: molecules, cells, organs, organisms, and groups of organisms. Immunologic research, both basic and applied, needs to deal with this complexity. We increasingly use mathematical modeling and computational simulation in the study of the immune system and immune responses. Thus, quantitative models that appropriately capture the complexity in architecture and function of the immune system are an integral component of the personalized medicine efforts. In silico models of the immune system can provide answers to a variety of questions, including understanding the general behavior of the immune system, the course of disease, effects of treatment, analysis of cellular and molecular interactions, and simulation of laboratory experiments. We herein present the ImmunoGrid project that integrates molecular and system level models of the immune system and processes for in silico studies of the immune function. The ImmunoGrid simulator uses Grid technologies, enabling computational simulation of the immune system at the natural scale, perform a large number of simulated experiments, capture the diversity of the immune system between individuals, and provide a basis for therapeutic approaches tailored to the individual genetic make-up.

Original languageEnglish
Pages (from-to)260-271
Number of pages12
JournalCurrent Pharmacogenomics and Personalized Medicine
Volume6
Issue number4
Publication statusPublished - 2008
Externally publishedYes

Fingerprint

Precision Medicine
Systems Analysis
Immune System
Pharmacogenetics
Computer Simulation
Immune System Phenomena
Therapeutics
Immune System Diseases
Allergy and Immunology
Immunotherapy
Vaccines
Transplantation
Learning
Technology
Safety
Research

Keywords

  • Computational modeling
  • Immune system simulation
  • ImmunoGrid
  • Pharmacogenomics applications
  • Pharmacogenomics in personalized medicine

ASJC Scopus subject areas

  • Genetics
  • Molecular Medicine
  • Pharmacology
  • Molecular Biology
  • Genetics(clinical)

Cite this

Pappalardo, F., Zhang, P., Halling-Brown, M., Basford, K., Scalia, A., Shepherd, A., ... Brusic, V. (2008). Computational simulations of the immune system for personalized medicine: State of the art and challenges. Current Pharmacogenomics and Personalized Medicine, 6(4), 260-271.

Computational simulations of the immune system for personalized medicine : State of the art and challenges. / Pappalardo, Francesco; Zhang, Ping; Halling-Brown, Mark; Basford, Kaye; Scalia, Antonio; Shepherd, Adrian; Moss, David; Motta, Santo; Brusic, Vladimir.

In: Current Pharmacogenomics and Personalized Medicine, Vol. 6, No. 4, 2008, p. 260-271.

Research output: Contribution to journalArticle

Pappalardo, F, Zhang, P, Halling-Brown, M, Basford, K, Scalia, A, Shepherd, A, Moss, D, Motta, S & Brusic, V 2008, 'Computational simulations of the immune system for personalized medicine: State of the art and challenges', Current Pharmacogenomics and Personalized Medicine, vol. 6, no. 4, pp. 260-271.
Pappalardo, Francesco ; Zhang, Ping ; Halling-Brown, Mark ; Basford, Kaye ; Scalia, Antonio ; Shepherd, Adrian ; Moss, David ; Motta, Santo ; Brusic, Vladimir. / Computational simulations of the immune system for personalized medicine : State of the art and challenges. In: Current Pharmacogenomics and Personalized Medicine. 2008 ; Vol. 6, No. 4. pp. 260-271.
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