Cancer immunoprevention: What can we learn from in silico models?

Francesco Pappalardo; Marzio Pennisi; Alessandro Cincotti; Ferdinando Chiacchio; Santo Motta; Pier Luigi Lollini

doi:10.1007/978-3-642-14831-6_15

Cancer immunoprevention: What can we learn from in silico models?

Francesco Pappalardo, Marzio Pennisi, Alessandro Cincotti, Ferdinando Chiacchio, Santo Motta, Pier Luigi Lollini

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present our experience of the artificial immunity induced by an immuoprevention vaccine succesfully tested on transgenic mice. The model mimics the phenomenon of initial cancer growing starting from the stage of the atypical hyperplasia and reproduces the action of the vaccine in activating the immune response. The model has been validated against in-vivo experiments. Finally we use the model to determine an optimal vaccination scheduling which reduce to a minimum the number of vaccine administrations still preventing the solid tumor formation is a population of virtual mice. The vaccination schedule proposed by the model is substantially lighter than the one's determined by the standard intuitive procedure.

Original language	English
Title of host publication	Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings
Pages	111-118
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-14831-6_15
Publication status	Published - 2010
Externally published	Yes
Event	6th International Conference on Intelligent Computing, ICIC 2010 - Changsha, China Duration: 18 Aug 2010 → 21 Aug 2010

Publication series

Name	Communications in Computer and Information Science
Volume	93 CCIS
ISSN (Print)	1865-0929

Conference

Conference	6th International Conference on Intelligent Computing, ICIC 2010
Country/Territory	China
City	Changsha
Period	18/08/10 → 21/08/10

Keywords

Artificial immunity
agent based models
cancer
vaccine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-14831-6_15

Cite this

Pappalardo, F., Pennisi, M., Cincotti, A., Chiacchio, F., Motta, S., & Lollini, P. L. (2010). Cancer immunoprevention: What can we learn from in silico models? In Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings (pp. 111-118). (Communications in Computer and Information Science; Vol. 93 CCIS). https://doi.org/10.1007/978-3-642-14831-6_15

@inproceedings{05559f689966459cb4a10c71a55a5f26,

title = "Cancer immunoprevention: What can we learn from in silico models?",

abstract = "We present our experience of the artificial immunity induced by an immuoprevention vaccine succesfully tested on transgenic mice. The model mimics the phenomenon of initial cancer growing starting from the stage of the atypical hyperplasia and reproduces the action of the vaccine in activating the immune response. The model has been validated against in-vivo experiments. Finally we use the model to determine an optimal vaccination scheduling which reduce to a minimum the number of vaccine administrations still preventing the solid tumor formation is a population of virtual mice. The vaccination schedule proposed by the model is substantially lighter than the one's determined by the standard intuitive procedure.",

keywords = "Artificial immunity, agent based models, cancer, vaccine",

author = "Francesco Pappalardo and Marzio Pennisi and Alessandro Cincotti and Ferdinando Chiacchio and Santo Motta and Lollini, {Pier Luigi}",

year = "2010",

doi = "10.1007/978-3-642-14831-6_15",

language = "English",

isbn = "3642148301",

series = "Communications in Computer and Information Science",

pages = "111--118",

booktitle = "Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings",

note = "6th International Conference on Intelligent Computing, ICIC 2010 ; Conference date: 18-08-2010 Through 21-08-2010",

}

Pappalardo, F, Pennisi, M, Cincotti, A, Chiacchio, F, Motta, S & Lollini, PL 2010, Cancer immunoprevention: What can we learn from in silico models? in Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. Communications in Computer and Information Science, vol. 93 CCIS, pp. 111-118, 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, 18/08/10. https://doi.org/10.1007/978-3-642-14831-6_15

Cancer immunoprevention: What can we learn from in silico models? / Pappalardo, Francesco; Pennisi, Marzio; Cincotti, Alessandro et al.
Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. 2010. p. 111-118 (Communications in Computer and Information Science; Vol. 93 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Cancer immunoprevention

T2 - 6th International Conference on Intelligent Computing, ICIC 2010

AU - Pappalardo, Francesco

AU - Pennisi, Marzio

AU - Cincotti, Alessandro

AU - Chiacchio, Ferdinando

AU - Motta, Santo

AU - Lollini, Pier Luigi

PY - 2010

Y1 - 2010

N2 - We present our experience of the artificial immunity induced by an immuoprevention vaccine succesfully tested on transgenic mice. The model mimics the phenomenon of initial cancer growing starting from the stage of the atypical hyperplasia and reproduces the action of the vaccine in activating the immune response. The model has been validated against in-vivo experiments. Finally we use the model to determine an optimal vaccination scheduling which reduce to a minimum the number of vaccine administrations still preventing the solid tumor formation is a population of virtual mice. The vaccination schedule proposed by the model is substantially lighter than the one's determined by the standard intuitive procedure.

AB - We present our experience of the artificial immunity induced by an immuoprevention vaccine succesfully tested on transgenic mice. The model mimics the phenomenon of initial cancer growing starting from the stage of the atypical hyperplasia and reproduces the action of the vaccine in activating the immune response. The model has been validated against in-vivo experiments. Finally we use the model to determine an optimal vaccination scheduling which reduce to a minimum the number of vaccine administrations still preventing the solid tumor formation is a population of virtual mice. The vaccination schedule proposed by the model is substantially lighter than the one's determined by the standard intuitive procedure.

KW - Artificial immunity

KW - agent based models

KW - cancer

KW - vaccine

UR - http://www.scopus.com/inward/record.url?scp=77957010572&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-14831-6_15

DO - 10.1007/978-3-642-14831-6_15

M3 - Conference contribution

AN - SCOPUS:77957010572

SN - 3642148301

SN - 9783642148309

T3 - Communications in Computer and Information Science

SP - 111

EP - 118

BT - Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings

Y2 - 18 August 2010 through 21 August 2010

ER -

Pappalardo F, Pennisi M, Cincotti A, Chiacchio F, Motta S, Lollini PL. Cancer immunoprevention: What can we learn from in silico models? In Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. 2010. p. 111-118. (Communications in Computer and Information Science). doi: 10.1007/978-3-642-14831-6_15

Cancer immunoprevention: What can we learn from in silico models?

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this