Modeling Exercises

Vida Abedi; Stefan Hoops; Raquel Hontecillas; Adria Carbo; Casandra Philipson; Monica Viladomiu; Andrew Leber; Pinyi Lu; Josep Bassaganya-Riera

doi:10.1016/B978-0-12-803697-6.00009-6

Modeling Exercises

Vida Abedi, Stefan Hoops, Raquel Hontecillas, Adria Carbo, Casandra Philipson, Monica Viladomiu, Andrew Leber, Pinyi Lu, Josep Bassaganya-Riera

Department of Public Health Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Computational modeling techniques and tools are playing increasingly important roles in advancing a system-level mechanistic understanding of biological processes. The in silico experimentation can help shape and guide experimental and clinical efforts. There are an array of tools that can be used to accelerate creation of mathematical models and therefore facilitate generation of in silico simulations. In this chapter, we will briefly review some of these tools and provide hands-on modeling examples. Finally, the chapter proposes an examination of the relationship between model complexity, reliability and knowledge discovery, and model-driven hypothesis generation.

Original language	English (US)
Title of host publication	Computational Immunology
Subtitle of host publication	Models and Tools
Publisher	Elsevier Inc.
Pages	175-200
Number of pages	26
ISBN (Electronic)	9780128037157
ISBN (Print)	9780128036976
DOIs	https://doi.org/10.1016/B978-0-12-803697-6.00009-6
State	Published - 2016

All Science Journal Classification (ASJC) codes

Medicine(all)

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10.1016/B978-0-12-803697-6.00009-6

Cite this

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abstract = "Computational modeling techniques and tools are playing increasingly important roles in advancing a system-level mechanistic understanding of biological processes. The in silico experimentation can help shape and guide experimental and clinical efforts. There are an array of tools that can be used to accelerate creation of mathematical models and therefore facilitate generation of in silico simulations. In this chapter, we will briefly review some of these tools and provide hands-on modeling examples. Finally, the chapter proposes an examination of the relationship between model complexity, reliability and knowledge discovery, and model-driven hypothesis generation.",

author = "Vida Abedi and Stefan Hoops and Raquel Hontecillas and Adria Carbo and Casandra Philipson and Monica Viladomiu and Andrew Leber and Pinyi Lu and Josep Bassaganya-Riera",

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AU - Abedi, Vida

AU - Hoops, Stefan

AU - Hontecillas, Raquel

AU - Carbo, Adria

AU - Philipson, Casandra

AU - Viladomiu, Monica

AU - Leber, Andrew

AU - Lu, Pinyi

AU - Bassaganya-Riera, Josep

N1 - Funding Information: This work was supported in part by National Institute of Allergy and Infectious Diseases Contract No. HHSN272201000056C to J. Bassaganya-Riera and funds from the Nutritional Immunology and Molecular Medicine Laboratory ( www.nimml.org ). Publisher Copyright: © 2016 Elsevier Inc. All rights reserved.

PY - 2016

Y1 - 2016

N2 - Computational modeling techniques and tools are playing increasingly important roles in advancing a system-level mechanistic understanding of biological processes. The in silico experimentation can help shape and guide experimental and clinical efforts. There are an array of tools that can be used to accelerate creation of mathematical models and therefore facilitate generation of in silico simulations. In this chapter, we will briefly review some of these tools and provide hands-on modeling examples. Finally, the chapter proposes an examination of the relationship between model complexity, reliability and knowledge discovery, and model-driven hypothesis generation.

AB - Computational modeling techniques and tools are playing increasingly important roles in advancing a system-level mechanistic understanding of biological processes. The in silico experimentation can help shape and guide experimental and clinical efforts. There are an array of tools that can be used to accelerate creation of mathematical models and therefore facilitate generation of in silico simulations. In this chapter, we will briefly review some of these tools and provide hands-on modeling examples. Finally, the chapter proposes an examination of the relationship between model complexity, reliability and knowledge discovery, and model-driven hypothesis generation.

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BT - Computational Immunology

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ER -

Modeling Exercises

Abstract

All Science Journal Classification (ASJC) codes

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