Agent-Based Modeling in Systems Pharmacology

被引：44

作者：

Cosgrove, J. ^{[1
,2
,3
]}

Butler, J. ^{[1
,2
,3
]}

Alden, K. ^{[1
,2
]}

Read, M. ^{[4
]}

Kumar, V. ^{[5
]}

Cucurull-Sanchez, L. ^{[6
]}

Timmis, J. ^{[1
,3
,7
]}

Coles, M. ^{[1
,2
,7
]}

机构：

[1] Univ York, York Computat Immunol Lab, York, N Yorkshire, England

[2] Univ York, Ctr Immunol & Infect, York, N Yorkshire, England

[3] Univ York, Dept Elect, York, N Yorkshire, England

[4] Univ Sydney, Charles Perkins Ctr, Sydney, NSW, Australia

[5] Univ Calif, Sch Med, La Jolla, CA USA

[6] GSK Med Res Ctr, Stevenage, Herts, England

[7] SimOmics, York, N Yorkshire, England

来源：

CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY | 2015年 / 4卷 / 11期

基金：

英国医学研究理事会; 美国国家卫生研究院; 英国惠康基金; 英国工程与自然科学研究理事会; 英国国家替代、减少和改良动物研究中心;

关键词：

D O I：

10.1002/psp4.12018

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent-based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM-specific strengths have yielded success in the area of preclinical mechanistic modeling.

引用

页码：615 / 629

页数：15

共 70 条

[1] Using argument notation to engineer biological simulations with increased confidence [J].

Alden, Kieran ;

Andrews, Paul S. ;

Polack, Fiona A. C. ;

Veiga-Fernandes, Henrique ;

Coles, Mark C. ;

Timmis, Jon .

JOURNAL OF THE ROYAL SOCIETY INTERFACE, 2015, 12 (104)

[2] Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems [J].

Alden, Kieran ;

Read, Mark ;

Timmis, Jon ;

Andrews, Paul S. ;

Veiga-Fernandes, Henrique ;

Coles, Mark .

PLOS COMPUTATIONAL BIOLOGY, 2013, 9 (02)

[3] Pairing experimentation and computational modeling to understand the role of tissue inducer cells in the development of lymphoic organs [J].

Alden, Kieran ;

Timmis, Jon ;

Andrews, Paul S. ;

Veiga-Fernandes, Henrique ;

Coles, Mark C. .

FRONTIERS IN IMMUNOLOGY, 2012, 3

[4] Impact of Modeling and Simulation: Myth or Fact? [J].

Allerheiligen, S. R. B. .

CLINICAL PHARMACOLOGY & THERAPEUTICS, 2014, 96 (04) :413-415

[5] Next-Generation Model-Based Drug Discovery and Development: Quantitative and Systems Pharmacology [J].

Allerheiligen, S. R. B. .

CLINICAL PHARMACOLOGY & THERAPEUTICS, 2010, 88 (01) :135-137

[6] In silico experiments of existing and hypothetical cytokine-diyected clinical trials using agent-based modeling [J].

An, G .

CRITICAL CARE MEDICINE, 2004, 32 (10) :2050-2060

[7] Agent-based models in translational systems biology [J].

An, Gary ;

Mi, Qi ;

Dutta-Moscato, Joyeeta ;

Vodovotz, Yoram .

WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE, 2009, 1 (02) :159-171

[8] Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research [J].

An, Gary C. .

WOUND REPAIR AND REGENERATION, 2010, 18 (01) :8-12

[9]

Andrews P. S., 2010, YCS2010453 U YORK

[10] The effects of EGF-receptor density on multiscale tumor growth patterns [J].

Athale, CA ;

Deisboeck, TS .

JOURNAL OF THEORETICAL BIOLOGY, 2006, 238 (04) :771-779

← 1 2 3 4 5 6 7 →