article

A Survey on Methods for Modeling and Analyzing Integrated Biological Networks

Authors:

Nuno Tenazinha,

Susana VingaAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 8, Issue 4

Pages 943 - 958

https://doi.org/10.1109/TCBB.2010.117

Published: 01 July 2011 Publication History

Abstract

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics” technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.

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Michelucci RCallegari VComet JPallez D(2024)Cellular Genetic Algorithms for Identifying Variables in Hybrid Gene Regulatory NetworksApplications of Evolutionary Computation10.1007/978-3-031-56852-7_9(131-145)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_9
Fernandes HGomes Gde Oliveira ACampos S(2020)Statistical Model Checking in Drug Repurposing for Alzheimer’s DiseaseFormal Methods: Foundations and Applications10.1007/978-3-030-63882-5_5(64-80)Online publication date: 25-Nov-2020
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Knox DDowell R(2016)A modeling framework for generation of positional and temporal simulations of transcriptional regulationIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2015.245970813:3(459-471)Online publication date: 1-May-2016
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IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 8, Issue 4

July 2011

287 pages

ISSN:1545-5963

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IEEE Computer Society Press

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Published: 01 July 2011

Published in TCBB Volume 8, Issue 4

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Cited By

Michelucci RCallegari VComet JPallez D(2024)Cellular Genetic Algorithms for Identifying Variables in Hybrid Gene Regulatory NetworksApplications of Evolutionary Computation10.1007/978-3-031-56852-7_9(131-145)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_9
Fernandes HGomes Gde Oliveira ACampos S(2020)Statistical Model Checking in Drug Repurposing for Alzheimer’s DiseaseFormal Methods: Foundations and Applications10.1007/978-3-030-63882-5_5(64-80)Online publication date: 25-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-63882-5_5
Knox DDowell R(2016)A modeling framework for generation of positional and temporal simulations of transcriptional regulationIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2015.245970813:3(459-471)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1109/TCBB.2015.2459708
Ren HHuang XHao J(2016)Finding robust adaptation gene regulatory networks using multi-objective genetic algorithmIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2015.243032113:3(571-577)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1109/TCBB.2015.2430321

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