Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Jorge G.T. Zañudo; Steven N. Steinway; Réka Albert

doi:10.1016/j.coisb.2018.02.002

Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Jorge G.T. Zañudo, Steven N. Steinway, Réka Albert

Research output: Contribution to journal › Review article › peer-review

41 Scopus citations

Abstract

Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.

Original language	English (US)
Pages (from-to)	1-10
Number of pages	10
Journal	Current Opinion in Systems Biology
Volume	9
DOIs	https://doi.org/10.1016/j.coisb.2018.02.002
State	Published - Jun 2018

All Science Journal Classification (ASJC) codes

Modeling and Simulation
Biochemistry, Genetics and Molecular Biology(all)
Drug Discovery
Computer Science Applications
Applied Mathematics

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10.1016/j.coisb.2018.02.002

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title = "Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer",

abstract = "Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.",

author = "{G.T. Za{\~n}udo}, Jorge and Steinway, {Steven N.} and R{\'e}ka Albert",

note = "Funding Information: Discrete dynamic modeling of cancer-related networks by the authors is supported by the National Science Foundation (grant PHY 1545832 ), the Stand Up to Cancer Foundation , and a Stand Up to Cancer Foundation/The V Foundation Convergence Scholar Award ( D2015-039 ) to J.G.T.Z. Publisher Copyright: {\textcopyright} 2018 The Authors",

year = "2018",

month = jun,

doi = "10.1016/j.coisb.2018.02.002",

language = "English (US)",

volume = "9",

pages = "1--10",

journal = "Current Opinion in Systems Biology",

issn = "2452-3100",

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TY - JOUR

T1 - Discrete dynamic network modeling of oncogenic signaling

T2 - Mechanistic insights for personalized treatment of cancer

AU - G.T. Zañudo, Jorge

AU - Steinway, Steven N.

AU - Albert, Réka

N1 - Funding Information: Discrete dynamic modeling of cancer-related networks by the authors is supported by the National Science Foundation (grant PHY 1545832 ), the Stand Up to Cancer Foundation , and a Stand Up to Cancer Foundation/The V Foundation Convergence Scholar Award ( D2015-039 ) to J.G.T.Z. Publisher Copyright: © 2018 The Authors

PY - 2018/6

Y1 - 2018/6

N2 - Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.

AB - Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.

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DO - 10.1016/j.coisb.2018.02.002

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EP - 10

JO - Current Opinion in Systems Biology

JF - Current Opinion in Systems Biology

ER -

Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Abstract

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