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Korsunsky I, McGovern K, LaGatta T, et al. Systems biology of cancer: a challenging expedition for clinical and quantitative biologists. Front Bioeng Biotechnol. 2014;2:27doi: 10.3389/fbioe.2014.00027.
Korsunsky, I., McGovern, K., LaGatta, T., Olde Loohuis, L., Grosso-Applewhite, T., Griffeth, N., & Mishra, B. (2014). Systems biology of cancer: a challenging expedition for clinical and quantitative biologists. Frontiers in bioengineering and biotechnology, 227. https://doi.org/10.3389/fbioe.2014.00027
Korsunsky, Ilya, et al. "Systems biology of cancer: a challenging expedition for clinical and quantitative biologists." Frontiers in bioengineering and biotechnology vol. 2 (2014): 27. doi: https://doi.org/10.3389/fbioe.2014.00027
Korsunsky I, McGovern K, LaGatta T, Olde Loohuis L, Grosso-Applewhite T, Griffeth N, Mishra B. Systems biology of cancer: a challenging expedition for clinical and quantitative biologists. Front Bioeng Biotechnol. 2014 Aug 19;2:27. doi: 10.3389/fbioe.2014.00027. eCollection 2014. PMID: 25191654; PMCID: PMC4137540.
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Front Bioeng Biotechnol. 2014 Aug 19;2:27. doi: 10.3389/fbioe.2014.00027. eCollection 2014.

Systems biology of cancer: a challenging expedition for clinical and quantitative biologists.

Frontiers in bioengineering and biotechnology

Ilya Korsunsky, Kathleen McGovern, Tom LaGatta, Loes Olde Loohuis, Terri Grosso-Applewhite, Nancy Griffeth, Bud Mishra

Affiliations

  1. Department of Computer Science, Courant Institute, New York University , New York, NY , USA.
  2. Department of Mathematics and Statistics, Hunter College, City University of New York , New York, NY , USA.
  3. Department of Mathematics, Courant Institute, New York University , New York, NY , USA.
  4. Department of Computer Science, The Graduate Center, City University of New York , New York, NY , USA.
  5. Department of Mathematics and Computer Science, Lehman College, City University of New York , New York, NY , USA.
  6. Department of Computer Science, Courant Institute, New York University , New York, NY , USA ; Department of Mathematics, Courant Institute, New York University , New York, NY , USA.

PMID: 25191654 PMCID: PMC4137540 DOI: 10.3389/fbioe.2014.00027

Abstract

A systems-biology approach to complex disease (such as cancer) is now complementing traditional experience-based approaches, which have typically been invasive and expensive. The rapid progress in biomedical knowledge is enabling the targeting of disease with therapies that are precise, proactive, preventive, and personalized. In this paper, we summarize and classify models of systems biology and model checking tools, which have been used to great success in computational biology and related fields. We demonstrate how these models and tools have been used to study some of the twelve biochemical pathways implicated in but not unique to pancreatic cancer, and conclude that the resulting mechanistic models will need to be further enhanced by various abstraction techniques to interpret phenomenological models of cancer progression.

Keywords: cancer pathway modeling; dynamical models; molecular modeling; multi-scale modeling; new algorithms

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